2025-07-30 06:44:06.034 | INFO     | yolox_microbt.core.trainer:before_train:88 - args: Namespace(config='configs.sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset', experiment_name='sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset', 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=[])
2025-07-30 06:44:06.038 | 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      │ False                                               │
├───────────────────┼─────────────────────────────────────────────────────┤
│ mixup_scale       │ (0.5, 1.5)                                          │
├───────────────────┼─────────────────────────────────────────────────────┤
│ shear             │ 2.0                                                 │
├───────────────────┼─────────────────────────────────────────────────────┤
│ warmup_epochs     │ 10                                                  │
├───────────────────┼─────────────────────────────────────────────────────┤
│ max_epoch         │ 300                                                 │
├───────────────────┼─────────────────────────────────────────────────────┤
│ warmup_lr         │ 0                                                   │
├───────────────────┼─────────────────────────────────────────────────────┤
│ min_lr_ratio      │ 0.05                                                │
├───────────────────┼─────────────────────────────────────────────────────┤
│ basic_lr_per_img  │ 3.125e-05                                           │
├───────────────────┼─────────────────────────────────────────────────────┤
│ scheduler         │ 'warmcos'                                           │
├───────────────────┼─────────────────────────────────────────────────────┤
│ no_aug_epochs     │ 200                                                 │
├───────────────────┼─────────────────────────────────────────────────────┤
│ ema               │ False                                               │
├───────────────────┼─────────────────────────────────────────────────────┤
│ weight_decay      │ 0.0005                                              │
├───────────────────┼─────────────────────────────────────────────────────┤
│ momentum          │ 0.9                                                 │
├───────────────────┼─────────────────────────────────────────────────────┤
│ save_history_ckpt │ True                                                │
├───────────────────┼─────────────────────────────────────────────────────┤
│ exp_name          │ 'sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset' │
├───────────────────┼─────────────────────────────────────────────────────┤
│ test_size         │ (416, 416)                                          │
├───────────────────┼─────────────────────────────────────────────────────┤
│ test_conf         │ 0.01                                                │
├───────────────────┼─────────────────────────────────────────────────────┤
│ nmsthre           │ 0.65                                                │
├───────────────────┼─────────────────────────────────────────────────────┤
│ qat_warmup_epoch  │ 0                                                   │
├───────────────────┼─────────────────────────────────────────────────────┤
│ qat_clib_epoch    │ 2                                                   │
╘═══════════════════╧═════════════════════════════════════════════════════╛
2025-07-30 06:44:06.944 | INFO     | yolox_microbt.core.trainer:before_train:129 - init prefetcher, this might take one minute or less...
2025-07-30 06:44:10.214 | INFO     | yolox_microbt.core.trainer:before_train:168 - Training start...
2025-07-30 06:44:10.438 | 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, 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)
                )
              )
            )
          )
          (2): 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, 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, 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)
                )
              )
            )
          )
          (4): 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, 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, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(24, 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(
                24, 96, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(96, 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(
                96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False
                (bn): BatchNorm2d(96, 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(
                96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(24, 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(
                24, 96, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(96, 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(
                96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False
                (bn): BatchNorm2d(96, 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(
                96, 40, kernel_size=(1, 1), stride=(1, 1), groups=2, 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)
                )
              )
            )
            (1): Module(
              (conv_pw): ConvBnReLU2d(
                40, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(160, 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(
                160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=160, bias=False
                (bn): BatchNorm2d(160, 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(
                160, 40, kernel_size=(1, 1), stride=(1, 1), groups=2, 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)
                )
              )
            )
          )
          (8): Module(
            (0): Module(
              (conv_pw): ConvBnReLU2d(
                40, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
                (bn): BatchNorm2d(160, 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(
                160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=160, bias=False
                (bn): BatchNorm2d(160, 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(
                160, 80, kernel_size=(1, 1), stride=(1, 1), groups=2, bias=False
                (bn): BatchNorm2d(80, 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(
              8, 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(
              24, 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(
              80, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv1): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv2): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_obj): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 1, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_cls): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 3, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_box): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 4, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
      )
      (x_post_act_fake_quantizer): FixedFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=0, quant_max=255, dtype=torch.quint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32)
        (activation_post_process): PseudoObserver(min_val=0.0, max_val=1.0, pot=False)
      )
      (backbone0_backbone_0_0_conv_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_1_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_1_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_1_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_2_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_3_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_4_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_5_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_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_6_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()
    )
  )
)
2025-07-30 06:44:10.440 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch1
2025-07-30 06:44:10.465 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-07-30 06:44:14.885 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/300, iter: 20/129, gpu mem: 1036Mb, mem: 75.9Gb, iter_time: 0.220s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 3.101e-05, size: 480, ETA: 2:21:48
2025-07-30 06:44:18.234 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/300, iter: 40/129, gpu mem: 1036Mb, mem: 75.9Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.0, conf_loss: 1.5, cls_loss: 0.6, lr: 6.202e-05, size: 352, ETA: 2:04:23
2025-07-30 06:44:21.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/300, iter: 60/129, gpu mem: 1036Mb, mem: 75.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 9.302e-05, size: 448, ETA: 1:57:41
2025-07-30 06:44:24.844 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/300, iter: 80/129, gpu mem: 1148Mb, mem: 76.0Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.240e-04, size: 512, ETA: 1:54:56
2025-07-30 06:44:27.775 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/300, iter: 100/129, gpu mem: 1148Mb, mem: 75.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.550e-04, size: 352, ETA: 1:50:34
2025-07-30 06:44:30.667 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 1/300, iter: 120/129, gpu mem: 1148Mb, mem: 76.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.860e-04, size: 256, ETA: 1:47:27
2025-07-30 06:44:32.075 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:44:39.096 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:44:40.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:44:40.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6030
2025-07-30 06:44:40.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5455
2025-07-30 06:44:40.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3781
2025-07-30 06:44:40.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5089
2025-07-30 06:44:40.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:44:40.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:44:40.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:44:40.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:44:40.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:44:40.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:44:41.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:44:42.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:44:43.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:44:44.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:44:44.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:44:45.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:44:46.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:44:47.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:44:48.040 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:44:48.040 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-07-30 06:44:48.040 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-07-30 06:44:48.040 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:44:48.049 | 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

2025-07-30 06:44:48.050 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:44:48.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:44:48.204 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch2
2025-07-30 06:44:48.210 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-07-30 06:44:51.413 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/300, iter: 20/129, gpu mem: 1720Mb, mem: 76.0Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.9, l1_loss: 0.0, conf_loss: 1.4, cls_loss: 0.5, lr: 2.310e-04, size: 544, ETA: 1:46:12
2025-07-30 06:44:54.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/300, iter: 40/129, gpu mem: 1720Mb, mem: 76.0Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 1.3, cls_loss: 0.7, lr: 2.620e-04, size: 384, ETA: 1:46:01
2025-07-30 06:44:57.544 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/300, iter: 60/129, gpu mem: 1720Mb, mem: 76.0Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.6, lr: 2.930e-04, size: 256, ETA: 1:44:18
2025-07-30 06:45:00.779 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.0Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.6, lr: 3.240e-04, size: 576, ETA: 1:44:07
2025-07-30 06:45:03.882 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.0Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.0, iou_loss: 2.0, l1_loss: 0.0, conf_loss: 1.3, cls_loss: 0.6, lr: 3.550e-04, size: 288, ETA: 1:43:35
2025-07-30 06:45:06.762 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 2/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.7, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.6, lr: 3.860e-04, size: 256, ETA: 1:42:34
2025-07-30 06:45:08.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:45:14.900 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:45:15.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:45:16.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5857
2025-07-30 06:45:16.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5238
2025-07-30 06:45:16.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3475
2025-07-30 06:45:16.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4857
2025-07-30 06:45:16.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:45:16.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:45:16.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-07-30 06:45:16.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-07-30 06:45:16.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:45:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:45:16.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:45:17.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:45:18.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:45:18.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:45:19.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:45:20.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:45:20.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:45:21.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:45:21.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:45:21.935 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-07-30 06:45:21.935 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-07-30 06:45:21.935 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:45:21.942 | 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

2025-07-30 06:45:21.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:45:22.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:45:22.091 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch3
2025-07-30 06:45:22.142 | INFO     | yolox_microbt.core.trainer:before_epoch:204 - --->enable mosaic aug for quantization training!
2025-07-30 06:45:25.963 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.6, cls_loss: 0.8, lr: 4.310e-04, size: 480, ETA: 1:43:19
2025-07-30 06:45:29.658 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 4.620e-04, size: 288, ETA: 1:44:07
2025-07-30 06:45:33.189 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 4.930e-04, size: 320, ETA: 1:44:31
2025-07-30 06:45:36.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.3, lr: 5.240e-04, size: 256, ETA: 1:45:06
2025-07-30 06:45:40.755 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.191s, 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: 5.550e-04, size: 256, ETA: 1:45:59
2025-07-30 06:45:44.559 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 3/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.186s, 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: 5.860e-04, size: 288, ETA: 1:46:37
2025-07-30 06:45:46.130 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:45:53.064 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:45:54.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:45:55.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1504
2025-07-30 06:45:55.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2079
2025-07-30 06:45:55.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1018
2025-07-30 06:45:55.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1534
2025-07-30 06:45:55.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:45:55.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:45:55.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.150
2025-07-30 06:45:55.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.208
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.102
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.153
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:45:55.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:45:55.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:45:56.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:45:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:45:58.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:45:59.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:46:00.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:46:01.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:46:03.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:46:04.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:46:05.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:46:05.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-07-30 06:46:05.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.15
2025-07-30 06:46:05.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:46:05.114 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.91 ms, Average inference time: 8.22 ms

2025-07-30 06:46:05.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:46:05.192 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:46:05.271 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch4
2025-07-30 06:46:08.739 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 10.0, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 5.2, cls_loss: 1.0, lr: 6.310e-04, size: 544, ETA: 1:46:42
2025-07-30 06:46:12.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 6.620e-04, size: 480, ETA: 1:47:03
2025-07-30 06:46:16.247 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 6.930e-04, size: 544, ETA: 1:47:30
2025-07-30 06:46:19.927 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 7.240e-04, size: 288, ETA: 1:47:45
2025-07-30 06:46:23.589 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 7.550e-04, size: 480, ETA: 1:47:58
2025-07-30 06:46:27.477 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 4/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 7.860e-04, size: 512, ETA: 1:48:22
2025-07-30 06:46:29.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:46:36.206 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:46:38.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:46:40.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3827
2025-07-30 06:46:40.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2576
2025-07-30 06:46:40.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1107
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2504
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.111
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.250
2025-07-30 06:46:40.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:46:40.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:46:42.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:46:44.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:46:46.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:46:48.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:46:50.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:46:52.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:46:54.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:46:56.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:46:58.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:46:58.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 06:46:58.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-07-30 06:46:58.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:46:58.530 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.91 ms, Average inference time: 8.33 ms

2025-07-30 06:46:58.531 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:46:58.604 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:46:58.682 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch5
2025-07-30 06:47:02.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.180s, 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: 8.310e-04, size: 416, ETA: 1:48:40
2025-07-30 06:47:06.044 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 8.620e-04, size: 320, ETA: 1:48:49
2025-07-30 06:47:09.666 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 8.930e-04, size: 288, ETA: 1:48:52
2025-07-30 06:47:13.361 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 9.240e-04, size: 480, ETA: 1:49:00
2025-07-30 06:47:17.129 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 9.2, iou_loss: 4.1, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 0.7, lr: 9.550e-04, size: 480, ETA: 1:49:11
2025-07-30 06:47:21.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 5/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 9.860e-04, size: 512, ETA: 1:49:31
2025-07-30 06:47:22.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:47:29.768 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:47:33.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:47:35.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4125
2025-07-30 06:47:36.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2986
2025-07-30 06:47:36.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1503
2025-07-30 06:47:36.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2871
2025-07-30 06:47:36.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:47:36.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:47:36.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.150
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:47:36.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:47:36.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:47:36.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:47:36.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:47:39.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:47:42.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:47:44.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:47:47.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:47:50.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:47:53.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:47:56.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:47:59.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:48:02.198 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:48:02.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 06:48:02.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 06:48:02.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:48:02.229 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.95 ms, Average inference time: 8.44 ms

2025-07-30 06:48:02.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:48:02.302 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:48:02.436 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch6
2025-07-30 06:48:05.926 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.031e-03, size: 448, ETA: 1:49:30
2025-07-30 06:48:09.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.062e-03, size: 384, ETA: 1:49:39
2025-07-30 06:48:13.234 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.1Gb, 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: 0.8, lr: 1.093e-03, size: 384, ETA: 1:49:38
2025-07-30 06:48:16.890 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.178s, 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: 1.124e-03, size: 352, ETA: 1:49:39
2025-07-30 06:48:20.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.8, cls_loss: 1.7, lr: 1.155e-03, size: 288, ETA: 1:49:42
2025-07-30 06:48:24.151 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 6/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.173s, 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: 1.186e-03, size: 544, ETA: 1:49:39
2025-07-30 06:48:25.839 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:48:32.889 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:48:34.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:48:35.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3664
2025-07-30 06:48:35.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2409
2025-07-30 06:48:35.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1961
2025-07-30 06:48:35.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2678
2025-07-30 06:48:35.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:48:35.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:48:35.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-07-30 06:48:35.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.241
2025-07-30 06:48:35.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-07-30 06:48:35.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.268
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:48:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:48:35.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:48:36.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:48:37.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:48:38.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:48:39.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:48:40.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:48:41.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:48:42.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:48:43.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:48:44.596 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:48:44.596 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 06:48:44.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 06:48:44.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:48:44.604 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.93 ms, Average inference time: 8.43 ms

2025-07-30 06:48:44.608 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:48:44.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:48:44.764 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch7
2025-07-30 06:48:48.413 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.231e-03, size: 544, ETA: 1:49:45
2025-07-30 06:48:52.286 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.189s, 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: 1.262e-03, size: 480, ETA: 1:49:56
2025-07-30 06:48:55.960 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.178s, 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: 1.293e-03, size: 416, ETA: 1:49:56
2025-07-30 06:48:59.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.196s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.324e-03, size: 288, ETA: 1:50:12
2025-07-30 06:49:03.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.355e-03, size: 448, ETA: 1:50:20
2025-07-30 06:49:07.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 7/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.190s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.386e-03, size: 352, ETA: 1:50:29
2025-07-30 06:49:09.449 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:49:16.171 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:49:18.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:49:19.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1427
2025-07-30 06:49:19.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1965
2025-07-30 06:49:20.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0244
2025-07-30 06:49:20.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1212
2025-07-30 06:49:20.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:49:20.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:49:20.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.143
2025-07-30 06:49:20.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.197
2025-07-30 06:49:20.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.024
2025-07-30 06:49:20.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.121
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:49:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:49:21.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:49:23.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:49:25.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:49:26.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:49:28.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:49:30.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:49:32.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:49:33.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:49:35.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:49:35.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 06:49:35.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.12
2025-07-30 06:49:35.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:49:35.692 | 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

2025-07-30 06:49:35.694 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:49:35.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:49:35.879 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch8
2025-07-30 06:49:39.390 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.431e-03, size: 256, ETA: 1:50:23
2025-07-30 06:49:43.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.462e-03, size: 480, ETA: 1:50:25
2025-07-30 06:49:46.944 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.185s, 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: 1.493e-03, size: 256, ETA: 1:50:29
2025-07-30 06:49:50.772 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.187s, 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: 1.524e-03, size: 448, ETA: 1:50:34
2025-07-30 06:49:54.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 0.6, lr: 1.555e-03, size: 576, ETA: 1:50:33
2025-07-30 06:49:58.393 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 8/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.192s, data_time: 0.003s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.586e-03, size: 256, ETA: 1:50:42
2025-07-30 06:49:59.979 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:50:06.861 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:50:08.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:50:09.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1938
2025-07-30 06:50:09.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1791
2025-07-30 06:50:09.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0426
2025-07-30 06:50:09.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1385
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.194
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.043
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.139
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:50:09.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:50:09.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:50:09.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:50:09.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:50:09.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:50:09.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:50:09.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:50:11.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:50:12.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:50:13.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:50:14.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:50:16.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:50:17.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:50:18.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:50:20.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:50:21.440 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:50:21.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.06
2025-07-30 06:50:21.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.14
2025-07-30 06:50:21.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:50:21.449 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.92 ms, Average inference time: 8.41 ms

2025-07-30 06:50:21.450 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:50:21.526 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:50:21.607 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch9
2025-07-30 06:50:25.166 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.631e-03, size: 352, ETA: 1:50:32
2025-07-30 06:50:28.942 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.662e-03, size: 288, ETA: 1:50:34
2025-07-30 06:50:32.853 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.693e-03, size: 448, ETA: 1:50:39
2025-07-30 06:50:36.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 0.8, lr: 1.724e-03, size: 352, ETA: 1:50:37
2025-07-30 06:50:40.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.2, lr: 1.755e-03, size: 576, ETA: 1:50:34
2025-07-30 06:50:44.117 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 9/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.1Gb, iter_time: 0.192s, data_time: 0.005s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.786e-03, size: 544, ETA: 1:50:40
2025-07-30 06:50:45.809 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:50:52.661 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:50:53.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:50:54.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4353
2025-07-30 06:50:54.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3492
2025-07-30 06:50:54.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2329
2025-07-30 06:50:54.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3391
2025-07-30 06:50:54.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:50:54.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:50:54.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-07-30 06:50:54.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-07-30 06:50:54.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.233
2025-07-30 06:50:54.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:50:54.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:50:55.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:50:56.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:50:57.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:50:57.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:50:58.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:50:59.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:51:00.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:51:01.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:51:01.851 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:51:01.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 06:51:01.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 06:51:01.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:51:01.859 | 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

2025-07-30 06:51:01.860 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:51:01.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:51:02.071 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch10
2025-07-30 06:51:05.563 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.831e-03, size: 256, ETA: 1:50:34
2025-07-30 06:51:09.333 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.862e-03, size: 320, ETA: 1:50:35
2025-07-30 06:51:12.873 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.893e-03, size: 256, ETA: 1:50:29
2025-07-30 06:51:16.630 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.183s, 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: 1.924e-03, size: 544, ETA: 1:50:29
2025-07-30 06:51:20.430 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.955e-03, size: 576, ETA: 1:50:29
2025-07-30 06:51:24.419 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 10/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.197s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.986e-03, size: 576, ETA: 1:50:37
2025-07-30 06:51:26.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:51:33.012 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:51:34.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:51:35.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4369
2025-07-30 06:51:36.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3621
2025-07-30 06:51:36.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1909
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3299
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:51:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:51:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:51:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:51:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:51:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:51:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:51:36.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:51:37.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:51:39.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:51:40.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:51:42.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:51:43.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:51:45.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:51:46.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:51:48.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:51:49.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:51:49.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 06:51:49.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 06:51:49.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:51:49.497 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.91 ms, Average inference time: 8.34 ms

2025-07-30 06:51:49.506 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:51:49.576 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:51:49.655 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch11
2025-07-30 06:51:53.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.176s, 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: 2.000e-03, size: 320, ETA: 1:50:37
2025-07-30 06:51:56.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.9, lr: 2.000e-03, size: 320, ETA: 1:50:32
2025-07-30 06:52:00.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.182s, 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: 2.000e-03, size: 512, ETA: 1:50:31
2025-07-30 06:52:04.346 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 2.000e-03, size: 544, ETA: 1:50:32
2025-07-30 06:52:08.145 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.186s, 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: 2.000e-03, size: 352, ETA: 1:50:33
2025-07-30 06:52:11.977 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 11/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 2.000e-03, size: 416, ETA: 1:50:35
2025-07-30 06:52:13.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:52:20.659 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:52:22.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:52:23.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2732
2025-07-30 06:52:23.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2240
2025-07-30 06:52:24.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1040
2025-07-30 06:52:24.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2004
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.273
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.104
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.200
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:52:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:52:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:52:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:52:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:52:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:52:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:52:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:52:24.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:52:25.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:52:26.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:52:27.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:52:28.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:52:29.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:52:31.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:52:32.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:52:33.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:52:34.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:52:34.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-07-30 06:52:34.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-07-30 06:52:34.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:52:34.540 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.90 ms, Average inference time: 8.47 ms

2025-07-30 06:52:34.541 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:52:34.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:52:34.690 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch12
2025-07-30 06:52:38.310 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 0.8, lr: 2.000e-03, size: 544, ETA: 1:50:30
2025-07-30 06:52:42.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 2.000e-03, size: 480, ETA: 1:50:28
2025-07-30 06:52:45.741 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.3, lr: 2.000e-03, size: 416, ETA: 1:50:26
2025-07-30 06:52:49.328 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 2.000e-03, size: 384, ETA: 1:50:21
2025-07-30 06:52:53.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 2.000e-03, size: 544, ETA: 1:50:22
2025-07-30 06:52:56.956 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 12/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 2.000e-03, size: 256, ETA: 1:50:21
2025-07-30 06:52:58.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:53:05.468 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:53:08.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:53:10.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2748
2025-07-30 06:53:10.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1592
2025-07-30 06:53:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0714
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1685
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.159
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.071
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.168
2025-07-30 06:53:10.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:53:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:53:13.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:53:15.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:53:18.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:53:20.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:53:22.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:53:25.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:53:27.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:53:29.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:53:32.301 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:53:32.301 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 06:53:32.301 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.17
2025-07-30 06:53:32.301 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:53:32.327 | 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

2025-07-30 06:53:32.331 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:53:32.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:53:32.483 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch13
2025-07-30 06:53:36.080 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.6, lr: 2.000e-03, size: 448, ETA: 1:50:14
2025-07-30 06:53:40.005 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 2.000e-03, size: 448, ETA: 1:50:17
2025-07-30 06:53:43.775 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 2.000e-03, size: 544, ETA: 1:50:16
2025-07-30 06:53:47.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 2.000e-03, size: 512, ETA: 1:50:17
2025-07-30 06:53:51.494 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.6, lr: 2.000e-03, size: 480, ETA: 1:50:18
2025-07-30 06:53:55.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 13/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.176s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.999e-03, size: 320, ETA: 1:50:14
2025-07-30 06:53:56.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:54:03.626 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:54:06.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:54:07.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3164
2025-07-30 06:54:07.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2392
2025-07-30 06:54:07.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0904
2025-07-30 06:54:07.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2153
2025-07-30 06:54:07.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.090
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.215
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:54:07.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:54:07.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:54:07.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:54:07.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:54:07.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:54:07.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:54:07.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:54:09.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:54:11.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:54:13.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:54:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:54:17.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:54:19.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:54:20.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:54:22.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:54:24.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:54:24.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-07-30 06:54:24.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-07-30 06:54:24.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:54:24.521 | 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

2025-07-30 06:54:24.522 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:54:24.598 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:54:24.727 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch14
2025-07-30 06:54:28.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.999e-03, size: 448, ETA: 1:50:03
2025-07-30 06:54:31.897 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.181s, 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: 1.999e-03, size: 352, ETA: 1:50:01
2025-07-30 06:54:35.596 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.999e-03, size: 576, ETA: 1:49:58
2025-07-30 06:54:39.409 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.999e-03, size: 448, ETA: 1:49:57
2025-07-30 06:54:43.190 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.999e-03, size: 576, ETA: 1:49:56
2025-07-30 06:54:46.966 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 14/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.999e-03, size: 480, ETA: 1:49:55
2025-07-30 06:54:48.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:54:55.456 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:55:01.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:55:05.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2519
2025-07-30 06:55:06.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2320
2025-07-30 06:55:06.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1029
2025-07-30 06:55:06.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1956
2025-07-30 06:55:06.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.252
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.103
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.196
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:55:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:55:06.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:55:06.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:55:06.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:55:06.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:55:10.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:55:15.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:55:20.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:55:25.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:55:29.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:55:34.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:55:39.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:55:43.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:55:48.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:55:48.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 06:55:48.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-07-30 06:55:48.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:55:48.620 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.30 ms, Average NMS time: 0.90 ms, Average inference time: 8.21 ms

2025-07-30 06:55:48.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:55:48.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:55:48.782 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch15
2025-07-30 06:55:52.345 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.3, lr: 1.999e-03, size: 480, ETA: 1:49:47
2025-07-30 06:55:56.108 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.999e-03, size: 352, ETA: 1:49:45
2025-07-30 06:55:59.803 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/300, iter: 60/129, gpu mem: 1856Mb, 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: 3.0, cls_loss: 1.2, lr: 1.999e-03, size: 256, ETA: 1:49:43
2025-07-30 06:56:03.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.170s, 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: 1.999e-03, size: 288, ETA: 1:49:36
2025-07-30 06:56:06.949 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.999e-03, size: 352, ETA: 1:49:33
2025-07-30 06:56:10.634 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 15/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.8, lr: 1.999e-03, size: 512, ETA: 1:49:30
2025-07-30 06:56:12.275 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:56:19.307 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:56:21.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:56:23.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4347
2025-07-30 06:56:23.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3756
2025-07-30 06:56:23.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2219
2025-07-30 06:56:23.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3441
2025-07-30 06:56:23.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:56:23.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:56:23.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-07-30 06:56:23.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-07-30 06:56:23.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.222
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:56:23.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:56:23.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:56:25.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:56:27.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:56:29.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:56:30.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:56:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:56:34.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:56:36.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:56:38.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:56:39.852 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:56:39.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 06:56:39.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 06:56:39.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:56:39.878 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.93 ms, Average inference time: 8.31 ms

2025-07-30 06:56:39.880 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:56:39.955 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:56:40.036 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch16
2025-07-30 06:56:43.679 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.998e-03, size: 480, ETA: 1:49:24
2025-07-30 06:56:47.684 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.195s, 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: 1.998e-03, size: 480, ETA: 1:49:27
2025-07-30 06:56:51.466 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.185s, 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: 1.998e-03, size: 544, ETA: 1:49:26
2025-07-30 06:56:55.267 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.998e-03, size: 352, ETA: 1:49:24
2025-07-30 06:56:58.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.172s, 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: 1.998e-03, size: 352, ETA: 1:49:18
2025-07-30 06:57:02.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 16/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.998e-03, size: 352, ETA: 1:49:18
2025-07-30 06:57:04.441 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:57:11.420 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:57:11.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:57:12.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2478
2025-07-30 06:57:12.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2253
2025-07-30 06:57:12.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0692
2025-07-30 06:57:12.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1808
2025-07-30 06:57:12.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:57:12.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:57:12.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.248
2025-07-30 06:57:12.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.225
2025-07-30 06:57:12.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.069
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.181
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:57:12.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:57:12.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:57:12.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:57:13.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:57:13.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:57:13.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:57:14.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:57:14.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:57:14.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:57:15.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:57:15.596 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:57:15.596 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 06:57:15.596 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.18
2025-07-30 06:57:15.596 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:57:15.602 | 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

2025-07-30 06:57:15.605 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:57:15.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:57:15.756 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch17
2025-07-30 06:57:19.535 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.998e-03, size: 480, ETA: 1:49:16
2025-07-30 06:57:23.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.998e-03, size: 384, ETA: 1:49:15
2025-07-30 06:57:27.097 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 0.8, lr: 1.998e-03, size: 416, ETA: 1:49:13
2025-07-30 06:57:30.652 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.3, lr: 1.997e-03, size: 512, ETA: 1:49:07
2025-07-30 06:57:34.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.997e-03, size: 416, ETA: 1:49:07
2025-07-30 06:57:38.438 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 17/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.997e-03, size: 576, ETA: 1:49:08
2025-07-30 06:57:40.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:57:47.215 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:57:50.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:57:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3759
2025-07-30 06:57:52.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3059
2025-07-30 06:57:52.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1663
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2827
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.166
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.283
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:57:52.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:57:52.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:57:52.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:57:52.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:57:52.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:57:52.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:57:52.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:57:52.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:57:55.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:57:57.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:58:00.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:58:02.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:58:05.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:58:08.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:58:10.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:58:13.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:58:15.617 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:58:15.617 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 06:58:15.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 06:58:15.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:58:15.644 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.91 ms, Average inference time: 8.26 ms

2025-07-30 06:58:15.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:58:15.734 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:58:15.830 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch18
2025-07-30 06:58:19.380 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.997e-03, size: 480, ETA: 1:49:02
2025-07-30 06:58:23.124 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.183s, 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: 1.997e-03, size: 512, ETA: 1:48:59
2025-07-30 06:58:26.861 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.997e-03, size: 512, ETA: 1:48:57
2025-07-30 06:58:30.631 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.997e-03, size: 448, ETA: 1:48:54
2025-07-30 06:58:34.429 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, 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: 1.996e-03, size: 256, ETA: 1:48:52
2025-07-30 06:58:38.282 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 18/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.996e-03, size: 320, ETA: 1:48:51
2025-07-30 06:58:39.872 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:58:46.659 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:58:47.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:58:48.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3928
2025-07-30 06:58:48.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2891
2025-07-30 06:58:48.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1375
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2731
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.137
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.273
2025-07-30 06:58:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:58:48.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:58:48.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:58:49.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:58:50.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:58:50.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:58:51.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:58:52.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:58:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:58:53.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:58:54.240 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:58:54.240 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 06:58:54.240 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 06:58:54.240 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:58:54.249 | 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

2025-07-30 06:58:54.250 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:58:54.373 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:58:54.455 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch19
2025-07-30 06:58:58.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.6, lr: 1.996e-03, size: 544, ETA: 1:48:43
2025-07-30 06:59:01.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.996e-03, size: 288, ETA: 1:48:39
2025-07-30 06:59:05.319 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, 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: 1.996e-03, size: 480, ETA: 1:48:35
2025-07-30 06:59:09.089 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, 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: 1.996e-03, size: 320, ETA: 1:48:33
2025-07-30 06:59:12.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.995e-03, size: 384, ETA: 1:48:27
2025-07-30 06:59:16.391 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 19/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.995e-03, size: 256, ETA: 1:48:24
2025-07-30 06:59:17.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:59:25.049 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 06:59:27.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 06:59:29.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3923
2025-07-30 06:59:30.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3646
2025-07-30 06:59:30.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1790
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3120
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.312
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 06:59:30.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 06:59:30.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 06:59:30.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 06:59:30.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 06:59:30.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 06:59:30.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 06:59:30.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 06:59:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 06:59:34.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 06:59:37.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 06:59:39.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 06:59:41.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 06:59:44.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 06:59:46.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 06:59:48.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 06:59:50.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 06:59:50.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 06:59:50.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 06:59:50.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 06:59:50.988 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.32 ms, Average NMS time: 0.89 ms, Average inference time: 8.21 ms

2025-07-30 06:59:50.989 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:59:51.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 06:59:51.144 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch20
2025-07-30 06:59:54.669 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.169s, 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: 1.995e-03, size: 512, ETA: 1:48:15
2025-07-30 06:59:58.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.995e-03, size: 256, ETA: 1:48:14
2025-07-30 07:00:02.437 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.995e-03, size: 320, ETA: 1:48:13
2025-07-30 07:00:06.259 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.2Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.995e-03, size: 288, ETA: 1:48:13
2025-07-30 07:00:09.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.994e-03, size: 320, ETA: 1:48:06
2025-07-30 07:00:13.307 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 20/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.175s, 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: 1.994e-03, size: 320, ETA: 1:48:02
2025-07-30 07:00:14.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:00:21.729 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:00:24.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:00:25.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4281
2025-07-30 07:00:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3232
2025-07-30 07:00:25.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2377
2025-07-30 07:00:25.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3297
2025-07-30 07:00:25.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:00:25.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:00:25.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:00:25.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:00:27.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:00:29.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:00:31.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:00:32.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:00:34.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:00:36.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:00:38.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:00:39.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:00:41.517 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:00:41.517 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:00:41.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 07:00:41.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:00:41.544 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.91 ms, Average inference time: 8.28 ms

2025-07-30 07:00:41.546 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:00:41.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:00:41.706 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch21
2025-07-30 07:00:45.253 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.994e-03, size: 416, ETA: 1:47:54
2025-07-30 07:00:48.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.994e-03, size: 256, ETA: 1:47:51
2025-07-30 07:00:52.794 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, 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: 0.7, lr: 1.994e-03, size: 352, ETA: 1:47:50
2025-07-30 07:00:56.460 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.993e-03, size: 416, ETA: 1:47:46
2025-07-30 07:01:00.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.182s, 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: 1.993e-03, size: 320, ETA: 1:47:43
2025-07-30 07:01:03.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 21/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.993e-03, size: 256, ETA: 1:47:39
2025-07-30 07:01:05.637 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:01:12.436 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:01:13.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:01:13.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1890
2025-07-30 07:01:14.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1143
2025-07-30 07:01:14.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1065
2025-07-30 07:01:14.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1366
2025-07-30 07:01:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:01:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:01:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.189
2025-07-30 07:01:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.114
2025-07-30 07:01:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.106
2025-07-30 07:01:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.137
2025-07-30 07:01:14.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:01:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:01:14.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:01:15.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:01:15.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:01:16.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:01:17.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:01:17.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:01:18.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:01:18.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:01:19.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:01:19.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.05
2025-07-30 07:01:19.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.14
2025-07-30 07:01:19.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:01:19.426 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.90 ms, Average inference time: 8.39 ms

2025-07-30 07:01:19.427 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:01:19.495 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:01:19.575 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch22
2025-07-30 07:01:23.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 1.7, cls_loss: 0.7, lr: 1.993e-03, size: 448, ETA: 1:47:37
2025-07-30 07:01:27.190 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 1.1, lr: 1.993e-03, size: 512, ETA: 1:47:36
2025-07-30 07:01:30.889 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.992e-03, size: 448, ETA: 1:47:33
2025-07-30 07:01:34.610 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.992e-03, size: 256, ETA: 1:47:30
2025-07-30 07:01:38.237 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.992e-03, size: 352, ETA: 1:47:26
2025-07-30 07:01:42.034 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 22/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.992e-03, size: 256, ETA: 1:47:24
2025-07-30 07:01:43.726 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:01:50.927 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:01:52.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:01:52.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2068
2025-07-30 07:01:52.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2087
2025-07-30 07:01:52.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1162
2025-07-30 07:01:52.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1772
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.116
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.177
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:01:52.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:01:52.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:01:52.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:01:52.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:01:52.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:01:53.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:01:54.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:01:55.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:01:56.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:01:57.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:01:57.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:01:58.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:01:59.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:02:00.434 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:02:00.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.06
2025-07-30 07:02:00.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.18
2025-07-30 07:02:00.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:02:00.443 | 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

2025-07-30 07:02:00.444 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:02:00.519 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:02:00.599 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch23
2025-07-30 07:02:04.047 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.170s, 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: 1.991e-03, size: 384, ETA: 1:47:17
2025-07-30 07:02:07.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.991e-03, size: 544, ETA: 1:47:15
2025-07-30 07:02:11.697 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.991e-03, size: 576, ETA: 1:47:12
2025-07-30 07:02:15.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.991e-03, size: 416, ETA: 1:47:11
2025-07-30 07:02:19.218 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.5, lr: 1.990e-03, size: 256, ETA: 1:47:07
2025-07-30 07:02:23.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 23/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.990e-03, size: 544, ETA: 1:47:06
2025-07-30 07:02:24.753 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:02:31.636 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:02:33.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:02:34.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3502
2025-07-30 07:02:35.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3268
2025-07-30 07:02:35.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1747
2025-07-30 07:02:35.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2839
2025-07-30 07:02:35.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:02:35.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.175
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.284
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:02:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:02:35.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:02:35.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:02:35.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:02:35.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:02:35.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:02:36.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:02:38.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:02:40.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:02:41.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:02:43.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:02:44.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:02:46.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:02:47.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:02:49.499 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:02:49.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:02:49.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:02:49.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:02:49.524 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.93 ms, Average inference time: 8.31 ms

2025-07-30 07:02:49.526 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:02:49.600 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:02:49.680 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch24
2025-07-30 07:02:53.141 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.6, lr: 1.990e-03, size: 288, ETA: 1:46:58
2025-07-30 07:02:56.857 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.990e-03, size: 576, ETA: 1:46:55
2025-07-30 07:03:00.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.188s, 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: 1.989e-03, size: 448, ETA: 1:46:53
2025-07-30 07:03:04.561 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.187s, 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: 1.989e-03, size: 352, ETA: 1:46:52
2025-07-30 07:03:08.172 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.5, lr: 1.989e-03, size: 352, ETA: 1:46:47
2025-07-30 07:03:12.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 24/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.190s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.989e-03, size: 480, ETA: 1:46:46
2025-07-30 07:03:13.768 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:03:20.734 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:03:23.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:03:26.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3699
2025-07-30 07:03:26.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3131
2025-07-30 07:03:26.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1742
2025-07-30 07:03:26.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2857
2025-07-30 07:03:26.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:03:26.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.174
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.286
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:03:26.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:03:26.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:03:26.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:03:26.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:03:29.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:03:31.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:03:34.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:03:37.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:03:39.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:03:42.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:03:45.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:03:47.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:03:50.333 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:03:50.333 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:03:50.333 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 07:03:50.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:03:50.359 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.92 ms, Average inference time: 8.49 ms

2025-07-30 07:03:50.361 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:03:50.437 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:03:50.515 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch25
2025-07-30 07:03:53.985 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.170s, 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: 1.988e-03, size: 352, ETA: 1:46:39
2025-07-30 07:03:57.646 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.988e-03, size: 352, ETA: 1:46:35
2025-07-30 07:04:01.410 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 0.7, lr: 1.988e-03, size: 544, ETA: 1:46:32
2025-07-30 07:04:05.247 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 9.6, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 4.8, cls_loss: 1.1, lr: 1.987e-03, size: 512, ETA: 1:46:31
2025-07-30 07:04:09.019 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.987e-03, size: 256, ETA: 1:46:28
2025-07-30 07:04:12.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 25/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 1.987e-03, size: 576, ETA: 1:46:25
2025-07-30 07:04:14.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:04:21.501 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:04:23.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:04:24.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3775
2025-07-30 07:04:24.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2704
2025-07-30 07:04:24.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1836
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2771
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.277
2025-07-30 07:04:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:04:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:04:25.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:04:27.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:04:28.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:04:29.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:04:30.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:04:32.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:04:33.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:04:34.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:04:36.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:04:36.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-07-30 07:04:36.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:04:36.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:04:36.018 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.59 ms, Average NMS time: 0.92 ms, Average inference time: 8.52 ms

2025-07-30 07:04:36.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:04:36.095 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:04:36.177 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch26
2025-07-30 07:04:39.695 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.987e-03, size: 352, ETA: 1:46:19
2025-07-30 07:04:43.540 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.986e-03, size: 352, ETA: 1:46:16
2025-07-30 07:04:47.220 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.986e-03, size: 256, ETA: 1:46:12
2025-07-30 07:04:51.005 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.185s, 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: 1.986e-03, size: 512, ETA: 1:46:09
2025-07-30 07:04:54.724 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.985e-03, size: 320, ETA: 1:46:06
2025-07-30 07:04:58.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 26/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.985e-03, size: 512, ETA: 1:46:03
2025-07-30 07:05:00.195 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:05:07.231 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:05:09.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:05:11.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4163
2025-07-30 07:05:11.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3215
2025-07-30 07:05:11.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1842
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3073
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.307
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:05:11.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:05:11.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:05:11.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:05:11.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:05:11.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:05:11.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:05:11.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:05:13.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:05:15.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:05:17.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:05:18.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:05:20.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:05:22.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:05:24.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:05:26.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:05:27.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:05:27.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:05:27.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 07:05:27.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:05:27.984 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.91 ms, Average inference time: 8.42 ms

2025-07-30 07:05:27.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:05:28.095 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:05:28.185 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch27
2025-07-30 07:05:31.681 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.985e-03, size: 544, ETA: 1:45:56
2025-07-30 07:05:35.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.182s, 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: 1.984e-03, size: 352, ETA: 1:45:53
2025-07-30 07:05:39.084 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.984e-03, size: 288, ETA: 1:45:49
2025-07-30 07:05:42.692 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.984e-03, size: 288, ETA: 1:45:44
2025-07-30 07:05:46.282 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.984e-03, size: 448, ETA: 1:45:40
2025-07-30 07:05:50.014 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 27/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.983e-03, size: 256, ETA: 1:45:37
2025-07-30 07:05:51.627 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:05:58.619 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:05:59.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:06:00.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2803
2025-07-30 07:06:00.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1349
2025-07-30 07:06:00.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1423
2025-07-30 07:06:00.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1858
2025-07-30 07:06:00.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.135
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.142
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.186
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:06:00.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:06:00.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:06:00.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:06:00.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:06:00.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:06:00.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:06:01.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:06:02.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:06:02.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:06:03.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:06:04.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:06:04.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:06:05.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:06:05.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:06:05.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.06
2025-07-30 07:06:05.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.19
2025-07-30 07:06:05.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:06:05.898 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.90 ms, Average inference time: 8.24 ms

2025-07-30 07:06:05.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:06:05.973 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:06:06.054 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch28
2025-07-30 07:06:09.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.6, lr: 1.983e-03, size: 512, ETA: 1:45:30
2025-07-30 07:06:13.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.982e-03, size: 448, ETA: 1:45:28
2025-07-30 07:06:17.471 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, 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: 1.982e-03, size: 576, ETA: 1:45:27
2025-07-30 07:06:21.388 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.9, cls_loss: 0.9, lr: 1.982e-03, size: 448, ETA: 1:45:25
2025-07-30 07:06:25.197 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.982e-03, size: 448, ETA: 1:45:23
2025-07-30 07:06:28.853 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 28/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.981e-03, size: 256, ETA: 1:45:19
2025-07-30 07:06:30.427 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:06:37.455 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:06:39.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:06:41.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2860
2025-07-30 07:06:41.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2653
2025-07-30 07:06:41.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0988
2025-07-30 07:06:41.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2167
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.265
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.099
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.217
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:06:41.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:06:41.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:06:41.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:06:41.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:06:41.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:06:41.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:06:41.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:06:43.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:06:45.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:06:47.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:06:49.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:06:51.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:06:53.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:06:55.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:06:57.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:06:59.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:06:59.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 07:06:59.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-07-30 07:06:59.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:06:59.118 | 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

2025-07-30 07:06:59.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:06:59.201 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:06:59.282 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch29
2025-07-30 07:07:02.808 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.170s, 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: 1.981e-03, size: 416, ETA: 1:45:11
2025-07-30 07:07:06.503 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.177s, 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: 1.980e-03, size: 448, ETA: 1:45:06
2025-07-30 07:07:10.270 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.980e-03, size: 448, ETA: 1:45:04
2025-07-30 07:07:14.116 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.186s, 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: 1.980e-03, size: 448, ETA: 1:45:01
2025-07-30 07:07:17.880 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.979e-03, size: 288, ETA: 1:44:58
2025-07-30 07:07:21.342 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 29/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.979e-03, size: 352, ETA: 1:44:53
2025-07-30 07:07:22.910 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:07:29.983 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:07:32.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:07:34.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3133
2025-07-30 07:07:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3646
2025-07-30 07:07:35.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1570
2025-07-30 07:07:35.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2783
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.157
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:07:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:07:35.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:07:35.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:07:35.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:07:35.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:07:35.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:07:37.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:07:39.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:07:42.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:07:44.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:07:46.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:07:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:07:51.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:07:53.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:07:56.117 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:07:56.117 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:07:56.117 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:07:56.117 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:07:56.142 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.87 ms, Average inference time: 8.18 ms

2025-07-30 07:07:56.144 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:07:56.217 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:07:56.297 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch30
2025-07-30 07:07:59.856 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.172s, 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: 1.979e-03, size: 576, ETA: 1:44:46
2025-07-30 07:08:03.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.978e-03, size: 576, ETA: 1:44:45
2025-07-30 07:08:07.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.197s, 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: 1.978e-03, size: 512, ETA: 1:44:44
2025-07-30 07:08:11.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.977e-03, size: 544, ETA: 1:44:40
2025-07-30 07:08:15.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, 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: 1.977e-03, size: 512, ETA: 1:44:37
2025-07-30 07:08:18.969 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 30/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.977e-03, size: 544, ETA: 1:44:34
2025-07-30 07:08:20.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:08:27.604 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:08:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:08:29.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3517
2025-07-30 07:08:29.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3039
2025-07-30 07:08:29.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1660
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2739
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.166
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.274
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:08:29.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:08:29.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:08:29.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:08:29.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:08:29.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:08:29.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:08:29.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:08:29.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:08:30.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:08:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:08:31.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:08:32.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:08:33.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:08:34.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:08:34.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:08:35.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:08:36.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:08:36.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 07:08:36.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:08:36.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:08:36.464 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.92 ms, Average inference time: 8.40 ms

2025-07-30 07:08:36.465 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:08:36.532 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:08:36.657 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch31
2025-07-30 07:08:40.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.976e-03, size: 384, ETA: 1:44:28
2025-07-30 07:08:43.777 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.976e-03, size: 352, ETA: 1:44:24
2025-07-30 07:08:47.263 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.976e-03, size: 320, ETA: 1:44:18
2025-07-30 07:08:50.906 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.975e-03, size: 256, ETA: 1:44:14
2025-07-30 07:08:54.647 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.975e-03, size: 352, ETA: 1:44:11
2025-07-30 07:08:58.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 31/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.974e-03, size: 480, ETA: 1:44:09
2025-07-30 07:09:00.141 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:09:07.022 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:09:08.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:09:08.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3691
2025-07-30 07:09:09.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2807
2025-07-30 07:09:09.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1641
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2713
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.164
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.271
2025-07-30 07:09:09.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:09:09.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:09:09.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:09:10.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:09:11.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:09:12.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:09:13.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:09:14.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:09:15.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:09:16.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:09:16.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:09:16.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:09:16.884 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:09:16.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:09:16.898 | 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

2025-07-30 07:09:16.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:09:17.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:09:17.115 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch32
2025-07-30 07:09:20.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.974e-03, size: 512, ETA: 1:44:04
2025-07-30 07:09:24.424 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.973e-03, size: 416, ETA: 1:44:01
2025-07-30 07:09:28.250 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.973e-03, size: 512, ETA: 1:43:58
2025-07-30 07:09:32.038 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.973e-03, size: 448, ETA: 1:43:55
2025-07-30 07:09:35.838 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.4, lr: 1.972e-03, size: 288, ETA: 1:43:52
2025-07-30 07:09:39.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 32/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.972e-03, size: 320, ETA: 1:43:49
2025-07-30 07:09:41.161 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:09:48.063 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:09:49.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:09:50.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3187
2025-07-30 07:09:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2898
2025-07-30 07:09:51.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1718
2025-07-30 07:09:51.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2601
2025-07-30 07:09:51.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.172
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.260
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:09:51.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:09:51.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:09:51.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:09:52.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:09:53.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:09:55.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:09:56.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:09:58.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:09:59.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:10:00.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:10:02.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:10:03.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:10:03.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:10:03.502 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 07:10:03.502 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:10:03.512 | 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

2025-07-30 07:10:03.514 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:10:03.589 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:10:03.668 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch33
2025-07-30 07:10:07.298 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.7, lr: 1.971e-03, size: 256, ETA: 1:43:43
2025-07-30 07:10:10.998 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.3, lr: 1.971e-03, size: 416, ETA: 1:43:40
2025-07-30 07:10:14.614 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.3, lr: 1.971e-03, size: 256, ETA: 1:43:36
2025-07-30 07:10:18.249 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.970e-03, size: 256, ETA: 1:43:31
2025-07-30 07:10:21.842 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.970e-03, size: 352, ETA: 1:43:27
2025-07-30 07:10:25.553 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 33/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.969e-03, size: 448, ETA: 1:43:23
2025-07-30 07:10:27.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:10:34.480 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:10:35.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:10:36.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1824
2025-07-30 07:10:36.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1925
2025-07-30 07:10:36.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1186
2025-07-30 07:10:36.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1645
2025-07-30 07:10:36.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:10:36.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.182
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.119
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.165
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:10:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:10:36.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:10:36.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:10:36.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:10:37.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:10:38.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:10:38.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:10:39.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:10:40.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:10:40.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:10:41.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:10:42.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:10:42.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 07:10:42.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.16
2025-07-30 07:10:42.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:10:42.297 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.92 ms, Average inference time: 8.36 ms

2025-07-30 07:10:42.298 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:10:42.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:10:42.449 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch34
2025-07-30 07:10:45.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.969e-03, size: 352, ETA: 1:43:17
2025-07-30 07:10:49.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.177s, 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: 1.968e-03, size: 352, ETA: 1:43:13
2025-07-30 07:10:53.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.968e-03, size: 320, ETA: 1:43:09
2025-07-30 07:10:56.959 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.967e-03, size: 544, ETA: 1:43:05
2025-07-30 07:11:00.840 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.189s, 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: 1.967e-03, size: 320, ETA: 1:43:03
2025-07-30 07:11:04.463 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 34/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.967e-03, size: 256, ETA: 1:42:59
2025-07-30 07:11:06.177 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:11:13.237 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:11:13.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:11:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2932
2025-07-30 07:11:14.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2697
2025-07-30 07:11:14.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1534
2025-07-30 07:11:14.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2388
2025-07-30 07:11:14.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:11:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:11:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-07-30 07:11:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-07-30 07:11:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-07-30 07:11:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.239
2025-07-30 07:11:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:11:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:11:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:11:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:11:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:11:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:11:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:11:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:11:14.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:11:14.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:11:15.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:11:15.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:11:16.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:11:16.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:11:17.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:11:17.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:11:18.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:11:18.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:11:18.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:11:18.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-07-30 07:11:18.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:11:18.697 | 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

2025-07-30 07:11:18.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:11:18.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:11:18.854 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch35
2025-07-30 07:11:22.448 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.175s, 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: 1.966e-03, size: 576, ETA: 1:42:53
2025-07-30 07:11:26.117 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.966e-03, size: 320, ETA: 1:42:50
2025-07-30 07:11:29.722 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.174s, 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: 1.965e-03, size: 384, ETA: 1:42:45
2025-07-30 07:11:33.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.6, lr: 1.965e-03, size: 480, ETA: 1:42:42
2025-07-30 07:11:37.159 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/300, iter: 100/129, gpu mem: 1856Mb, 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: 2.7, cls_loss: 0.6, lr: 1.964e-03, size: 320, ETA: 1:42:39
2025-07-30 07:11:40.935 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 35/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.964e-03, size: 320, ETA: 1:42:36
2025-07-30 07:11:42.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:11:49.542 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:11:51.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:11:53.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4339
2025-07-30 07:11:53.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3969
2025-07-30 07:11:53.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2484
2025-07-30 07:11:53.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3597
2025-07-30 07:11:53.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:11:53.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.248
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.360
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:11:53.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:11:53.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:11:53.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:11:53.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:11:55.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:11:57.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:11:58.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:12:00.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:12:02.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:12:03.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:12:05.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:12:07.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:12:09.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:12:09.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 07:12:09.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 07:12:09.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:12:09.209 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.93 ms, Average inference time: 8.55 ms

2025-07-30 07:12:09.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:12:09.356 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:12:09.440 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch36
2025-07-30 07:12:12.900 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.963e-03, size: 416, ETA: 1:42:28
2025-07-30 07:12:16.610 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.963e-03, size: 544, ETA: 1:42:25
2025-07-30 07:12:20.290 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.962e-03, size: 448, ETA: 1:42:21
2025-07-30 07:12:23.861 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.962e-03, size: 320, ETA: 1:42:16
2025-07-30 07:12:27.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.961e-03, size: 416, ETA: 1:42:13
2025-07-30 07:12:31.256 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 36/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, 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: 1.961e-03, size: 384, ETA: 1:42:09
2025-07-30 07:12:32.828 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:12:39.752 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:12:42.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:12:43.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4419
2025-07-30 07:12:44.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3642
2025-07-30 07:12:44.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2205
2025-07-30 07:12:44.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3422
2025-07-30 07:12:44.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:12:44.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.342
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:12:44.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:12:44.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:12:46.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:12:48.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:12:50.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:12:52.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:12:54.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:12:56.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:12:58.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:13:00.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:13:02.419 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:13:02.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:13:02.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 07:13:02.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:13:02.446 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.32 ms, Average NMS time: 0.90 ms, Average inference time: 8.22 ms

2025-07-30 07:13:02.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:13:02.529 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:13:02.628 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch37
2025-07-30 07:13:05.996 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.960e-03, size: 288, ETA: 1:42:02
2025-07-30 07:13:09.560 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.960e-03, size: 352, ETA: 1:41:58
2025-07-30 07:13:13.147 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.174s, 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: 1.959e-03, size: 384, ETA: 1:41:53
2025-07-30 07:13:16.724 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.959e-03, size: 448, ETA: 1:41:49
2025-07-30 07:13:20.460 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, 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: 1.958e-03, size: 512, ETA: 1:41:46
2025-07-30 07:13:24.166 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 37/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.3, lr: 1.958e-03, size: 512, ETA: 1:41:42
2025-07-30 07:13:25.847 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:13:32.798 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:13:34.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:13:35.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1569
2025-07-30 07:13:35.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1874
2025-07-30 07:13:36.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1135
2025-07-30 07:13:36.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1526
2025-07-30 07:13:36.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:13:36.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:13:36.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.157
2025-07-30 07:13:36.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.187
2025-07-30 07:13:36.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.114
2025-07-30 07:13:36.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.153
2025-07-30 07:13:36.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:13:36.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:13:37.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:13:38.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:13:39.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:13:40.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:13:41.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:13:42.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:13:43.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:13:44.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:13:46.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:13:46.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.05
2025-07-30 07:13:46.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.15
2025-07-30 07:13:46.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:13:46.053 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.92 ms, Average inference time: 8.40 ms

2025-07-30 07:13:46.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:13:46.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:13:46.241 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch38
2025-07-30 07:13:49.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.957e-03, size: 544, ETA: 1:41:37
2025-07-30 07:13:53.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.957e-03, size: 256, ETA: 1:41:36
2025-07-30 07:13:57.321 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.956e-03, size: 352, ETA: 1:41:32
2025-07-30 07:14:01.081 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.956e-03, size: 544, ETA: 1:41:29
2025-07-30 07:14:04.739 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.955e-03, size: 480, ETA: 1:41:25
2025-07-30 07:14:08.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 38/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.955e-03, size: 512, ETA: 1:41:22
2025-07-30 07:14:10.138 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:14:17.062 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:14:19.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:14:20.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3737
2025-07-30 07:14:20.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3148
2025-07-30 07:14:20.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1959
2025-07-30 07:14:20.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2948
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.295
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:14:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:14:20.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:14:20.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:14:20.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:14:20.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:14:20.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:14:22.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:14:24.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:14:25.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:14:27.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:14:29.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:14:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:14:32.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:14:35.624 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:14:35.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:14:35.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 07:14:35.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:14:35.650 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.90 ms, Average inference time: 8.36 ms

2025-07-30 07:14:35.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:14:35.734 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:14:35.834 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch39
2025-07-30 07:14:39.326 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, 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: 1.954e-03, size: 512, ETA: 1:41:16
2025-07-30 07:14:43.039 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.953e-03, size: 480, ETA: 1:41:12
2025-07-30 07:14:46.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 0.7, lr: 1.953e-03, size: 352, ETA: 1:41:09
2025-07-30 07:14:50.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.952e-03, size: 544, ETA: 1:41:06
2025-07-30 07:14:54.310 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.952e-03, size: 448, ETA: 1:41:03
2025-07-30 07:14:58.188 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 39/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.951e-03, size: 288, ETA: 1:41:01
2025-07-30 07:14:59.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:15:06.688 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:15:09.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:15:11.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4184
2025-07-30 07:15:11.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3485
2025-07-30 07:15:11.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1932
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3200
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.320
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:15:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:15:11.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:15:11.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:15:11.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:15:11.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:15:11.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:15:11.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:15:11.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:15:13.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:15:15.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:15:17.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:15:20.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:15:22.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:15:24.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:15:26.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:15:28.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:15:30.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:15:30.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:15:30.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 07:15:30.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:15:30.983 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.91 ms, Average inference time: 8.31 ms

2025-07-30 07:15:30.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:15:31.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:15:31.136 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch40
2025-07-30 07:15:35.828 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/300, iter: 20/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.233s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.4, lr: 1.951e-03, size: 576, ETA: 1:41:03
2025-07-30 07:15:40.833 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/300, iter: 40/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.249s, 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: 1.950e-03, size: 480, ETA: 1:41:08
2025-07-30 07:15:45.134 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.213s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.949e-03, size: 544, ETA: 1:41:09
2025-07-30 07:15:48.949 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 1.0, lr: 1.949e-03, size: 320, ETA: 1:41:06
2025-07-30 07:15:52.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.948e-03, size: 256, ETA: 1:41:02
2025-07-30 07:15:56.314 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 40/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.006s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.948e-03, size: 544, ETA: 1:40:59
2025-07-30 07:15:58.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:16:04.856 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:16:06.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:16:06.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3223
2025-07-30 07:16:07.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2915
2025-07-30 07:16:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1727
2025-07-30 07:16:07.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2622
2025-07-30 07:16:07.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:16:07.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:16:07.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-07-30 07:16:07.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-07-30 07:16:07.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.262
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:16:07.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:16:08.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:16:09.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:16:10.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:16:11.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:16:12.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:16:13.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:16:14.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:16:15.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:16:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:16:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:16:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 07:16:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:16:16.393 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.91 ms, Average inference time: 8.32 ms

2025-07-30 07:16:16.395 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:16:16.518 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:16:16.601 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch41
2025-07-30 07:16:21.196 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/300, iter: 20/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.229s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.947e-03, size: 448, ETA: 1:41:00
2025-07-30 07:16:25.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/300, iter: 40/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.231s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.947e-03, size: 384, ETA: 1:41:03
2025-07-30 07:16:30.409 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/300, iter: 60/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.228s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.946e-03, size: 352, ETA: 1:41:05
2025-07-30 07:16:34.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/300, iter: 80/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.225s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.1, lr: 1.945e-03, size: 320, ETA: 1:41:07
2025-07-30 07:16:39.802 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/300, iter: 100/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.242s, data_time: 0.003s, total_loss: 9.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.8, cls_loss: 1.2, lr: 1.945e-03, size: 544, ETA: 1:41:11
2025-07-30 07:16:44.597 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 41/300, iter: 120/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.239s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.944e-03, size: 448, ETA: 1:41:15
2025-07-30 07:16:46.775 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:16:54.748 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:16:55.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:16:56.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4532
2025-07-30 07:16:56.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4097
2025-07-30 07:16:56.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2237
2025-07-30 07:16:56.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3622
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.362
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:16:56.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:16:56.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:16:56.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:16:56.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:16:56.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:16:56.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:16:57.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:16:58.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:16:59.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:17:00.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:17:00.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:17:01.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:17:02.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:17:03.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:17:03.973 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:17:03.973 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 07:17:03.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 07:17:03.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:17:03.988 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 8.05 ms, Average NMS time: 0.91 ms, Average inference time: 8.96 ms

2025-07-30 07:17:03.989 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:17:04.095 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:17:04.220 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch42
2025-07-30 07:17:08.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/300, iter: 20/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.230s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.944e-03, size: 384, ETA: 1:41:19
2025-07-30 07:17:13.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/300, iter: 40/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.221s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.943e-03, size: 256, ETA: 1:41:20
2025-07-30 07:17:17.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/300, iter: 60/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.230s, 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: 1.942e-03, size: 352, ETA: 1:41:23
2025-07-30 07:17:22.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/300, iter: 80/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.239s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.942e-03, size: 480, ETA: 1:41:26
2025-07-30 07:17:27.357 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/300, iter: 100/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.232s, data_time: 0.006s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.2, lr: 1.941e-03, size: 256, ETA: 1:41:28
2025-07-30 07:17:32.113 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 42/300, iter: 120/129, gpu mem: 1856Mb, mem: 80.2Gb, iter_time: 0.237s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.941e-03, size: 320, ETA: 1:41:31
2025-07-30 07:17:34.298 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:17:42.488 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:17:44.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:17:45.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3102
2025-07-30 07:17:45.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3328
2025-07-30 07:17:45.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1503
2025-07-30 07:17:45.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2645
2025-07-30 07:17:45.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:17:45.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:17:45.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-07-30 07:17:45.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-07-30 07:17:45.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.150
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.264
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:17:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:17:46.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:17:47.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:17:49.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:17:50.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:17:51.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:17:52.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:17:53.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:17:55.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:17:56.248 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:17:56.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:17:56.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 07:17:56.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:17:56.258 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.98 ms, Average NMS time: 0.96 ms, Average inference time: 8.95 ms

2025-07-30 07:17:56.259 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:17:56.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:17:56.405 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch43
2025-07-30 07:18:00.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.3, lr: 1.940e-03, size: 480, ETA: 1:41:29
2025-07-30 07:18:03.756 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.939e-03, size: 384, ETA: 1:41:25
2025-07-30 07:18:07.495 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, 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: 1.939e-03, size: 288, ETA: 1:41:21
2025-07-30 07:18:11.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.938e-03, size: 384, ETA: 1:41:17
2025-07-30 07:18:14.749 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.938e-03, size: 480, ETA: 1:41:12
2025-07-30 07:18:18.403 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 43/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.937e-03, size: 416, ETA: 1:41:08
2025-07-30 07:18:19.969 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:18:26.759 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:18:28.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:18:29.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4034
2025-07-30 07:18:30.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3385
2025-07-30 07:18:30.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1982
2025-07-30 07:18:30.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3134
2025-07-30 07:18:30.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:18:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:18:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:18:31.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:18:33.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:18:34.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:18:36.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:18:37.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:18:39.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:18:40.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:18:42.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:18:43.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:18:43.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:18:43.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 07:18:43.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:18:43.701 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.92 ms, Average inference time: 8.43 ms

2025-07-30 07:18:43.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:18:43.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:18:43.913 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch44
2025-07-30 07:18:47.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.936e-03, size: 544, ETA: 1:41:01
2025-07-30 07:18:51.263 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/300, iter: 40/129, gpu mem: 1856Mb, 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: 3.0, cls_loss: 0.9, lr: 1.936e-03, size: 352, ETA: 1:40:57
2025-07-30 07:18:54.946 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.935e-03, size: 320, ETA: 1:40:53
2025-07-30 07:18:58.902 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.934e-03, size: 288, ETA: 1:40:50
2025-07-30 07:19:02.949 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.200s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.934e-03, size: 576, ETA: 1:40:49
2025-07-30 07:19:06.763 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 44/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.933e-03, size: 352, ETA: 1:40:46
2025-07-30 07:19:08.361 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:19:15.336 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:19:19.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:19:22.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3938
2025-07-30 07:19:23.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2807
2025-07-30 07:19:23.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1951
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2899
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.290
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:19:23.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:19:23.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:19:23.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:19:23.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:19:23.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:19:23.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:19:26.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:19:30.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:19:34.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:19:37.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:19:41.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:19:44.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:19:48.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:19:51.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:19:55.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:19:55.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:19:55.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 07:19:55.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:19:55.369 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.92 ms, Average inference time: 8.50 ms

2025-07-30 07:19:55.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:19:55.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:19:55.581 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch45
2025-07-30 07:19:59.077 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.932e-03, size: 320, ETA: 1:40:39
2025-07-30 07:20:02.937 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.932e-03, size: 512, ETA: 1:40:36
2025-07-30 07:20:06.631 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.3Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.931e-03, size: 384, ETA: 1:40:32
2025-07-30 07:20:10.545 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.930e-03, size: 480, ETA: 1:40:29
2025-07-30 07:20:14.264 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.930e-03, size: 512, ETA: 1:40:25
2025-07-30 07:20:17.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 45/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.175s, 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: 1.929e-03, size: 320, ETA: 1:40:21
2025-07-30 07:20:19.520 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:20:26.512 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:20:27.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:20:28.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4020
2025-07-30 07:20:28.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3643
2025-07-30 07:20:28.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1944
2025-07-30 07:20:28.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3203
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.194
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.320
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:20:28.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:20:28.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:20:28.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:20:28.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:20:28.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:20:28.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:20:29.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:20:30.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:20:31.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:20:32.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:20:33.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:20:34.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:20:35.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:20:36.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:20:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:20:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:20:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 07:20:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:20:37.893 | 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

2025-07-30 07:20:37.895 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:20:37.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:20:38.053 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch46
2025-07-30 07:20:41.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.928e-03, size: 320, ETA: 1:40:15
2025-07-30 07:20:45.243 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.928e-03, size: 576, ETA: 1:40:10
2025-07-30 07:20:48.968 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 0.8, lr: 1.927e-03, size: 416, ETA: 1:40:06
2025-07-30 07:20:52.693 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.182s, 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: 1.926e-03, size: 256, ETA: 1:40:02
2025-07-30 07:20:56.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.6, lr: 1.926e-03, size: 480, ETA: 1:39:58
2025-07-30 07:21:00.107 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 46/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.185s, 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: 1.925e-03, size: 256, ETA: 1:39:54
2025-07-30 07:21:01.857 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:21:08.713 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:21:10.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:21:10.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2816
2025-07-30 07:21:10.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2376
2025-07-30 07:21:11.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1218
2025-07-30 07:21:11.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2137
2025-07-30 07:21:11.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:21:11.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.282
2025-07-30 07:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-07-30 07:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.122
2025-07-30 07:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.214
2025-07-30 07:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:21:11.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:21:11.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:21:12.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:21:12.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:21:13.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:21:14.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:21:15.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:21:17.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:21:18.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:21:19.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:21:19.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-07-30 07:21:19.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.21
2025-07-30 07:21:19.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:21:19.918 | 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

2025-07-30 07:21:19.923 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:21:20.054 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:21:20.136 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch47
2025-07-30 07:21:23.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.175s, 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: 1.924e-03, size: 512, ETA: 1:39:48
2025-07-30 07:21:27.405 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.924e-03, size: 288, ETA: 1:39:44
2025-07-30 07:21:30.972 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.923e-03, size: 480, ETA: 1:39:39
2025-07-30 07:21:34.915 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 1.1, lr: 1.922e-03, size: 288, ETA: 1:39:36
2025-07-30 07:21:38.559 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.922e-03, size: 480, ETA: 1:39:32
2025-07-30 07:21:42.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 47/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 0.9, lr: 1.921e-03, size: 352, ETA: 1:39:28
2025-07-30 07:21:43.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:21:50.617 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:21:51.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:21:52.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3045
2025-07-30 07:21:52.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2259
2025-07-30 07:21:52.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1691
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2332
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.226
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.233
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:21:52.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:21:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:21:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:21:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:21:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:21:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:21:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:21:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:21:54.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:21:55.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:21:56.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:21:57.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:21:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:21:59.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:22:00.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:22:01.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:22:02.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:22:02.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:22:02.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-07-30 07:22:02.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:22:02.183 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.55 ms, Average NMS time: 0.90 ms, Average inference time: 8.44 ms

2025-07-30 07:22:02.183 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:22:02.255 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:22:02.399 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch48
2025-07-30 07:22:05.948 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.920e-03, size: 448, ETA: 1:39:22
2025-07-30 07:22:09.685 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.919e-03, size: 576, ETA: 1:39:18
2025-07-30 07:22:13.480 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.919e-03, size: 416, ETA: 1:39:14
2025-07-30 07:22:17.360 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.918e-03, size: 352, ETA: 1:39:12
2025-07-30 07:22:21.212 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 8.3, iou_loss: 3.9, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.7, lr: 1.917e-03, size: 480, ETA: 1:39:08
2025-07-30 07:22:24.885 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 48/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.917e-03, size: 576, ETA: 1:39:04
2025-07-30 07:22:26.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:22:33.498 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:22:34.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:22:35.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1290
2025-07-30 07:22:35.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1769
2025-07-30 07:22:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0962
2025-07-30 07:22:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1341
2025-07-30 07:22:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:22:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:22:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.129
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.177
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.096
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.134
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:22:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:22:36.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:22:37.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:22:38.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:22:39.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:22:40.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:22:41.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:22:42.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:22:43.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:22:44.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:22:45.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:22:45.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 07:22:45.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.13
2025-07-30 07:22:45.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:22:45.686 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.90 ms, Average inference time: 8.37 ms

2025-07-30 07:22:45.688 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:22:45.756 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:22:45.833 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch49
2025-07-30 07:22:49.436 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/300, iter: 20/129, gpu mem: 1856Mb, 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: 2.8, cls_loss: 1.1, lr: 1.916e-03, size: 320, ETA: 1:38:59
2025-07-30 07:22:53.059 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.915e-03, size: 320, ETA: 1:38:54
2025-07-30 07:22:56.745 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, 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: 1.914e-03, size: 512, ETA: 1:38:50
2025-07-30 07:23:00.719 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.8, lr: 1.914e-03, size: 576, ETA: 1:38:48
2025-07-30 07:23:04.576 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.913e-03, size: 352, ETA: 1:38:45
2025-07-30 07:23:08.347 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 49/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.912e-03, size: 576, ETA: 1:38:41
2025-07-30 07:23:10.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:23:16.970 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:23:18.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:23:19.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4297
2025-07-30 07:23:19.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3969
2025-07-30 07:23:19.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2419
2025-07-30 07:23:19.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3561
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:23:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:23:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:23:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:23:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:23:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:23:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:23:21.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:23:22.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:23:23.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:23:24.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:23:25.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:23:27.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:23:28.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:23:29.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:23:30.699 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:23:30.699 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 07:23:30.699 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 07:23:30.699 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:23:30.708 | 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: 8.28 ms

2025-07-30 07:23:30.709 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:23:30.783 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:23:30.862 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch50
2025-07-30 07:23:34.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.911e-03, size: 448, ETA: 1:38:35
2025-07-30 07:23:38.206 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.911e-03, size: 288, ETA: 1:38:32
2025-07-30 07:23:41.783 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.175s, 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: 1.910e-03, size: 544, ETA: 1:38:28
2025-07-30 07:23:45.705 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.909e-03, size: 576, ETA: 1:38:25
2025-07-30 07:23:49.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.003s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 1.0, lr: 1.909e-03, size: 544, ETA: 1:38:22
2025-07-30 07:23:53.255 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 50/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.908e-03, size: 288, ETA: 1:38:18
2025-07-30 07:23:54.811 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:24:01.669 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:24:04.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:24:05.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3900
2025-07-30 07:24:06.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3250
2025-07-30 07:24:06.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1219
2025-07-30 07:24:06.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2790
2025-07-30 07:24:06.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:24:06.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:24:06.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-07-30 07:24:06.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.325
2025-07-30 07:24:06.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.122
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.279
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:24:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:24:08.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:24:10.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:24:12.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:24:14.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:24:16.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:24:18.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:24:20.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:24:23.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:24:25.106 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:24:25.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:24:25.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:24:25.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:24:25.135 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.92 ms, Average inference time: 8.41 ms

2025-07-30 07:24:25.136 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:24:25.261 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:24:25.344 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch51
2025-07-30 07:24:28.844 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.907e-03, size: 544, ETA: 1:38:11
2025-07-30 07:24:32.617 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 1.906e-03, size: 576, ETA: 1:38:08
2025-07-30 07:24:36.290 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.905e-03, size: 512, ETA: 1:38:04
2025-07-30 07:24:40.181 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.905e-03, size: 256, ETA: 1:38:01
2025-07-30 07:24:43.983 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.904e-03, size: 544, ETA: 1:37:58
2025-07-30 07:24:48.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 51/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 8.0, iou_loss: 4.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.6, lr: 1.903e-03, size: 480, ETA: 1:37:55
2025-07-30 07:24:49.642 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:24:56.496 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:25:00.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:25:02.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3971
2025-07-30 07:25:03.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2241
2025-07-30 07:25:03.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1471
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2561
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.147
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.256
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:25:03.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:25:03.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:25:03.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:25:03.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:25:03.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:25:03.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:25:06.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:25:08.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:25:11.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:25:14.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:25:17.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:25:20.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:25:23.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:25:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:25:28.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:25:28.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:25:28.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 07:25:28.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:25:28.921 | 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

2025-07-30 07:25:28.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:25:29.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:25:29.113 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch52
2025-07-30 07:25:32.539 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.902e-03, size: 384, ETA: 1:37:48
2025-07-30 07:25:36.154 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.902e-03, size: 480, ETA: 1:37:44
2025-07-30 07:25:39.844 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/300, iter: 60/129, gpu mem: 1856Mb, 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: 2.6, cls_loss: 1.0, lr: 1.901e-03, size: 256, ETA: 1:37:41
2025-07-30 07:25:43.662 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.900e-03, size: 576, ETA: 1:37:37
2025-07-30 07:25:47.401 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 1.0, lr: 1.899e-03, size: 288, ETA: 1:37:34
2025-07-30 07:25:51.012 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 52/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.899e-03, size: 384, ETA: 1:37:30
2025-07-30 07:25:52.710 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:25:59.520 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:26:00.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:26:01.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3315
2025-07-30 07:26:01.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3191
2025-07-30 07:26:01.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1634
2025-07-30 07:26:01.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2713
2025-07-30 07:26:01.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:26:01.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.163
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.271
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:26:01.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:26:01.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:26:01.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:26:01.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:26:02.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:26:03.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:26:04.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:26:04.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:26:05.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:26:06.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:26:07.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:26:08.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:26:09.134 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:26:09.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:26:09.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:26:09.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:26:09.144 | 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

2025-07-30 07:26:09.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:26:09.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:26:09.304 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch53
2025-07-30 07:26:13.430 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/300, iter: 20/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.203s, 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: 1.898e-03, size: 320, ETA: 1:37:26
2025-07-30 07:26:17.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/300, iter: 40/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.227s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.897e-03, size: 256, ETA: 1:37:27
2025-07-30 07:26:22.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/300, iter: 60/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.231s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.896e-03, size: 512, ETA: 1:37:28
2025-07-30 07:26:27.393 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/300, iter: 80/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.237s, 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: 1:37:29
2025-07-30 07:26:32.037 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/300, iter: 100/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.231s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.895e-03, size: 480, ETA: 1:37:30
2025-07-30 07:26:36.733 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 53/300, iter: 120/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.234s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.894e-03, size: 256, ETA: 1:37:31
2025-07-30 07:26:38.763 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:26:46.773 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:26:48.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:26:48.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2452
2025-07-30 07:26:49.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2537
2025-07-30 07:26:49.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1569
2025-07-30 07:26:49.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2186
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.245
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.157
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.219
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:26:49.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:26:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:26:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:26:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:26:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:26:49.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:26:50.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:26:51.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:26:52.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:26:52.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:26:53.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:26:54.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:26:55.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:26:56.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:26:57.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:26:57.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 07:26:57.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-07-30 07:26:57.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:26:57.678 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.84 ms, Average NMS time: 0.92 ms, Average inference time: 8.76 ms

2025-07-30 07:26:57.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:26:57.756 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:26:57.836 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch54
2025-07-30 07:27:02.173 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/300, iter: 20/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.216s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.893e-03, size: 288, ETA: 1:37:30
2025-07-30 07:27:06.838 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/300, iter: 40/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.232s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.892e-03, size: 512, ETA: 1:37:31
2025-07-30 07:27:11.536 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/300, iter: 60/129, gpu mem: 1856Mb, mem: 80.3Gb, iter_time: 0.234s, 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: 1.891e-03, size: 288, ETA: 1:37:32
2025-07-30 07:27:15.611 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.200s, 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: 1.890e-03, size: 256, ETA: 1:37:30
2025-07-30 07:27:19.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.890e-03, size: 544, ETA: 1:37:27
2025-07-30 07:27:23.340 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 54/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.889e-03, size: 448, ETA: 1:37:23
2025-07-30 07:27:24.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:27:32.022 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:27:34.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:27:36.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3998
2025-07-30 07:27:36.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3336
2025-07-30 07:27:36.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1955
2025-07-30 07:27:36.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3096
2025-07-30 07:27:36.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.310
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:27:36.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:27:36.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:27:36.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:27:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:27:40.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:27:42.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:27:44.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:27:46.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:27:48.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:27:50.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:27:51.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:27:53.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:27:53.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:27:53.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 07:27:53.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:27:53.655 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.90 ms, Average inference time: 8.38 ms

2025-07-30 07:27:53.660 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:27:53.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:27:53.817 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch55
2025-07-30 07:27:57.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.176s, 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: 1.888e-03, size: 544, ETA: 1:37:17
2025-07-30 07:28:01.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.887e-03, size: 320, ETA: 1:37:12
2025-07-30 07:28:04.951 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.188s, 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: 1.886e-03, size: 416, ETA: 1:37:09
2025-07-30 07:28:08.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.885e-03, size: 288, ETA: 1:37:05
2025-07-30 07:28:12.668 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.003s, total_loss: 8.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 4.7, cls_loss: 0.9, lr: 1.885e-03, size: 480, ETA: 1:37:02
2025-07-30 07:28:16.403 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 55/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.181s, 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: 1.884e-03, size: 320, ETA: 1:36:58
2025-07-30 07:28:17.986 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:28:24.882 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:28:26.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:28:27.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4314
2025-07-30 07:28:27.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3814
2025-07-30 07:28:27.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2364
2025-07-30 07:28:27.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3497
2025-07-30 07:28:27.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:28:27.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.350
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:28:27.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:28:27.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:28:27.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:28:27.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:28:27.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:28:28.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:28:29.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:28:30.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:28:31.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:28:32.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:28:33.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:28:34.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:28:35.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:28:36.576 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:28:36.576 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:28:36.577 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 07:28:36.577 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:28:36.585 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.91 ms, Average inference time: 8.35 ms

2025-07-30 07:28:36.586 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:28:36.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:28:36.763 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch56
2025-07-30 07:28:40.369 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.883e-03, size: 288, ETA: 1:36:52
2025-07-30 07:28:43.900 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.172s, 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: 1.882e-03, size: 480, ETA: 1:36:47
2025-07-30 07:28:47.572 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.7, lr: 1.881e-03, size: 544, ETA: 1:36:43
2025-07-30 07:28:51.517 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.191s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.880e-03, size: 384, ETA: 1:36:40
2025-07-30 07:28:55.334 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.188s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.880e-03, size: 352, ETA: 1:36:36
2025-07-30 07:28:59.083 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 56/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.185s, 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: 1.879e-03, size: 352, ETA: 1:36:33
2025-07-30 07:29:00.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:29:07.497 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:29:09.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:29:11.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3663
2025-07-30 07:29:11.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3461
2025-07-30 07:29:12.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1778
2025-07-30 07:29:12.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2967
2025-07-30 07:29:12.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:29:12.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:29:12.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-07-30 07:29:12.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.346
2025-07-30 07:29:12.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.178
2025-07-30 07:29:12.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.297
2025-07-30 07:29:12.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:29:12.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:29:12.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:29:12.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:29:12.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:29:12.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:29:12.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:29:12.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:29:12.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:29:14.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:29:16.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:29:18.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:29:20.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:29:22.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:29:24.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:29:26.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:29:28.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:29:30.750 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:29:30.750 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:29:30.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 07:29:30.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:29:30.779 | 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

2025-07-30 07:29:30.780 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:29:30.855 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:29:30.936 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch57
2025-07-30 07:29:34.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.878e-03, size: 352, ETA: 1:36:26
2025-07-30 07:29:38.162 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.6, lr: 1.877e-03, size: 288, ETA: 1:36:22
2025-07-30 07:29:41.885 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, 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: 1.876e-03, size: 384, ETA: 1:36:18
2025-07-30 07:29:45.617 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.875e-03, size: 512, ETA: 1:36:14
2025-07-30 07:29:49.325 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.182s, 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: 1.874e-03, size: 288, ETA: 1:36:10
2025-07-30 07:29:52.899 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 57/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.874e-03, size: 512, ETA: 1:36:06
2025-07-30 07:29:54.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:30:01.435 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:30:02.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:30:03.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4309
2025-07-30 07:30:04.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3345
2025-07-30 07:30:04.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2008
2025-07-30 07:30:04.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3220
2025-07-30 07:30:04.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:30:04.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:30:04.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-07-30 07:30:04.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-07-30 07:30:04.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:30:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:30:04.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:30:05.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:30:06.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:30:07.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:30:08.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:30:10.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:30:11.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:30:12.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:30:13.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:30:14.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:30:14.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:30:14.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 07:30:14.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:30:14.941 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.91 ms, Average inference time: 8.39 ms

2025-07-30 07:30:14.942 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:30:15.017 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:30:15.096 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch58
2025-07-30 07:30:18.753 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.872e-03, size: 416, ETA: 1:36:00
2025-07-30 07:30:22.390 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.872e-03, size: 416, ETA: 1:35:55
2025-07-30 07:30:26.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, 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: 1.871e-03, size: 352, ETA: 1:35:51
2025-07-30 07:30:29.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.181s, 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: 1.870e-03, size: 416, ETA: 1:35:47
2025-07-30 07:30:33.452 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, 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: 1.869e-03, size: 512, ETA: 1:35:43
2025-07-30 07:30:37.297 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 58/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.868e-03, size: 576, ETA: 1:35:40
2025-07-30 07:30:38.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:30:45.922 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:30:48.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:30:50.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4147
2025-07-30 07:30:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3306
2025-07-30 07:30:50.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1835
2025-07-30 07:30:50.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3096
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.310
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:30:50.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:30:50.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:30:50.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:30:50.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:30:52.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:30:54.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:30:56.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:30:59.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:31:01.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:31:03.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:31:05.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:31:07.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:31:09.485 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:31:09.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:31:09.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 07:31:09.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:31:09.511 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.48 ms, Average NMS time: 0.92 ms, Average inference time: 8.40 ms

2025-07-30 07:31:09.512 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:31:09.589 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:31:09.670 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch59
2025-07-30 07:31:13.387 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 1.867e-03, size: 384, ETA: 1:35:35
2025-07-30 07:31:17.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, 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: 1.866e-03, size: 512, ETA: 1:35:30
2025-07-30 07:31:20.840 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.186s, 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: 1.865e-03, size: 352, ETA: 1:35:27
2025-07-30 07:31:24.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.864e-03, size: 288, ETA: 1:35:23
2025-07-30 07:31:28.340 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.187s, 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: 1.864e-03, size: 416, ETA: 1:35:19
2025-07-30 07:31:32.041 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 59/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, 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: 1.863e-03, size: 480, ETA: 1:35:15
2025-07-30 07:31:33.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:31:40.646 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:31:42.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:31:43.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4129
2025-07-30 07:31:44.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3563
2025-07-30 07:31:44.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2073
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3255
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.326
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:31:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:31:44.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:31:44.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:31:44.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:31:44.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:31:44.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:31:44.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:31:44.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:31:45.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:31:47.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:31:48.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:31:50.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:31:51.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:31:53.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:31:54.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:31:56.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:31:57.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:31:57.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:31:57.820 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 07:31:57.820 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:31:57.876 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.91 ms, Average inference time: 8.37 ms

2025-07-30 07:31:57.879 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:31:57.951 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:31:58.035 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch60
2025-07-30 07:32:01.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.862e-03, size: 256, ETA: 1:35:09
2025-07-30 07:32:05.188 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.175s, 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: 1.861e-03, size: 256, ETA: 1:35:04
2025-07-30 07:32:08.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.172s, 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: 1.860e-03, size: 544, ETA: 1:35:00
2025-07-30 07:32:12.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.188s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.859e-03, size: 256, ETA: 1:34:56
2025-07-30 07:32:16.296 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.858e-03, size: 320, ETA: 1:34:53
2025-07-30 07:32:19.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 60/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.857e-03, size: 512, ETA: 1:34:49
2025-07-30 07:32:21.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:32:28.558 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:32:30.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:32:31.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3174
2025-07-30 07:32:31.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3058
2025-07-30 07:32:31.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1823
2025-07-30 07:32:31.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2685
2025-07-30 07:32:31.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.317
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.182
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.268
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:32:31.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:32:31.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:32:31.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:32:31.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:32:31.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:32:32.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:32:33.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:32:35.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:32:36.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:32:37.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:32:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:32:40.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:32:41.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:32:42.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:32:42.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:32:42.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:32:42.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:32:42.665 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.49 ms, Average NMS time: 0.91 ms, Average inference time: 8.39 ms

2025-07-30 07:32:42.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:32:42.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:32:42.896 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch61
2025-07-30 07:32:46.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.856e-03, size: 544, ETA: 1:34:43
2025-07-30 07:32:50.235 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.855e-03, size: 384, ETA: 1:34:39
2025-07-30 07:32:53.907 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.854e-03, size: 480, ETA: 1:34:35
2025-07-30 07:32:57.664 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.182s, 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: 1.853e-03, size: 448, ETA: 1:34:31
2025-07-30 07:33:01.307 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.176s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.852e-03, size: 352, ETA: 1:34:27
2025-07-30 07:33:05.198 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 61/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 0.9, lr: 1.852e-03, size: 480, ETA: 1:34:24
2025-07-30 07:33:06.910 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:33:13.803 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:33:15.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:33:16.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1581
2025-07-30 07:33:16.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1738
2025-07-30 07:33:16.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1051
2025-07-30 07:33:16.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1457
2025-07-30 07:33:16.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:33:16.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:33:16.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.158
2025-07-30 07:33:16.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.174
2025-07-30 07:33:16.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.105
2025-07-30 07:33:16.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.146
2025-07-30 07:33:16.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:33:16.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:33:16.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:33:16.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:33:16.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:33:16.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:33:16.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:33:16.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:33:16.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:33:17.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:33:18.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:33:19.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:33:20.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:33:21.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:33:22.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:33:23.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:33:23.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:33:24.744 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:33:24.744 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 07:33:24.744 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.15
2025-07-30 07:33:24.744 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:33:24.753 | 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

2025-07-30 07:33:24.754 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:33:24.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:33:24.913 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch62
2025-07-30 07:33:28.446 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.173s, 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: 1.850e-03, size: 320, ETA: 1:34:18
2025-07-30 07:33:32.152 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.849e-03, size: 256, ETA: 1:34:14
2025-07-30 07:33:35.942 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.849e-03, size: 352, ETA: 1:34:10
2025-07-30 07:33:39.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.848e-03, size: 288, ETA: 1:34:07
2025-07-30 07:33:43.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.187s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.847e-03, size: 288, ETA: 1:34:04
2025-07-30 07:33:47.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 62/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.846e-03, size: 544, ETA: 1:34:00
2025-07-30 07:33:49.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:33:56.473 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:33:58.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:33:59.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4209
2025-07-30 07:33:59.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3546
2025-07-30 07:33:59.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1956
2025-07-30 07:33:59.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3237
2025-07-30 07:33:59.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:33:59.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.324
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:33:59.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:33:59.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:33:59.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:33:59.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:33:59.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:34:00.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:34:02.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:34:03.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:34:04.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:34:06.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:34:07.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:34:08.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:34:10.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:34:11.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:34:11.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:34:11.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 07:34:11.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:34:11.444 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.94 ms, Average inference time: 8.41 ms

2025-07-30 07:34:11.445 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:34:11.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:34:11.704 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch63
2025-07-30 07:34:15.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.845e-03, size: 384, ETA: 1:33:55
2025-07-30 07:34:18.940 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, 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: 1.844e-03, size: 512, ETA: 1:33:51
2025-07-30 07:34:22.742 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.843e-03, size: 448, ETA: 1:33:47
2025-07-30 07:34:26.695 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.192s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.6, lr: 1.842e-03, size: 576, ETA: 1:33:44
2025-07-30 07:34:30.373 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.841e-03, size: 384, ETA: 1:33:40
2025-07-30 07:34:33.877 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 63/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.171s, 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: 1.840e-03, size: 384, ETA: 1:33:35
2025-07-30 07:34:35.511 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:34:42.490 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:34:43.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:34:44.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1111
2025-07-30 07:34:44.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.0952
2025-07-30 07:34:44.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0135
2025-07-30 07:34:44.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.0732
2025-07-30 07:34:44.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:34:44.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.111
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.095
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.013
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.073
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:34:44.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:34:44.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:34:45.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:34:46.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:34:46.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:34:47.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:34:48.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:34:48.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:34:49.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:34:50.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:34:51.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:34:51.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.03
2025-07-30 07:34:51.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.07
2025-07-30 07:34:51.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:34:51.204 | 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

2025-07-30 07:34:51.205 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:34:51.274 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:34:51.357 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch64
2025-07-30 07:34:55.018 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, 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: 1.839e-03, size: 256, ETA: 1:33:29
2025-07-30 07:34:58.858 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 0.7, lr: 1.838e-03, size: 384, ETA: 1:33:26
2025-07-30 07:35:02.791 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.2, lr: 1.837e-03, size: 320, ETA: 1:33:23
2025-07-30 07:35:06.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, 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: 320, ETA: 1:33:19
2025-07-30 07:35:10.148 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, 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: 1.835e-03, size: 384, ETA: 1:33:14
2025-07-30 07:35:13.876 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 64/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.834e-03, size: 320, ETA: 1:33:11
2025-07-30 07:35:15.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:35:22.351 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:35:24.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:35:25.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3435
2025-07-30 07:35:25.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3483
2025-07-30 07:35:25.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1900
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2939
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.190
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.294
2025-07-30 07:35:25.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:35:25.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:35:27.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:35:28.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:35:29.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:35:31.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:35:32.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:35:34.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:35:35.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:35:36.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:35:38.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:35:38.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:35:38.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 07:35:38.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:35:38.503 | 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: 8.39 ms

2025-07-30 07:35:38.505 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:35:38.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:35:38.653 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch65
2025-07-30 07:35:42.108 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.167s, 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: 1.833e-03, size: 256, ETA: 1:33:04
2025-07-30 07:35:45.971 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 0.9, lr: 1.832e-03, size: 544, ETA: 1:33:01
2025-07-30 07:35:49.836 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.831e-03, size: 512, ETA: 1:32:57
2025-07-30 07:35:53.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 0.7, lr: 1.830e-03, size: 544, ETA: 1:32:53
2025-07-30 07:35:57.059 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, 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: 1.829e-03, size: 416, ETA: 1:32:49
2025-07-30 07:36:00.828 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 65/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.828e-03, size: 544, ETA: 1:32:45
2025-07-30 07:36:02.561 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:36:09.603 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:36:11.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:36:12.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4222
2025-07-30 07:36:12.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3790
2025-07-30 07:36:12.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2100
2025-07-30 07:36:12.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3371
2025-07-30 07:36:12.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:36:12.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:36:12.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-07-30 07:36:12.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-07-30 07:36:12.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.210
2025-07-30 07:36:12.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-07-30 07:36:12.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:36:12.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:36:12.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:36:12.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:36:12.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:36:12.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:36:12.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:36:12.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:36:12.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:36:14.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:36:15.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:36:16.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:36:18.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:36:19.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:36:21.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:36:22.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:36:23.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:36:25.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:36:25.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:36:25.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 07:36:25.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:36:25.223 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.94 ms, Average inference time: 8.48 ms

2025-07-30 07:36:25.224 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:36:25.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:36:25.390 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch66
2025-07-30 07:36:28.925 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.171s, 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: 1.827e-03, size: 384, ETA: 1:32:39
2025-07-30 07:36:32.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.826e-03, size: 416, ETA: 1:32:35
2025-07-30 07:36:36.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.189s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.825e-03, size: 448, ETA: 1:32:32
2025-07-30 07:36:40.009 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.824e-03, size: 288, ETA: 1:32:28
2025-07-30 07:36:43.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.179s, data_time: 0.006s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.823e-03, size: 576, ETA: 1:32:24
2025-07-30 07:36:47.485 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 66/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, 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: 1.822e-03, size: 544, ETA: 1:32:20
2025-07-30 07:36:49.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:36:56.111 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:36:58.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:36:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3889
2025-07-30 07:36:59.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3352
2025-07-30 07:36:59.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1669
2025-07-30 07:36:59.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2970
2025-07-30 07:36:59.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.335
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.167
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.297
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:36:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:36:59.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:36:59.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:37:01.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:37:02.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:37:04.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:37:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:37:06.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:37:08.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:37:09.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:37:11.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:37:12.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:37:12.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:37:12.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 07:37:12.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:37:12.818 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.91 ms, Average inference time: 8.34 ms

2025-07-30 07:37:12.819 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:37:12.897 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:37:13.029 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch67
2025-07-30 07:37:16.552 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, 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: 1.821e-03, size: 544, ETA: 1:32:14
2025-07-30 07:37:20.287 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 8.4, iou_loss: 4.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.5, lr: 1.820e-03, size: 544, ETA: 1:32:11
2025-07-30 07:37:24.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.819e-03, size: 320, ETA: 1:32:07
2025-07-30 07:37:27.566 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.175s, 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: 1.818e-03, size: 544, ETA: 1:32:03
2025-07-30 07:37:31.315 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.817e-03, size: 256, ETA: 1:31:59
2025-07-30 07:37:35.108 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 67/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.816e-03, size: 480, ETA: 1:31:55
2025-07-30 07:37:36.874 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:37:43.703 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:37:44.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:37:45.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2899
2025-07-30 07:37:45.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2802
2025-07-30 07:37:45.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1758
2025-07-30 07:37:45.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2486
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.176
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.249
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:37:45.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:37:45.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:37:45.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:37:45.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:37:45.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:37:45.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:37:46.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:37:47.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:37:48.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:37:49.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:37:50.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:37:50.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:37:51.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:37:52.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:37:53.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:37:53.447 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:37:53.447 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-07-30 07:37:53.447 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:37:53.455 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.90 ms, Average inference time: 8.35 ms

2025-07-30 07:37:53.456 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:37:53.532 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:37:53.661 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch68
2025-07-30 07:37:57.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.814e-03, size: 576, ETA: 1:31:50
2025-07-30 07:38:01.133 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.813e-03, size: 320, ETA: 1:31:46
2025-07-30 07:38:04.722 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.812e-03, size: 416, ETA: 1:31:42
2025-07-30 07:38:08.328 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.811e-03, size: 544, ETA: 1:31:38
2025-07-30 07:38:12.000 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, 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: 1.810e-03, size: 320, ETA: 1:31:34
2025-07-30 07:38:15.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 68/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, 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: 1.809e-03, size: 448, ETA: 1:31:30
2025-07-30 07:38:17.364 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:38:24.451 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:38:26.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:38:27.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3971
2025-07-30 07:38:27.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3822
2025-07-30 07:38:27.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2061
2025-07-30 07:38:27.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3285
2025-07-30 07:38:27.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:38:27.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:38:27.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-07-30 07:38:27.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-07-30 07:38:27.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.206
2025-07-30 07:38:27.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.328
2025-07-30 07:38:27.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:38:27.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:38:27.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:38:27.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:38:27.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:38:27.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:38:27.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:38:27.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:38:27.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:38:28.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:38:29.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:38:31.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:38:32.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:38:33.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:38:34.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:38:36.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:38:37.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:38:38.408 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:38:38.408 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:38:38.409 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 07:38:38.409 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:38:38.418 | 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

2025-07-30 07:38:38.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:38:38.583 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:38:38.669 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch69
2025-07-30 07:38:42.326 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.808e-03, size: 512, ETA: 1:31:25
2025-07-30 07:38:46.165 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.0, lr: 1.807e-03, size: 288, ETA: 1:31:22
2025-07-30 07:38:49.915 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.806e-03, size: 352, ETA: 1:31:18
2025-07-30 07:38:53.754 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.805e-03, size: 512, ETA: 1:31:15
2025-07-30 07:38:57.426 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.804e-03, size: 352, ETA: 1:31:11
2025-07-30 07:39:01.090 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 69/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.803e-03, size: 352, ETA: 1:31:07
2025-07-30 07:39:02.729 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:39:09.572 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:39:11.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:39:13.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3979
2025-07-30 07:39:13.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2387
2025-07-30 07:39:13.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2006
2025-07-30 07:39:13.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2791
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.239
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.279
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:39:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:39:13.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:39:13.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:39:13.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:39:13.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:39:13.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:39:13.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:39:15.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:39:17.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:39:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:39:21.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:39:23.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:39:25.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:39:27.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:39:28.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:39:30.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:39:30.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:39:30.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:39:30.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:39:30.875 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.92 ms, Average inference time: 8.44 ms

2025-07-30 07:39:30.876 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:39:30.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:39:31.094 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch70
2025-07-30 07:39:34.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, 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: 1.802e-03, size: 288, ETA: 1:31:01
2025-07-30 07:39:38.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.801e-03, size: 352, ETA: 1:30:57
2025-07-30 07:39:42.042 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.8, cls_loss: 1.2, lr: 1.800e-03, size: 256, ETA: 1:30:53
2025-07-30 07:39:45.743 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.799e-03, size: 352, ETA: 1:30:49
2025-07-30 07:39:49.365 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.177s, 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: 1.798e-03, size: 320, ETA: 1:30:45
2025-07-30 07:39:52.971 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 70/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.5, lr: 1.797e-03, size: 288, ETA: 1:30:41
2025-07-30 07:39:54.592 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:40:01.488 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:40:03.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:40:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3868
2025-07-30 07:40:05.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2832
2025-07-30 07:40:05.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1270
2025-07-30 07:40:05.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2657
2025-07-30 07:40:05.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:40:05.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:40:05.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.127
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.266
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:40:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:40:05.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:40:07.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:40:08.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:40:10.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:40:11.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:40:13.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:40:14.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:40:16.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:40:17.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:40:19.344 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:40:19.344 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:40:19.344 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:40:19.344 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:40:19.373 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.92 ms, Average inference time: 8.43 ms

2025-07-30 07:40:19.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:40:19.449 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:40:19.526 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch71
2025-07-30 07:40:23.035 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.795e-03, size: 288, ETA: 1:30:35
2025-07-30 07:40:26.843 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.794e-03, size: 384, ETA: 1:30:31
2025-07-30 07:40:30.398 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.793e-03, size: 384, ETA: 1:30:27
2025-07-30 07:40:34.230 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.792e-03, size: 512, ETA: 1:30:23
2025-07-30 07:40:38.176 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.193s, data_time: 0.006s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.791e-03, size: 512, ETA: 1:30:20
2025-07-30 07:40:42.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 71/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 0.9, lr: 1.790e-03, size: 480, ETA: 1:30:17
2025-07-30 07:40:43.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:40:50.576 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:40:53.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:40:56.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2857
2025-07-30 07:40:56.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2991
2025-07-30 07:40:56.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1452
2025-07-30 07:40:56.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2433
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.145
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.243
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:40:56.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:40:56.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:40:56.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:40:56.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:40:56.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:40:56.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:40:59.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:41:02.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:41:04.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:41:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:41:10.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:41:12.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:41:15.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:41:18.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:41:20.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:41:20.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:41:20.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-07-30 07:41:20.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:41:20.802 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.42 ms, Average NMS time: 0.92 ms, Average inference time: 8.34 ms

2025-07-30 07:41:20.803 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:41:20.885 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:41:20.965 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch72
2025-07-30 07:41:24.579 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.788e-03, size: 320, ETA: 1:30:11
2025-07-30 07:41:28.348 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.787e-03, size: 576, ETA: 1:30:08
2025-07-30 07:41:32.064 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.786e-03, size: 320, ETA: 1:30:04
2025-07-30 07:41:35.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.8, lr: 1.785e-03, size: 544, ETA: 1:30:00
2025-07-30 07:41:39.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.191s, data_time: 0.005s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.784e-03, size: 384, ETA: 1:29:57
2025-07-30 07:41:43.635 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 72/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.191s, 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: 1.783e-03, size: 576, ETA: 1:29:54
2025-07-30 07:41:45.388 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:41:52.345 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:41:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:41:54.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2749
2025-07-30 07:41:55.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2723
2025-07-30 07:41:55.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1340
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2271
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.134
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.227
2025-07-30 07:41:55.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:41:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:41:56.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:41:57.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:41:58.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:41:59.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:42:00.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:42:01.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:42:02.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:42:03.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:42:04.207 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:42:04.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 07:42:04.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-07-30 07:42:04.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:42:04.217 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.90 ms, Average inference time: 8.29 ms

2025-07-30 07:42:04.224 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:42:04.343 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:42:04.423 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch73
2025-07-30 07:42:08.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.782e-03, size: 448, ETA: 1:29:48
2025-07-30 07:42:11.693 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.781e-03, size: 512, ETA: 1:29:45
2025-07-30 07:42:15.310 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.3, lr: 1.780e-03, size: 352, ETA: 1:29:41
2025-07-30 07:42:18.920 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.779e-03, size: 384, ETA: 1:29:36
2025-07-30 07:42:22.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.778e-03, size: 288, ETA: 1:29:33
2025-07-30 07:42:26.493 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 73/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, 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: 1.776e-03, size: 256, ETA: 1:29:29
2025-07-30 07:42:28.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:42:35.068 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:42:36.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:42:37.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4091
2025-07-30 07:42:37.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3622
2025-07-30 07:42:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2188
2025-07-30 07:42:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3300
2025-07-30 07:42:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:42:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:42:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-07-30 07:42:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-07-30 07:42:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.219
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:42:37.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:42:37.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:42:37.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:42:39.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:42:40.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:42:41.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:42:42.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:42:44.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:42:45.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:42:46.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:42:47.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:42:49.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:42:49.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:42:49.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 07:42:49.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:42:49.231 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.67 ms, Average NMS time: 0.91 ms, Average inference time: 8.58 ms

2025-07-30 07:42:49.232 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:42:49.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:42:49.391 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch74
2025-07-30 07:42:52.972 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.775e-03, size: 480, ETA: 1:29:23
2025-07-30 07:42:56.680 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.774e-03, size: 384, ETA: 1:29:20
2025-07-30 07:43:00.536 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 5.2, cls_loss: 0.8, lr: 1.773e-03, size: 576, ETA: 1:29:16
2025-07-30 07:43:04.394 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.7, lr: 1.772e-03, size: 384, ETA: 1:29:13
2025-07-30 07:43:08.154 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.771e-03, size: 480, ETA: 1:29:09
2025-07-30 07:43:12.009 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 74/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.190s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.770e-03, size: 448, ETA: 1:29:06
2025-07-30 07:43:13.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:43:20.572 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:43:21.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:43:22.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3034
2025-07-30 07:43:22.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3056
2025-07-30 07:43:22.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1628
2025-07-30 07:43:22.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2573
2025-07-30 07:43:22.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:43:22.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:43:22.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-07-30 07:43:22.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.163
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.257
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:43:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:43:22.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:43:23.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:43:24.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:43:25.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:43:26.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:43:27.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:43:28.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:43:29.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:43:30.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:43:31.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:43:31.897 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:43:31.897 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 07:43:31.897 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:43:31.908 | 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: 8.35 ms

2025-07-30 07:43:31.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:43:32.023 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:43:32.140 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch75
2025-07-30 07:43:35.752 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.768e-03, size: 384, ETA: 1:29:00
2025-07-30 07:43:39.605 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.767e-03, size: 544, ETA: 1:28:57
2025-07-30 07:43:43.371 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.766e-03, size: 352, ETA: 1:28:53
2025-07-30 07:43:47.028 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 8.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.1, lr: 1.765e-03, size: 352, ETA: 1:28:49
2025-07-30 07:43:50.891 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.764e-03, size: 448, ETA: 1:28:46
2025-07-30 07:43:54.737 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 75/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.1, lr: 1.763e-03, size: 544, ETA: 1:28:42
2025-07-30 07:43:56.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:44:03.388 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:44:07.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:44:10.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3810
2025-07-30 07:44:10.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2899
2025-07-30 07:44:11.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1920
2025-07-30 07:44:11.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2876
2025-07-30 07:44:11.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:44:11.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.192
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.288
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:44:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:44:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:44:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:44:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:44:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:44:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:44:14.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:44:17.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:44:21.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:44:24.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:44:27.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:44:31.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:44:34.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:44:37.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:44:41.301 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:44:41.301 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:44:41.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 07:44:41.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:44:41.331 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.91 ms, Average inference time: 8.36 ms

2025-07-30 07:44:41.332 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:44:41.406 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:44:41.486 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch76
2025-07-30 07:44:45.018 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.761e-03, size: 416, ETA: 1:28:36
2025-07-30 07:44:49.071 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.196s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.760e-03, size: 448, ETA: 1:28:33
2025-07-30 07:44:52.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.759e-03, size: 352, ETA: 1:28:29
2025-07-30 07:44:56.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.758e-03, size: 416, ETA: 1:28:26
2025-07-30 07:45:00.207 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.757e-03, size: 288, ETA: 1:28:22
2025-07-30 07:45:03.989 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 76/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.756e-03, size: 576, ETA: 1:28:18
2025-07-30 07:45:05.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:45:12.500 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:45:14.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:45:15.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3234
2025-07-30 07:45:15.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3377
2025-07-30 07:45:15.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1495
2025-07-30 07:45:15.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2702
2025-07-30 07:45:15.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:45:15.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.150
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.270
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:45:15.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:45:15.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:45:17.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:45:18.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:45:19.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:45:21.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:45:22.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:45:24.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:45:25.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:45:26.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:45:27.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:45:27.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:45:27.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:45:27.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:45:27.972 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.40 ms, Average NMS time: 0.91 ms, Average inference time: 8.30 ms

2025-07-30 07:45:27.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:45:28.049 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:45:28.130 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch77
2025-07-30 07:45:31.677 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.754e-03, size: 416, ETA: 1:28:12
2025-07-30 07:45:35.269 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, 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: 1.753e-03, size: 320, ETA: 1:28:08
2025-07-30 07:45:39.075 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.2, lr: 1.752e-03, size: 416, ETA: 1:28:05
2025-07-30 07:45:42.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.751e-03, size: 320, ETA: 1:28:01
2025-07-30 07:45:46.803 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.750e-03, size: 544, ETA: 1:27:58
2025-07-30 07:45:50.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 77/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.748e-03, size: 288, ETA: 1:27:54
2025-07-30 07:45:52.142 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:45:59.097 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:46:01.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:46:02.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3372
2025-07-30 07:46:02.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3270
2025-07-30 07:46:02.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1881
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2841
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.188
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.284
2025-07-30 07:46:02.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:46:02.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:46:04.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:46:05.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:46:07.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:46:08.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:46:10.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:46:11.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:46:13.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:46:14.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:46:16.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:46:16.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-07-30 07:46:16.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:46:16.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:46:16.411 | 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

2025-07-30 07:46:16.412 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:46:16.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:46:16.592 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch78
2025-07-30 07:46:20.218 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, 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: 1.747e-03, size: 416, ETA: 1:27:48
2025-07-30 07:46:23.911 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.7, lr: 1.746e-03, size: 544, ETA: 1:27:44
2025-07-30 07:46:27.678 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.745e-03, size: 448, ETA: 1:27:41
2025-07-30 07:46:31.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.743e-03, size: 480, ETA: 1:27:37
2025-07-30 07:46:35.116 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.742e-03, size: 512, ETA: 1:27:33
2025-07-30 07:46:39.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 78/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.191s, 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: 1.741e-03, size: 480, ETA: 1:27:30
2025-07-30 07:46:40.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:46:47.824 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:46:51.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:46:53.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4064
2025-07-30 07:46:53.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3467
2025-07-30 07:46:54.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2122
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3218
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.212
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:46:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:46:54.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:46:54.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:46:54.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:46:54.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:46:54.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:46:54.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:46:54.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:46:57.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:46:59.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:47:02.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:47:05.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:47:08.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:47:11.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:47:14.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:47:17.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:47:19.957 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:47:19.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:47:19.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 07:47:19.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:47:19.991 | 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

2025-07-30 07:47:19.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:47:20.078 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:47:20.188 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch79
2025-07-30 07:47:23.605 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.740e-03, size: 576, ETA: 1:27:24
2025-07-30 07:47:27.460 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.738e-03, size: 384, ETA: 1:27:20
2025-07-30 07:47:31.170 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.6, lr: 1.737e-03, size: 384, ETA: 1:27:16
2025-07-30 07:47:35.102 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.191s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.736e-03, size: 288, ETA: 1:27:13
2025-07-30 07:47:38.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.735e-03, size: 384, ETA: 1:27:09
2025-07-30 07:47:42.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 79/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, 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: 1.734e-03, size: 352, ETA: 1:27:06
2025-07-30 07:47:44.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:47:50.951 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:47:52.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:47:53.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3761
2025-07-30 07:47:53.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3281
2025-07-30 07:47:53.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1663
2025-07-30 07:47:53.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2902
2025-07-30 07:47:53.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:47:53.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.166
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.290
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:47:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:47:53.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:47:53.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:47:54.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:47:55.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:47:56.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:47:58.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:47:59.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:48:00.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:48:01.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:48:02.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:48:03.860 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:48:03.860 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 07:48:03.860 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 07:48:03.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:48:03.869 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.92 ms, Average inference time: 8.33 ms

2025-07-30 07:48:03.870 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:48:03.945 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:48:04.025 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch80
2025-07-30 07:48:07.561 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.6, lr: 1.732e-03, size: 320, ETA: 1:26:59
2025-07-30 07:48:11.274 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, 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: 1.731e-03, size: 384, ETA: 1:26:56
2025-07-30 07:48:14.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.730e-03, size: 320, ETA: 1:26:52
2025-07-30 07:48:18.660 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.184s, 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: 1.729e-03, size: 416, ETA: 1:26:48
2025-07-30 07:48:22.441 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.728e-03, size: 576, ETA: 1:26:44
2025-07-30 07:48:26.367 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 80/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.195s, 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: 1.727e-03, size: 448, ETA: 1:26:41
2025-07-30 07:48:28.164 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:48:35.004 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:48:37.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:48:38.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3790
2025-07-30 07:48:38.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3461
2025-07-30 07:48:38.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1700
2025-07-30 07:48:38.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2984
2025-07-30 07:48:38.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:48:38.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:48:38.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-07-30 07:48:38.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.346
2025-07-30 07:48:38.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.170
2025-07-30 07:48:38.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.298
2025-07-30 07:48:38.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:48:38.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:48:38.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:48:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:48:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:48:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:48:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:48:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:48:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:48:40.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:48:41.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:48:43.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:48:44.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:48:46.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:48:47.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:48:49.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:48:50.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:48:52.066 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:48:52.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:48:52.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 07:48:52.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:48:52.077 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.93 ms, Average inference time: 8.42 ms

2025-07-30 07:48:52.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:48:52.153 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:48:52.234 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch81
2025-07-30 07:48:55.764 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.725e-03, size: 352, ETA: 1:26:36
2025-07-30 07:48:59.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.189s, data_time: 0.003s, total_loss: 6.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.5, lr: 1.724e-03, size: 576, ETA: 1:26:32
2025-07-30 07:49:03.667 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.4Gb, iter_time: 0.193s, 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: 1.723e-03, size: 576, ETA: 1:26:29
2025-07-30 07:49:07.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.199s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.721e-03, size: 576, ETA: 1:26:26
2025-07-30 07:49:11.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.193s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.720e-03, size: 512, ETA: 1:26:23
2025-07-30 07:49:15.508 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 81/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.190s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.719e-03, size: 512, ETA: 1:26:20
2025-07-30 07:49:17.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:49:24.158 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:49:26.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:49:27.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3690
2025-07-30 07:49:28.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3609
2025-07-30 07:49:28.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1853
2025-07-30 07:49:28.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3051
2025-07-30 07:49:28.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.185
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.305
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:49:28.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:49:28.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:49:28.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:49:29.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:49:31.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:49:32.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:49:34.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:49:36.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:49:37.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:49:39.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:49:40.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:49:42.309 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:49:42.309 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:49:42.309 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 07:49:42.310 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:49:42.334 | 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

2025-07-30 07:49:42.335 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:49:42.427 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:49:42.522 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch82
2025-07-30 07:49:46.039 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.717e-03, size: 480, ETA: 1:26:14
2025-07-30 07:49:49.682 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.716e-03, size: 384, ETA: 1:26:10
2025-07-30 07:49:53.350 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.715e-03, size: 512, ETA: 1:26:06
2025-07-30 07:49:57.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.714e-03, size: 480, ETA: 1:26:03
2025-07-30 07:50:00.882 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.713e-03, size: 480, ETA: 1:25:59
2025-07-30 07:50:04.666 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 82/300, iter: 120/129, gpu mem: 1856Mb, 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: 2.9, cls_loss: 0.6, lr: 1.711e-03, size: 480, ETA: 1:25:55
2025-07-30 07:50:06.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:50:13.265 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:50:15.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:50:16.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3591
2025-07-30 07:50:16.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3014
2025-07-30 07:50:16.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1860
2025-07-30 07:50:16.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2822
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.282
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:50:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:50:16.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:50:16.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:50:16.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:50:16.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:50:16.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:50:16.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:50:17.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:50:19.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:50:20.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:50:22.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:50:23.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:50:24.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:50:26.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:50:27.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:50:29.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:50:29.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:50:29.120 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:50:29.120 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:50:29.130 | 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

2025-07-30 07:50:29.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:50:29.199 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:50:29.278 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch83
2025-07-30 07:50:32.937 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.710e-03, size: 480, ETA: 1:25:50
2025-07-30 07:50:36.801 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.191s, 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: 1.709e-03, size: 576, ETA: 1:25:46
2025-07-30 07:50:40.511 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, 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: 1.707e-03, size: 320, ETA: 1:25:43
2025-07-30 07:50:44.339 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.706e-03, size: 512, ETA: 1:25:39
2025-07-30 07:50:48.114 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.705e-03, size: 512, ETA: 1:25:36
2025-07-30 07:50:51.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 83/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.704e-03, size: 384, ETA: 1:25:32
2025-07-30 07:50:53.547 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:51:00.420 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:51:02.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:51:03.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3507
2025-07-30 07:51:03.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3077
2025-07-30 07:51:03.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1615
2025-07-30 07:51:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2733
2025-07-30 07:51:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:51:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:51:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-07-30 07:51:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-07-30 07:51:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.162
2025-07-30 07:51:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.273
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:51:03.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:51:04.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:51:05.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:51:06.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:51:07.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:51:09.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:51:10.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:51:11.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:51:12.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:51:13.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:51:13.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:51:13.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:51:13.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:51:13.956 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.30 ms, Average NMS time: 0.90 ms, Average inference time: 8.20 ms

2025-07-30 07:51:13.957 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:51:14.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:51:14.140 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch84
2025-07-30 07:51:17.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.702e-03, size: 512, ETA: 1:25:26
2025-07-30 07:51:21.477 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.701e-03, size: 320, ETA: 1:25:22
2025-07-30 07:51:25.227 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.700e-03, size: 352, ETA: 1:25:19
2025-07-30 07:51:28.994 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.698e-03, size: 448, ETA: 1:25:15
2025-07-30 07:51:32.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.191s, 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: 1.697e-03, size: 576, ETA: 1:25:12
2025-07-30 07:51:36.910 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 84/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.195s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.2, lr: 1.696e-03, size: 480, ETA: 1:25:09
2025-07-30 07:51:38.568 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:51:45.518 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:51:47.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:51:49.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4055
2025-07-30 07:51:49.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3483
2025-07-30 07:51:49.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1979
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3172
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.317
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:51:49.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:51:49.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:51:49.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:51:49.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:51:49.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:51:49.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:51:49.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:51:49.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:51:51.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:51:52.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:51:54.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:51:56.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:51:57.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:51:59.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:52:01.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:52:02.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:52:04.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:52:04.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 07:52:04.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 07:52:04.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:52:04.866 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.29 ms, Average NMS time: 0.91 ms, Average inference time: 8.20 ms

2025-07-30 07:52:04.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:52:04.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:52:05.062 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch85
2025-07-30 07:52:08.751 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.178s, 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: 1.694e-03, size: 512, ETA: 1:25:03
2025-07-30 07:52:12.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, 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: 1.693e-03, size: 544, ETA: 1:24:59
2025-07-30 07:52:16.230 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.692e-03, size: 384, ETA: 1:24:56
2025-07-30 07:52:20.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.2, lr: 1.691e-03, size: 384, ETA: 1:24:52
2025-07-30 07:52:24.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.193s, 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: 1.689e-03, size: 576, ETA: 1:24:49
2025-07-30 07:52:27.802 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 85/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.7, lr: 1.688e-03, size: 256, ETA: 1:24:45
2025-07-30 07:52:29.512 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:52:36.521 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:52:37.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:52:38.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3525
2025-07-30 07:52:38.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2966
2025-07-30 07:52:38.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1983
2025-07-30 07:52:38.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2825
2025-07-30 07:52:38.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:52:38.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:52:38.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-07-30 07:52:38.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-07-30 07:52:38.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.198
2025-07-30 07:52:38.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.282
2025-07-30 07:52:38.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:52:38.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:52:38.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:52:38.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:52:38.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:52:38.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:52:38.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:52:38.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:52:38.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:52:39.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:52:40.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:52:41.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:52:42.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:52:43.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:52:44.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:52:45.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:52:46.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:52:47.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:52:47.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:52:47.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:52:47.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:52:47.722 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.92 ms, Average inference time: 8.32 ms

2025-07-30 07:52:47.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:52:47.802 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:52:47.884 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch86
2025-07-30 07:52:51.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.177s, 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: 1.686e-03, size: 448, ETA: 1:24:40
2025-07-30 07:52:55.321 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.184s, 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: 1.685e-03, size: 416, ETA: 1:24:36
2025-07-30 07:52:59.106 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.684e-03, size: 480, ETA: 1:24:33
2025-07-30 07:53:02.880 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.683e-03, size: 384, ETA: 1:24:29
2025-07-30 07:53:06.604 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, 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: 1.682e-03, size: 256, ETA: 1:24:25
2025-07-30 07:53:10.192 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 86/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.680e-03, size: 288, ETA: 1:24:21
2025-07-30 07:53:11.828 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:53:18.923 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:53:22.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:53:23.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4013
2025-07-30 07:53:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3217
2025-07-30 07:53:24.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1890
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3040
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.189
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.304
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:53:24.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:53:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:53:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:53:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:53:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:53:24.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:53:26.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:53:29.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:53:31.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:53:34.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:53:36.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:53:39.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:53:41.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:53:44.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:53:46.453 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:53:46.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:53:46.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 07:53:46.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:53:46.480 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.93 ms, Average inference time: 8.37 ms

2025-07-30 07:53:46.481 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:53:46.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:53:46.641 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch87
2025-07-30 07:53:50.105 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, 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: 1.679e-03, size: 256, ETA: 1:24:15
2025-07-30 07:53:53.608 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.171s, 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: 1.677e-03, size: 352, ETA: 1:24:11
2025-07-30 07:53:57.405 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.2, lr: 1.676e-03, size: 288, ETA: 1:24:07
2025-07-30 07:54:01.456 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.194s, data_time: 0.003s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.675e-03, size: 480, ETA: 1:24:04
2025-07-30 07:54:05.149 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.674e-03, size: 480, ETA: 1:24:00
2025-07-30 07:54:08.833 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 87/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.672e-03, size: 384, ETA: 1:23:57
2025-07-30 07:54:10.534 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:54:17.429 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:54:18.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:54:19.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3630
2025-07-30 07:54:20.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3299
2025-07-30 07:54:20.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1613
2025-07-30 07:54:20.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2847
2025-07-30 07:54:20.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.330
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.161
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.285
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:54:20.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:54:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:54:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:54:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:54:21.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:54:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:54:23.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:54:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:54:25.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:54:26.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:54:27.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:54:29.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:54:30.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:54:30.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:54:30.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 07:54:30.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:54:30.273 | 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

2025-07-30 07:54:30.275 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:54:30.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:54:30.434 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch88
2025-07-30 07:54:34.066 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.671e-03, size: 480, ETA: 1:23:51
2025-07-30 07:54:37.739 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.669e-03, size: 320, ETA: 1:23:47
2025-07-30 07:54:41.548 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.185s, 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: 1.668e-03, size: 288, ETA: 1:23:43
2025-07-30 07:54:45.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.181s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.667e-03, size: 576, ETA: 1:23:40
2025-07-30 07:54:49.101 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.666e-03, size: 416, ETA: 1:23:36
2025-07-30 07:54:52.761 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 88/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.664e-03, size: 480, ETA: 1:23:32
2025-07-30 07:54:54.521 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:55:01.380 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:55:03.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:55:04.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4194
2025-07-30 07:55:04.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3300
2025-07-30 07:55:05.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1896
2025-07-30 07:55:05.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3130
2025-07-30 07:55:05.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:55:05.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:55:05.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-07-30 07:55:05.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.330
2025-07-30 07:55:05.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.190
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:55:05.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:55:06.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:55:08.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:55:09.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:55:11.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:55:13.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:55:14.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:55:16.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:55:17.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:55:19.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:55:19.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 07:55:19.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 07:55:19.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:55:19.400 | 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

2025-07-30 07:55:19.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:55:19.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:55:19.564 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch89
2025-07-30 07:55:23.171 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.662e-03, size: 512, ETA: 1:23:27
2025-07-30 07:55:27.045 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.189s, 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: 1.661e-03, size: 480, ETA: 1:23:23
2025-07-30 07:55:30.866 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.188s, 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: 1.660e-03, size: 384, ETA: 1:23:20
2025-07-30 07:55:34.582 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.659e-03, size: 480, ETA: 1:23:16
2025-07-30 07:55:38.321 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.657e-03, size: 288, ETA: 1:23:13
2025-07-30 07:55:42.003 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 89/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.656e-03, size: 416, ETA: 1:23:09
2025-07-30 07:55:43.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:55:50.652 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:55:52.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:55:54.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4467
2025-07-30 07:55:54.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3608
2025-07-30 07:55:54.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1931
2025-07-30 07:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3335
2025-07-30 07:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-07-30 07:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-07-30 07:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-07-30 07:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.334
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:55:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:55:56.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:55:58.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:55:59.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:56:01.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:56:03.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:56:04.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:56:06.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:56:08.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:56:10.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:56:10.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:56:10.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 07:56:10.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:56:10.060 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.90 ms, Average inference time: 8.51 ms

2025-07-30 07:56:10.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:56:10.140 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:56:10.224 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch90
2025-07-30 07:56:13.801 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.654e-03, size: 448, ETA: 1:23:03
2025-07-30 07:56:17.588 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.184s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.653e-03, size: 576, ETA: 1:22:59
2025-07-30 07:56:21.594 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.197s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.652e-03, size: 352, ETA: 1:22:56
2025-07-30 07:56:25.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.651e-03, size: 480, ETA: 1:22:53
2025-07-30 07:56:28.972 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.181s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 0.8, lr: 1.649e-03, size: 544, ETA: 1:22:49
2025-07-30 07:56:32.945 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 90/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.6, lr: 1.648e-03, size: 352, ETA: 1:22:46
2025-07-30 07:56:34.600 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:56:41.583 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:56:44.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:56:46.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4021
2025-07-30 07:56:47.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3595
2025-07-30 07:56:47.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2356
2025-07-30 07:56:47.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3324
2025-07-30 07:56:47.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:56:47.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:56:47.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 07:56:47.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.332
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:56:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:56:47.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:56:49.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:56:52.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:56:54.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:56:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:56:59.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:57:01.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:57:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:57:06.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:57:09.370 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:57:09.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:57:09.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 07:57:09.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:57:09.401 | 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

2025-07-30 07:57:09.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:57:09.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:57:09.571 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch91
2025-07-30 07:57:13.193 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.2, lr: 1.646e-03, size: 448, ETA: 1:22:40
2025-07-30 07:57:17.039 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.189s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.645e-03, size: 576, ETA: 1:22:37
2025-07-30 07:57:20.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.188s, 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: 1.644e-03, size: 480, ETA: 1:22:33
2025-07-30 07:57:24.691 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.642e-03, size: 480, ETA: 1:22:30
2025-07-30 07:57:28.362 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.182s, 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: 1.641e-03, size: 384, ETA: 1:22:26
2025-07-30 07:57:32.308 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 91/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.194s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.640e-03, size: 576, ETA: 1:22:23
2025-07-30 07:57:34.031 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:57:40.991 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:57:42.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:57:43.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2960
2025-07-30 07:57:43.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2135
2025-07-30 07:57:43.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1167
2025-07-30 07:57:43.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2088
2025-07-30 07:57:43.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:57:43.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:57:43.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-07-30 07:57:43.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-07-30 07:57:43.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.117
2025-07-30 07:57:43.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.209
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:57:43.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:57:44.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:57:45.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:57:46.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:57:47.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:57:48.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:57:49.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:57:50.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:57:51.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:57:52.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:57:52.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 07:57:52.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.21
2025-07-30 07:57:52.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:57:52.817 | 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

2025-07-30 07:57:52.818 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:57:52.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:57:53.000 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch92
2025-07-30 07:57:56.538 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.638e-03, size: 448, ETA: 1:22:17
2025-07-30 07:58:00.108 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.637e-03, size: 256, ETA: 1:22:13
2025-07-30 07:58:03.892 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/300, iter: 60/129, gpu mem: 1856Mb, 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: 2.9, cls_loss: 1.1, lr: 1.635e-03, size: 288, ETA: 1:22:09
2025-07-30 07:58:07.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.191s, data_time: 0.003s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.634e-03, size: 256, ETA: 1:22:06
2025-07-30 07:58:11.704 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.192s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.633e-03, size: 416, ETA: 1:22:03
2025-07-30 07:58:15.431 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 92/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.184s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.631e-03, size: 448, ETA: 1:21:59
2025-07-30 07:58:17.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:58:24.282 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:58:26.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:58:28.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3234
2025-07-30 07:58:28.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3139
2025-07-30 07:58:28.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1860
2025-07-30 07:58:28.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2744
2025-07-30 07:58:28.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:58:28.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:58:28.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-07-30 07:58:28.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-07-30 07:58:28.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.274
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:58:28.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:58:31.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:58:33.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:58:35.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:58:37.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:58:39.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:58:41.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:58:43.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:58:45.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:58:47.423 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:58:47.423 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 07:58:47.423 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 07:58:47.423 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:58:47.452 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.37 ms, Average NMS time: 0.90 ms, Average inference time: 8.27 ms

2025-07-30 07:58:47.456 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:58:47.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:58:47.703 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch93
2025-07-30 07:58:51.201 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.629e-03, size: 256, ETA: 1:21:53
2025-07-30 07:58:55.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.628e-03, size: 512, ETA: 1:21:50
2025-07-30 07:58:58.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.627e-03, size: 448, ETA: 1:21:46
2025-07-30 07:59:02.771 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.195s, 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: 1.625e-03, size: 480, ETA: 1:21:43
2025-07-30 07:59:06.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, 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: 1.624e-03, size: 544, ETA: 1:21:40
2025-07-30 07:59:10.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 93/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.186s, data_time: 0.002s, total_loss: 9.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 1.8, lr: 1.623e-03, size: 576, ETA: 1:21:36
2025-07-30 07:59:12.142 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:59:19.158 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 07:59:20.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 07:59:20.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4153
2025-07-30 07:59:21.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3268
2025-07-30 07:59:21.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1721
2025-07-30 07:59:21.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3047
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.172
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.305
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 07:59:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 07:59:21.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 07:59:21.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 07:59:21.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 07:59:21.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 07:59:21.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 07:59:21.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 07:59:22.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 07:59:23.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 07:59:24.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 07:59:25.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 07:59:26.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 07:59:26.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 07:59:27.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 07:59:28.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 07:59:28.639 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 07:59:28.639 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 07:59:28.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 07:59:28.653 | 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: 8.34 ms

2025-07-30 07:59:28.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:59:28.796 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 07:59:28.883 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch94
2025-07-30 07:59:32.450 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.1, lr: 1.621e-03, size: 544, ETA: 1:21:30
2025-07-30 07:59:36.188 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.620e-03, size: 544, ETA: 1:21:27
2025-07-30 07:59:40.022 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, 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: 1.618e-03, size: 448, ETA: 1:21:23
2025-07-30 07:59:43.891 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.188s, 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: 1.617e-03, size: 576, ETA: 1:21:20
2025-07-30 07:59:47.654 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.185s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.616e-03, size: 480, ETA: 1:21:16
2025-07-30 07:59:51.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 94/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.614e-03, size: 416, ETA: 1:21:12
2025-07-30 07:59:53.018 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:00:00.159 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:00:03.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:00:04.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3205
2025-07-30 08:00:05.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3077
2025-07-30 08:00:05.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0946
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2409
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.095
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.241
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:00:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:00:05.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:00:05.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:00:05.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:00:05.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:00:05.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:00:05.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:00:07.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:00:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:00:10.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:00:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:00:14.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:00:16.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:00:17.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:00:19.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:00:21.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:00:21.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-07-30 08:00:21.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-07-30 08:00:21.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:00:21.638 | 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

2025-07-30 08:00:21.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:00:21.747 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:00:21.863 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch95
2025-07-30 08:00:25.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.612e-03, size: 480, ETA: 1:21:06
2025-07-30 08:00:29.020 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.611e-03, size: 512, ETA: 1:21:02
2025-07-30 08:00:32.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.192s, 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: 1.610e-03, size: 512, ETA: 1:20:59
2025-07-30 08:00:36.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.608e-03, size: 352, ETA: 1:20:56
2025-07-30 08:00:40.226 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.607e-03, size: 288, ETA: 1:20:51
2025-07-30 08:00:43.837 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 95/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.606e-03, size: 288, ETA: 1:20:47
2025-07-30 08:00:45.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:00:52.297 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:00:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:00:56.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3849
2025-07-30 08:00:56.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3365
2025-07-30 08:00:56.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2054
2025-07-30 08:00:56.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3090
2025-07-30 08:00:56.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:00:56.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:00:56.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-07-30 08:00:56.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-07-30 08:00:56.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.205
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:00:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:00:58.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:00:59.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:01:01.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:01:03.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:01:04.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:01:06.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:01:08.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:01:09.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:01:11.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:01:11.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:01:11.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:01:11.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:01:11.698 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.94 ms, Average inference time: 8.44 ms

2025-07-30 08:01:11.699 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:01:11.866 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:01:12.069 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch96
2025-07-30 08:01:15.763 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.604e-03, size: 384, ETA: 1:20:42
2025-07-30 08:01:19.443 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.179s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.602e-03, size: 352, ETA: 1:20:38
2025-07-30 08:01:23.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.188s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.601e-03, size: 544, ETA: 1:20:35
2025-07-30 08:01:26.867 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.178s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.600e-03, size: 320, ETA: 1:20:31
2025-07-30 08:01:30.452 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.598e-03, size: 480, ETA: 1:20:27
2025-07-30 08:01:34.248 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 96/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.185s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.597e-03, size: 512, ETA: 1:20:23
2025-07-30 08:01:35.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:01:42.855 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:01:45.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:01:46.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3782
2025-07-30 08:01:47.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2321
2025-07-30 08:01:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1621
2025-07-30 08:01:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2575
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.162
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.257
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:01:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:01:47.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:01:47.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:01:47.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:01:47.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:01:47.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:01:49.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:01:50.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:01:52.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:01:54.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:01:56.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:01:58.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:02:00.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:02:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:02:04.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:02:04.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:02:04.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 08:02:04.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:02:04.287 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.45 ms, Average NMS time: 0.93 ms, Average inference time: 8.39 ms

2025-07-30 08:02:04.288 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:02:04.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:02:04.456 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch97
2025-07-30 08:02:08.069 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.595e-03, size: 352, ETA: 1:20:18
2025-07-30 08:02:12.002 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.193s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.594e-03, size: 544, ETA: 1:20:15
2025-07-30 08:02:15.846 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.187s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.592e-03, size: 448, ETA: 1:20:11
2025-07-30 08:02:19.554 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.591e-03, size: 384, ETA: 1:20:07
2025-07-30 08:02:23.363 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.188s, 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: 1.590e-03, size: 384, ETA: 1:20:04
2025-07-30 08:02:27.080 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 97/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.588e-03, size: 320, ETA: 1:20:00
2025-07-30 08:02:28.724 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:02:35.444 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:02:37.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:02:38.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3084
2025-07-30 08:02:38.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3536
2025-07-30 08:02:38.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1133
2025-07-30 08:02:38.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2584
2025-07-30 08:02:38.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:02:38.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.113
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.258
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:02:38.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:02:38.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:02:39.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:02:41.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:02:42.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:02:43.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:02:45.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:02:46.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:02:47.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:02:49.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:02:50.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:02:50.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 08:02:50.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 08:02:50.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:02:50.656 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.91 ms, Average inference time: 8.37 ms

2025-07-30 08:02:50.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:02:50.766 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:02:50.882 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch98
2025-07-30 08:02:54.491 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.586e-03, size: 480, ETA: 1:19:54
2025-07-30 08:02:58.195 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.585e-03, size: 480, ETA: 1:19:51
2025-07-30 08:03:01.851 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.178s, 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: 1.584e-03, size: 256, ETA: 1:19:47
2025-07-30 08:03:05.633 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.185s, data_time: 0.003s, total_loss: 9.0, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 1.2, lr: 1.582e-03, size: 352, ETA: 1:19:43
2025-07-30 08:03:09.197 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.2, lr: 1.581e-03, size: 352, ETA: 1:19:39
2025-07-30 08:03:12.764 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 98/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, 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: 1.580e-03, size: 448, ETA: 1:19:35
2025-07-30 08:03:14.397 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:03:21.464 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:03:23.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:03:24.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3223
2025-07-30 08:03:25.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2881
2025-07-30 08:03:25.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1175
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2427
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.118
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.243
2025-07-30 08:03:25.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:03:25.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:03:26.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:03:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:03:29.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:03:31.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:03:33.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:03:34.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:03:36.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:03:38.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:03:39.649 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:03:39.650 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-07-30 08:03:39.650 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-07-30 08:03:39.650 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:03:39.674 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.94 ms, Average inference time: 8.44 ms

2025-07-30 08:03:39.675 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:03:39.751 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:03:39.832 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch99
2025-07-30 08:03:43.362 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, 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: 1.578e-03, size: 576, ETA: 1:19:29
2025-07-30 08:03:47.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.180s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.576e-03, size: 416, ETA: 1:19:25
2025-07-30 08:03:50.834 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.183s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.575e-03, size: 448, ETA: 1:19:22
2025-07-30 08:03:54.498 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.573e-03, size: 576, ETA: 1:19:18
2025-07-30 08:03:58.343 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.189s, 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: 1.572e-03, size: 320, ETA: 1:19:14
2025-07-30 08:04:02.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 99/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.182s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.571e-03, size: 416, ETA: 1:19:11
2025-07-30 08:04:03.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:04:10.609 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:04:12.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:04:12.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3697
2025-07-30 08:04:12.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2453
2025-07-30 08:04:13.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1446
2025-07-30 08:04:13.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2532
2025-07-30 08:04:13.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:04:13.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.245
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.145
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.253
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:04:13.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:04:13.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:04:13.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:04:14.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:04:15.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:04:16.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:04:17.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:04:18.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:04:19.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:04:20.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:04:21.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:04:22.409 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:04:22.409 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 08:04:22.409 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-07-30 08:04:22.409 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:04:22.418 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.92 ms, Average inference time: 8.33 ms

2025-07-30 08:04:22.424 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:04:22.557 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:04:22.633 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch100
2025-07-30 08:04:22.633 | INFO     | yolox_microbt.core.trainer:before_epoch:208 - --->No mosaic aug now!
2025-07-30 08:04:22.634 | INFO     | yolox_microbt.core.trainer:before_epoch:210 - --->Add additional L1 loss now!
2025-07-30 08:04:22.634 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:04:26.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.569e-03, size: 256, ETA: 1:19:04
2025-07-30 08:04:29.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.567e-03, size: 256, ETA: 1:18:59
2025-07-30 08:04:32.201 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 1.1, lr: 1.566e-03, size: 480, ETA: 1:18:54
2025-07-30 08:04:35.506 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.7, lr: 1.565e-03, size: 256, ETA: 1:18:50
2025-07-30 08:04:38.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.6, lr: 1.563e-03, size: 480, ETA: 1:18:45
2025-07-30 08:04:41.939 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 100/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.8, lr: 1.562e-03, size: 256, ETA: 1:18:40
2025-07-30 08:04:43.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:04:50.114 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:04:51.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:04:52.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3324
2025-07-30 08:04:53.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2465
2025-07-30 08:04:53.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0883
2025-07-30 08:04:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2224
2025-07-30 08:04:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:04:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:04:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-07-30 08:04:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.247
2025-07-30 08:04:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.088
2025-07-30 08:04:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.222
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:04:53.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:04:53.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:04:54.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:04:55.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:04:57.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:04:58.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:04:59.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:05:00.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:05:02.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:05:03.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:05:04.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:05:04.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:05:04.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-07-30 08:05:04.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:05:04.720 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.91 ms, Average inference time: 8.24 ms

2025-07-30 08:05:04.725 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:05:04.798 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:05:04.877 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch101
2025-07-30 08:05:08.143 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 11.2, iou_loss: 3.7, l1_loss: 1.3, conf_loss: 4.6, cls_loss: 1.5, lr: 1.560e-03, size: 320, ETA: 1:18:33
2025-07-30 08:05:11.151 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 1.558e-03, size: 352, ETA: 1:18:28
2025-07-30 08:05:14.460 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, 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: 1.557e-03, size: 416, ETA: 1:18:24
2025-07-30 08:05:17.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.556e-03, size: 480, ETA: 1:18:19
2025-07-30 08:05:21.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 1.554e-03, size: 352, ETA: 1:18:15
2025-07-30 08:05:24.343 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 101/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 1.0, lr: 1.553e-03, size: 512, ETA: 1:18:10
2025-07-30 08:05:25.821 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:05:32.560 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:05:33.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:05:33.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3502
2025-07-30 08:05:33.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2735
2025-07-30 08:05:33.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1293
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2510
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.273
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.129
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.251
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:05:33.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:05:33.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:05:33.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:05:33.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:05:33.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:05:33.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:05:33.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:05:34.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:05:34.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:05:35.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:05:35.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:05:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:05:36.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:05:37.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:05:37.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:05:37.986 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:05:37.986 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 08:05:37.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-07-30 08:05:37.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:05:38.000 | 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

2025-07-30 08:05:38.001 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:05:38.108 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:05:38.210 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch102
2025-07-30 08:05:41.295 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, 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: 1.551e-03, size: 288, ETA: 1:18:03
2025-07-30 08:05:44.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.9, lr: 1.549e-03, size: 320, ETA: 1:17:59
2025-07-30 08:05:47.951 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.548e-03, size: 288, ETA: 1:17:55
2025-07-30 08:05:51.284 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.547e-03, size: 288, ETA: 1:17:50
2025-07-30 08:05:54.584 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.545e-03, size: 480, ETA: 1:17:46
2025-07-30 08:05:57.977 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 102/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 1.0, lr: 1.544e-03, size: 352, ETA: 1:17:41
2025-07-30 08:05:59.348 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:06:06.099 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:06:07.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:06:07.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3893
2025-07-30 08:06:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3426
2025-07-30 08:06:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2086
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3135
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.209
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-07-30 08:06:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:06:08.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:06:08.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:06:09.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:06:10.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:06:11.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:06:12.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:06:13.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:06:13.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:06:14.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:06:15.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:06:15.670 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:06:15.670 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:06:15.670 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:06:15.678 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.91 ms, Average inference time: 8.35 ms

2025-07-30 08:06:15.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:06:15.802 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:06:15.881 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch103
2025-07-30 08:06:19.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 3.2, cls_loss: 0.7, lr: 1.542e-03, size: 512, ETA: 1:17:35
2025-07-30 08:06:22.406 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.540e-03, size: 416, ETA: 1:17:30
2025-07-30 08:06:25.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.8, lr: 1.539e-03, size: 288, ETA: 1:17:26
2025-07-30 08:06:29.072 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 1.0, lr: 1.537e-03, size: 416, ETA: 1:17:21
2025-07-30 08:06:32.475 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.8, lr: 1.536e-03, size: 480, ETA: 1:17:17
2025-07-30 08:06:35.768 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 103/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 1.535e-03, size: 256, ETA: 1:17:13
2025-07-30 08:06:37.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:06:44.311 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:06:44.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:06:45.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4632
2025-07-30 08:06:45.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3909
2025-07-30 08:06:45.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2534
2025-07-30 08:06:45.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3692
2025-07-30 08:06:45.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:06:45.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.253
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:06:45.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:06:45.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:06:45.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:06:45.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:06:46.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:06:46.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:06:47.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:06:47.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:06:48.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:06:48.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:06:49.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:06:49.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:06:49.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:06:49.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 08:06:49.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:06:49.486 | 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

2025-07-30 08:06:49.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:06:49.566 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:06:49.666 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch104
2025-07-30 08:06:52.880 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 1.533e-03, size: 320, ETA: 1:17:06
2025-07-30 08:06:56.336 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.531e-03, size: 416, ETA: 1:17:02
2025-07-30 08:06:59.638 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 9.3, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 4.2, cls_loss: 0.8, lr: 1.530e-03, size: 512, ETA: 1:16:57
2025-07-30 08:07:02.949 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.8, lr: 1.528e-03, size: 288, ETA: 1:16:53
2025-07-30 08:07:06.400 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 1.527e-03, size: 448, ETA: 1:16:49
2025-07-30 08:07:09.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 104/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.525e-03, size: 448, ETA: 1:16:45
2025-07-30 08:07:11.287 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:07:18.242 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:07:19.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:07:19.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3559
2025-07-30 08:07:19.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2533
2025-07-30 08:07:19.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1431
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2507
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.253
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.143
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.251
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:07:19.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:07:19.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:07:19.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:07:19.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:07:19.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:07:19.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:07:19.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:07:20.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:07:21.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:07:21.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:07:22.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:07:23.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:07:23.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:07:24.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:07:25.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:07:26.041 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:07:26.041 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 08:07:26.041 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-07-30 08:07:26.041 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:07:26.049 | 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

2025-07-30 08:07:26.050 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:07:26.123 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:07:26.199 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch105
2025-07-30 08:07:29.246 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.3, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 5.1, cls_loss: 1.2, lr: 1.523e-03, size: 256, ETA: 1:16:38
2025-07-30 08:07:32.457 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 10.4, iou_loss: 3.1, l1_loss: 1.6, conf_loss: 4.7, cls_loss: 0.9, lr: 1.522e-03, size: 576, ETA: 1:16:33
2025-07-30 08:07:35.631 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.521e-03, size: 256, ETA: 1:16:28
2025-07-30 08:07:38.971 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 1.519e-03, size: 544, ETA: 1:16:24
2025-07-30 08:07:42.163 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.518e-03, size: 320, ETA: 1:16:20
2025-07-30 08:07:45.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 105/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.516e-03, size: 448, ETA: 1:16:15
2025-07-30 08:07:46.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:07:53.795 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:07:54.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:07:55.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4611
2025-07-30 08:07:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3889
2025-07-30 08:07:55.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2269
2025-07-30 08:07:55.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3589
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:07:55.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:07:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:07:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:07:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:07:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:07:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:07:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:07:56.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:07:56.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:07:57.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:07:58.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:07:58.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:07:59.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:08:00.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:08:00.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:08:01.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:08:01.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:08:01.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:08:01.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:08:01.663 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.88 ms, Average inference time: 8.22 ms

2025-07-30 08:08:01.664 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:08:01.737 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:08:01.815 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch106
2025-07-30 08:08:05.178 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.514e-03, size: 256, ETA: 1:16:09
2025-07-30 08:08:08.599 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 0.7, lr: 1.513e-03, size: 384, ETA: 1:16:04
2025-07-30 08:08:11.870 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 1.511e-03, size: 544, ETA: 1:16:00
2025-07-30 08:08:15.364 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.510e-03, size: 576, ETA: 1:15:56
2025-07-30 08:08:18.682 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.8, lr: 1.508e-03, size: 256, ETA: 1:15:52
2025-07-30 08:08:21.871 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 106/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.507e-03, size: 256, ETA: 1:15:47
2025-07-30 08:08:23.249 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:08:30.143 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:08:31.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:08:31.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4944
2025-07-30 08:08:31.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4058
2025-07-30 08:08:31.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2710
2025-07-30 08:08:31.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3904
2025-07-30 08:08:31.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:08:31.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:08:31.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-07-30 08:08:31.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-07-30 08:08:31.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-07-30 08:08:31.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.390
2025-07-30 08:08:31.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:08:31.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:08:31.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:08:31.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:08:31.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:08:31.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:08:31.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:08:31.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:08:31.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:08:32.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:08:33.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:08:34.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:08:35.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:08:35.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:08:36.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:08:37.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:08:38.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:08:39.189 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:08:39.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:08:39.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-07-30 08:08:39.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:08:39.198 | 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

2025-07-30 08:08:39.205 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:08:39.327 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:08:39.405 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch107
2025-07-30 08:08:42.617 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 1.505e-03, size: 384, ETA: 1:15:40
2025-07-30 08:08:45.880 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 0.7, lr: 1.503e-03, size: 352, ETA: 1:15:36
2025-07-30 08:08:49.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 3.9, cls_loss: 0.7, lr: 1.502e-03, size: 384, ETA: 1:15:31
2025-07-30 08:08:52.617 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 1.2, lr: 1.500e-03, size: 384, ETA: 1:15:27
2025-07-30 08:08:56.033 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.499e-03, size: 352, ETA: 1:15:23
2025-07-30 08:08:59.436 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 107/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.498e-03, size: 480, ETA: 1:15:19
2025-07-30 08:09:01.031 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:09:07.747 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:09:09.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:09:09.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5018
2025-07-30 08:09:10.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4261
2025-07-30 08:09:10.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2440
2025-07-30 08:09:10.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3906
2025-07-30 08:09:10.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:09:10.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.391
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:09:10.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:09:10.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:09:10.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:09:10.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:09:11.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:09:12.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:09:13.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:09:14.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:09:15.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:09:16.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:09:17.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:09:18.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:09:19.700 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:09:19.700 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 08:09:19.700 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-07-30 08:09:19.700 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:09:19.708 | 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

2025-07-30 08:09:19.711 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:09:19.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:09:19.861 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch108
2025-07-30 08:09:23.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 0.6, lr: 1.495e-03, size: 416, ETA: 1:15:12
2025-07-30 08:09:26.400 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.8, cls_loss: 1.2, lr: 1.494e-03, size: 288, ETA: 1:15:08
2025-07-30 08:09:29.889 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, 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: 1.492e-03, size: 256, ETA: 1:15:04
2025-07-30 08:09:33.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 1.2, lr: 1.491e-03, size: 256, ETA: 1:15:00
2025-07-30 08:09:36.378 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.490e-03, size: 512, ETA: 1:14:55
2025-07-30 08:09:39.906 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 108/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 1.488e-03, size: 512, ETA: 1:14:51
2025-07-30 08:09:41.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:09:48.117 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:09:48.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:09:48.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3481
2025-07-30 08:09:49.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3036
2025-07-30 08:09:49.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1801
2025-07-30 08:09:49.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2772
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.180
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.277
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:09:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:09:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:09:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:09:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:09:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:09:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:09:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:09:49.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:09:49.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:09:50.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:09:50.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:09:51.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:09:51.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:09:51.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:09:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:09:52.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:09:52.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:09:52.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 08:09:52.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:09:52.764 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.85 ms, Average inference time: 8.45 ms

2025-07-30 08:09:52.765 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:09:52.843 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:09:52.917 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch109
2025-07-30 08:09:56.505 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.178s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 1.486e-03, size: 576, ETA: 1:14:45
2025-07-30 08:10:00.005 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.8, lr: 1.485e-03, size: 256, ETA: 1:14:41
2025-07-30 08:10:03.246 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.9, lr: 1.483e-03, size: 448, ETA: 1:14:37
2025-07-30 08:10:06.612 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.8, lr: 1.482e-03, size: 544, ETA: 1:14:32
2025-07-30 08:10:10.140 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.9, lr: 1.480e-03, size: 416, ETA: 1:14:29
2025-07-30 08:10:13.549 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 109/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.479e-03, size: 352, ETA: 1:14:24
2025-07-30 08:10:15.017 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:10:21.926 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:10:23.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:10:23.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4664
2025-07-30 08:10:23.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3703
2025-07-30 08:10:24.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2350
2025-07-30 08:10:24.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3573
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:10:24.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:10:24.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:10:24.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:10:24.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:10:24.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:10:24.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:10:24.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:10:25.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:10:26.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:10:27.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:10:28.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:10:29.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:10:30.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:10:31.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:10:32.403 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:10:32.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:10:32.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:10:32.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:10:32.412 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.91 ms, Average inference time: 8.45 ms

2025-07-30 08:10:32.414 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:10:32.486 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:10:32.564 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch110
2025-07-30 08:10:35.735 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.7, lr: 1.476e-03, size: 576, ETA: 1:14:18
2025-07-30 08:10:39.164 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.475e-03, size: 288, ETA: 1:14:14
2025-07-30 08:10:42.346 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.8, lr: 1.474e-03, size: 480, ETA: 1:14:09
2025-07-30 08:10:45.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.472e-03, size: 352, ETA: 1:14:05
2025-07-30 08:10:49.163 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.9, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 4.0, cls_loss: 1.0, lr: 1.471e-03, size: 448, ETA: 1:14:01
2025-07-30 08:10:52.547 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 110/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.9, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.8, lr: 1.469e-03, size: 320, ETA: 1:13:57
2025-07-30 08:10:53.914 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:11:00.714 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:11:02.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:11:03.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4166
2025-07-30 08:11:03.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3334
2025-07-30 08:11:03.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1667
2025-07-30 08:11:03.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3056
2025-07-30 08:11:03.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:11:03.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:11:03.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-07-30 08:11:03.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-07-30 08:11:03.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.167
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:11:03.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:11:03.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:11:04.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:11:06.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:11:07.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:11:08.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:11:09.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:11:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:11:11.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:11:13.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:11:14.204 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:11:14.204 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:11:14.205 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:11:14.205 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:11:14.215 | 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: 8.45 ms

2025-07-30 08:11:14.216 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:11:14.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:11:14.376 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch111
2025-07-30 08:11:17.544 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 1.467e-03, size: 352, ETA: 1:13:50
2025-07-30 08:11:20.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 0.7, lr: 1.465e-03, size: 288, ETA: 1:13:46
2025-07-30 08:11:24.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 1.464e-03, size: 256, ETA: 1:13:41
2025-07-30 08:11:27.359 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 1.462e-03, size: 416, ETA: 1:13:37
2025-07-30 08:11:30.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.8, lr: 1.461e-03, size: 480, ETA: 1:13:33
2025-07-30 08:11:33.927 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 111/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, 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: 1.459e-03, size: 576, ETA: 1:13:28
2025-07-30 08:11:35.405 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:11:42.110 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:11:42.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:11:43.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4761
2025-07-30 08:11:43.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4098
2025-07-30 08:11:43.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2411
2025-07-30 08:11:43.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3757
2025-07-30 08:11:43.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:11:43.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:11:43.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-07-30 08:11:43.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-07-30 08:11:43.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.241
2025-07-30 08:11:43.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:11:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:11:43.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:11:44.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:11:44.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:11:45.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:11:46.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:11:46.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:11:47.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:11:47.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:11:48.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:11:48.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:11:48.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 08:11:48.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:11:48.464 | 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

2025-07-30 08:11:48.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:11:48.555 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:11:48.629 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch112
2025-07-30 08:11:51.940 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 3.6, cls_loss: 0.7, lr: 1.457e-03, size: 448, ETA: 1:13:22
2025-07-30 08:11:55.176 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.9, lr: 1.456e-03, size: 544, ETA: 1:13:18
2025-07-30 08:11:58.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 4.7, cls_loss: 0.6, lr: 1.454e-03, size: 320, ETA: 1:13:13
2025-07-30 08:12:01.754 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.453e-03, size: 352, ETA: 1:13:09
2025-07-30 08:12:05.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.451e-03, size: 416, ETA: 1:13:05
2025-07-30 08:12:08.292 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 112/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.450e-03, size: 480, ETA: 1:13:00
2025-07-30 08:12:09.756 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:12:16.795 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:12:17.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:12:18.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3649
2025-07-30 08:12:18.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3883
2025-07-30 08:12:18.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1915
2025-07-30 08:12:18.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3149
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.315
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:12:18.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:12:18.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:12:18.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:12:18.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:12:18.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:12:18.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:12:19.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:12:19.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:12:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:12:21.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:12:21.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:12:22.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:12:23.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:12:23.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:12:24.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:12:24.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:12:24.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:12:24.593 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:12:24.606 | 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: 8.35 ms

2025-07-30 08:12:24.607 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:12:24.686 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:12:24.762 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch113
2025-07-30 08:12:28.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 1.448e-03, size: 480, ETA: 1:12:54
2025-07-30 08:12:31.410 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 1.0, lr: 1.446e-03, size: 288, ETA: 1:12:50
2025-07-30 08:12:34.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 1.445e-03, size: 384, ETA: 1:12:45
2025-07-30 08:12:37.844 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.443e-03, size: 544, ETA: 1:12:41
2025-07-30 08:12:41.165 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.442e-03, size: 352, ETA: 1:12:37
2025-07-30 08:12:44.504 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 113/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 1.5, lr: 1.440e-03, size: 320, ETA: 1:12:32
2025-07-30 08:12:45.936 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:12:53.030 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:12:54.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:12:54.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3433
2025-07-30 08:12:54.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3173
2025-07-30 08:12:54.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1692
2025-07-30 08:12:54.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2766
2025-07-30 08:12:54.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:12:54.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:12:54.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.317
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.277
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:12:54.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:12:54.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:12:54.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:12:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:12:56.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:12:56.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:12:57.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:12:58.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:12:58.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:12:59.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:13:00.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:13:01.045 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:13:01.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 08:13:01.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 08:13:01.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:13:01.054 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.66 ms, Average NMS time: 0.90 ms, Average inference time: 8.55 ms

2025-07-30 08:13:01.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:13:01.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:13:01.206 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch114
2025-07-30 08:13:04.204 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.438e-03, size: 288, ETA: 1:12:26
2025-07-30 08:13:07.593 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.436e-03, size: 288, ETA: 1:12:22
2025-07-30 08:13:10.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.435e-03, size: 320, ETA: 1:12:18
2025-07-30 08:13:14.384 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 10.6, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 5.3, cls_loss: 0.9, lr: 1.433e-03, size: 448, ETA: 1:12:14
2025-07-30 08:13:17.574 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.432e-03, size: 416, ETA: 1:12:09
2025-07-30 08:13:20.951 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 114/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.7, lr: 1.430e-03, size: 448, ETA: 1:12:05
2025-07-30 08:13:22.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:13:29.380 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:13:31.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:13:31.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4172
2025-07-30 08:13:32.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2682
2025-07-30 08:13:32.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1740
2025-07-30 08:13:32.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2865
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.174
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.286
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:13:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:13:32.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:13:32.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:13:32.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:13:32.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:13:32.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:13:32.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:13:32.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:13:33.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:13:35.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:13:36.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:13:37.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:13:39.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:13:40.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:13:41.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:13:42.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:13:44.280 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:13:44.281 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:13:44.281 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:13:44.281 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:13:44.289 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.44 ms, Average NMS time: 0.90 ms, Average inference time: 8.34 ms

2025-07-30 08:13:44.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:13:44.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:13:44.450 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch115
2025-07-30 08:13:47.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 9.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 4.8, cls_loss: 0.8, lr: 1.428e-03, size: 544, ETA: 1:11:59
2025-07-30 08:13:51.050 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.7, lr: 1.427e-03, size: 576, ETA: 1:11:54
2025-07-30 08:13:54.522 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 1.1, lr: 1.425e-03, size: 544, ETA: 1:11:50
2025-07-30 08:13:58.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.177s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.424e-03, size: 480, ETA: 1:11:47
2025-07-30 08:14:01.386 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 1.1, lr: 1.422e-03, size: 384, ETA: 1:11:43
2025-07-30 08:14:04.583 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 115/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 1.4, lr: 1.421e-03, size: 288, ETA: 1:11:38
2025-07-30 08:14:05.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:14:12.723 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:14:14.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:14:14.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4625
2025-07-30 08:14:15.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4214
2025-07-30 08:14:15.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1940
2025-07-30 08:14:15.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3593
2025-07-30 08:14:15.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:14:15.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.194
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:14:15.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:14:15.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:14:15.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:14:16.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:14:17.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:14:18.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:14:19.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:14:20.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:14:21.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:14:22.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:14:23.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:14:24.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:14:24.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:14:24.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:14:24.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:14:24.653 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.35 ms, Average NMS time: 0.90 ms, Average inference time: 8.24 ms

2025-07-30 08:14:24.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:14:24.730 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:14:24.855 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch116
2025-07-30 08:14:28.035 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.6, lr: 1.418e-03, size: 448, ETA: 1:11:32
2025-07-30 08:14:31.392 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 1.1, lr: 1.417e-03, size: 544, ETA: 1:11:28
2025-07-30 08:14:34.796 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 1.0, lr: 1.415e-03, size: 256, ETA: 1:11:24
2025-07-30 08:14:38.226 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.414e-03, size: 480, ETA: 1:11:19
2025-07-30 08:14:41.611 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 1.412e-03, size: 416, ETA: 1:11:15
2025-07-30 08:14:45.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 116/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.9, cls_loss: 0.7, lr: 1.411e-03, size: 352, ETA: 1:11:11
2025-07-30 08:14:46.509 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:14:53.227 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:14:54.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:14:55.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3895
2025-07-30 08:14:55.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3511
2025-07-30 08:14:55.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1544
2025-07-30 08:14:55.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2984
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.154
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.298
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:14:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:14:55.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:14:55.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:14:55.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:14:55.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:14:55.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:14:55.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:14:56.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:14:57.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:14:58.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:14:59.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:15:00.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:15:01.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:15:02.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:15:03.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:15:04.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:15:04.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:15:04.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 08:15:04.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:15:04.931 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.63 ms, Average NMS time: 0.90 ms, Average inference time: 8.53 ms

2025-07-30 08:15:04.934 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:15:05.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:15:05.089 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch117
2025-07-30 08:15:08.175 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.0, iou_loss: 1.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.409e-03, size: 512, ETA: 1:11:05
2025-07-30 08:15:11.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.7, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 3.5, cls_loss: 0.6, lr: 1.407e-03, size: 448, ETA: 1:11:01
2025-07-30 08:15:14.809 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 1.2, lr: 1.405e-03, size: 384, ETA: 1:10:56
2025-07-30 08:15:18.160 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.404e-03, size: 256, ETA: 1:10:52
2025-07-30 08:15:21.415 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.402e-03, size: 544, ETA: 1:10:48
2025-07-30 08:15:24.830 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 117/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.401e-03, size: 544, ETA: 1:10:44
2025-07-30 08:15:26.316 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:15:33.085 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:15:33.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:15:33.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4362
2025-07-30 08:15:33.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3501
2025-07-30 08:15:34.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1804
2025-07-30 08:15:34.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3222
2025-07-30 08:15:34.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:15:34.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:15:34.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-07-30 08:15:34.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-07-30 08:15:34.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.180
2025-07-30 08:15:34.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-07-30 08:15:34.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:15:34.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:15:34.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:15:34.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:15:34.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:15:34.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:15:34.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:15:34.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:15:34.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:15:34.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:15:34.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:15:35.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:15:35.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:15:36.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:15:36.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:15:36.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:15:37.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:15:37.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 08:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:15:37.644 | 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

2025-07-30 08:15:37.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:15:37.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:15:37.796 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch118
2025-07-30 08:15:41.128 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.8, lr: 1.399e-03, size: 352, ETA: 1:10:38
2025-07-30 08:15:44.401 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 3.1, cls_loss: 0.6, lr: 1.397e-03, size: 256, ETA: 1:10:34
2025-07-30 08:15:47.651 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.005s, total_loss: 8.6, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.8, cls_loss: 0.8, lr: 1.396e-03, size: 480, ETA: 1:10:29
2025-07-30 08:15:51.002 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, 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: 1.394e-03, size: 544, ETA: 1:10:25
2025-07-30 08:15:54.502 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 1.0, lr: 1.392e-03, size: 352, ETA: 1:10:21
2025-07-30 08:15:57.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 118/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.391e-03, size: 512, ETA: 1:10:17
2025-07-30 08:15:59.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:16:06.010 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:16:06.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:16:07.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4029
2025-07-30 08:16:07.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3915
2025-07-30 08:16:07.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1647
2025-07-30 08:16:07.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3197
2025-07-30 08:16:07.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:16:07.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:16:07.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.165
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.320
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:16:07.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:16:07.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:16:07.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:16:08.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:16:08.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:16:09.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:16:09.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:16:09.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:16:10.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:16:10.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:16:11.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:16:11.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:16:11.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 08:16:11.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:16:11.381 | 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

2025-07-30 08:16:11.382 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:16:11.512 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:16:11.593 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch119
2025-07-30 08:16:14.642 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.389e-03, size: 544, ETA: 1:10:11
2025-07-30 08:16:17.946 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.387e-03, size: 544, ETA: 1:10:07
2025-07-30 08:16:21.285 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.386e-03, size: 416, ETA: 1:10:02
2025-07-30 08:16:24.503 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.384e-03, size: 320, ETA: 1:09:58
2025-07-30 08:16:27.649 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 1.382e-03, size: 320, ETA: 1:09:54
2025-07-30 08:16:30.739 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 119/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 1.1, lr: 1.381e-03, size: 352, ETA: 1:09:49
2025-07-30 08:16:32.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:16:39.083 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:16:39.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:16:40.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4225
2025-07-30 08:16:40.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3184
2025-07-30 08:16:40.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1464
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2958
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.146
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.296
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:16:40.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:16:40.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:16:40.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:16:40.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:16:40.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:16:40.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:16:40.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:16:40.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:16:41.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:16:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:16:42.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:16:43.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:16:43.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:16:44.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:16:44.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:16:45.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:16:46.082 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:16:46.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:16:46.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 08:16:46.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:16:46.090 | 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

2025-07-30 08:16:46.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:16:46.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:16:46.241 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch120
2025-07-30 08:16:49.427 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.379e-03, size: 544, ETA: 1:09:43
2025-07-30 08:16:52.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, 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: 1.377e-03, size: 416, ETA: 1:09:39
2025-07-30 08:16:56.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.8, lr: 1.376e-03, size: 352, ETA: 1:09:35
2025-07-30 08:16:59.563 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.6, lr: 1.374e-03, size: 512, ETA: 1:09:31
2025-07-30 08:17:03.101 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 9.3, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 4.6, cls_loss: 0.7, lr: 1.372e-03, size: 512, ETA: 1:09:27
2025-07-30 08:17:06.329 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 120/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 1.1, lr: 1.371e-03, size: 352, ETA: 1:09:23
2025-07-30 08:17:07.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:17:14.590 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:17:15.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:17:15.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3245
2025-07-30 08:17:15.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3349
2025-07-30 08:17:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1666
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2753
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.325
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.335
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.167
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.275
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:17:16.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:17:16.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:17:16.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:17:16.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:17:16.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:17:16.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:17:16.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:17:16.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:17:17.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:17:17.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:17:18.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:17:18.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:17:19.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:17:19.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:17:20.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:17:21.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:17:21.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:17:21.006 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 08:17:21.006 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:17:21.014 | 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

2025-07-30 08:17:21.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:17:21.091 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:17:21.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch121
2025-07-30 08:17:24.473 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, 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: 1.369e-03, size: 512, ETA: 1:09:17
2025-07-30 08:17:27.718 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.367e-03, size: 416, ETA: 1:09:13
2025-07-30 08:17:30.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.9, lr: 1.365e-03, size: 256, ETA: 1:09:08
2025-07-30 08:17:34.115 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.364e-03, size: 480, ETA: 1:09:04
2025-07-30 08:17:37.445 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.362e-03, size: 480, ETA: 1:09:00
2025-07-30 08:17:40.834 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 121/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 1.2, lr: 1.361e-03, size: 352, ETA: 1:08:56
2025-07-30 08:17:42.411 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:17:49.168 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:17:50.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:17:50.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3935
2025-07-30 08:17:50.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3118
2025-07-30 08:17:50.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1566
2025-07-30 08:17:50.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2873
2025-07-30 08:17:50.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.157
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:17:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:17:50.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:17:50.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:17:50.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:17:50.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:17:50.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:17:51.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:17:52.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:17:53.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:17:53.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:17:54.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:17:55.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:17:55.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:17:56.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:17:57.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:17:57.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:17:57.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:17:57.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:17:57.182 | 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

2025-07-30 08:17:57.183 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:17:57.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:17:57.375 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch122
2025-07-30 08:18:00.485 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 1.358e-03, size: 416, ETA: 1:08:50
2025-07-30 08:18:04.029 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.357e-03, size: 416, ETA: 1:08:46
2025-07-30 08:18:07.397 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 1.355e-03, size: 576, ETA: 1:08:42
2025-07-30 08:18:10.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 1.354e-03, size: 384, ETA: 1:08:38
2025-07-30 08:18:13.822 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.352e-03, size: 288, ETA: 1:08:34
2025-07-30 08:18:17.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 122/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.351e-03, size: 480, ETA: 1:08:29
2025-07-30 08:18:18.691 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:18:25.555 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:18:26.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:18:27.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4379
2025-07-30 08:18:27.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3041
2025-07-30 08:18:27.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2070
2025-07-30 08:18:27.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3164
2025-07-30 08:18:27.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:18:27.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.316
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:18:27.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:18:27.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:18:28.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:18:29.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:18:30.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:18:31.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:18:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:18:32.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:18:33.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:18:34.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:18:35.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:18:35.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:18:35.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 08:18:35.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:18:35.379 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.76 ms, Average NMS time: 0.87 ms, Average inference time: 8.63 ms

2025-07-30 08:18:35.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:18:35.502 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:18:35.579 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch123
2025-07-30 08:18:38.793 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, 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: 1.348e-03, size: 256, ETA: 1:08:23
2025-07-30 08:18:41.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.347e-03, size: 256, ETA: 1:08:19
2025-07-30 08:18:45.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 1.345e-03, size: 256, ETA: 1:08:15
2025-07-30 08:18:48.390 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.8, lr: 1.344e-03, size: 512, ETA: 1:08:11
2025-07-30 08:18:51.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, 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: 1.342e-03, size: 416, ETA: 1:08:07
2025-07-30 08:18:55.062 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 123/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 1.340e-03, size: 256, ETA: 1:08:03
2025-07-30 08:18:56.524 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:19:03.215 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:19:03.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:19:04.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2465
2025-07-30 08:19:04.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2030
2025-07-30 08:19:04.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1005
2025-07-30 08:19:04.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1833
2025-07-30 08:19:04.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.247
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.100
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.183
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:19:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:19:04.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:19:04.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:19:04.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:19:04.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:19:04.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:19:04.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:19:05.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:19:05.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:19:06.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:19:06.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:19:06.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:19:07.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:19:07.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:19:07.689 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.09
2025-07-30 08:19:07.689 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.18
2025-07-30 08:19:07.689 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:19:07.695 | 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

2025-07-30 08:19:07.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:19:07.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:19:07.921 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch124
2025-07-30 08:19:11.285 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.338e-03, size: 416, ETA: 1:07:57
2025-07-30 08:19:14.948 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.176s, data_time: 0.004s, total_loss: 7.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 1.1, lr: 1.337e-03, size: 512, ETA: 1:07:53
2025-07-30 08:19:18.424 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 1.335e-03, size: 320, ETA: 1:07:49
2025-07-30 08:19:21.833 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.333e-03, size: 512, ETA: 1:07:45
2025-07-30 08:19:25.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 3.0, cls_loss: 1.1, lr: 1.332e-03, size: 256, ETA: 1:07:41
2025-07-30 08:19:28.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 124/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.330e-03, size: 352, ETA: 1:07:37
2025-07-30 08:19:30.035 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:19:36.914 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:19:37.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:19:37.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4393
2025-07-30 08:19:38.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2904
2025-07-30 08:19:38.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1886
2025-07-30 08:19:38.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3061
2025-07-30 08:19:38.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:19:38.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.189
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:19:38.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:19:38.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:19:38.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:19:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:19:39.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:19:39.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:19:40.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:19:40.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:19:41.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:19:41.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:19:42.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:19:42.732 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:19:42.732 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:19:42.732 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:19:42.732 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:19:42.740 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.85 ms, Average inference time: 8.42 ms

2025-07-30 08:19:42.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:19:42.818 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:19:42.894 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch125
2025-07-30 08:19:46.206 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.0, l1_loss: 0.8, conf_loss: 3.6, cls_loss: 0.7, lr: 1.328e-03, size: 384, ETA: 1:07:31
2025-07-30 08:19:49.332 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.326e-03, size: 256, ETA: 1:07:27
2025-07-30 08:19:52.934 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.6, lr: 1.325e-03, size: 448, ETA: 1:07:23
2025-07-30 08:19:56.372 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.8, lr: 1.323e-03, size: 416, ETA: 1:07:19
2025-07-30 08:19:59.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 1.322e-03, size: 416, ETA: 1:07:16
2025-07-30 08:20:03.016 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 125/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 0.8, lr: 1.320e-03, size: 576, ETA: 1:07:11
2025-07-30 08:20:04.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:20:11.547 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:20:12.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:20:12.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3693
2025-07-30 08:20:12.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3387
2025-07-30 08:20:12.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1842
2025-07-30 08:20:12.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2974
2025-07-30 08:20:12.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:20:12.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.297
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:20:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:20:12.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:20:12.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:20:12.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:20:13.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:20:13.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:20:14.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:20:14.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:20:15.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:20:15.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:20:16.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:20:16.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:20:17.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:20:17.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:20:17.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 08:20:17.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:20:17.285 | 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: 8.43 ms

2025-07-30 08:20:17.286 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:20:17.358 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:20:17.433 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch126
2025-07-30 08:20:20.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 1.0, lr: 1.318e-03, size: 448, ETA: 1:07:06
2025-07-30 08:20:23.747 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.316e-03, size: 288, ETA: 1:07:01
2025-07-30 08:20:26.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 1.315e-03, size: 288, ETA: 1:06:57
2025-07-30 08:20:30.199 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.7, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 2.8, cls_loss: 0.6, lr: 1.313e-03, size: 288, ETA: 1:06:53
2025-07-30 08:20:33.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.311e-03, size: 256, ETA: 1:06:48
2025-07-30 08:20:36.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 126/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.310e-03, size: 256, ETA: 1:06:44
2025-07-30 08:20:38.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:20:45.004 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:20:45.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:20:46.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4195
2025-07-30 08:20:46.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3726
2025-07-30 08:20:46.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1597
2025-07-30 08:20:46.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3173
2025-07-30 08:20:46.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:20:46.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:20:46.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-07-30 08:20:46.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-07-30 08:20:46.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.160
2025-07-30 08:20:46.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.317
2025-07-30 08:20:46.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:20:46.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:20:46.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:20:46.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:20:46.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:20:46.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:20:46.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:20:46.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:20:46.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:20:47.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:20:47.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:20:48.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:20:49.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:20:49.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:20:50.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:20:51.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:20:51.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:20:52.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:20:52.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:20:52.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 08:20:52.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:20:52.473 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.27 ms, Average NMS time: 0.87 ms, Average inference time: 8.14 ms

2025-07-30 08:20:52.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:20:52.555 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:20:52.634 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch127
2025-07-30 08:20:56.081 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.307e-03, size: 448, ETA: 1:06:39
2025-07-30 08:20:59.561 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.306e-03, size: 576, ETA: 1:06:35
2025-07-30 08:21:03.066 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.6, lr: 1.304e-03, size: 576, ETA: 1:06:31
2025-07-30 08:21:06.393 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.303e-03, size: 416, ETA: 1:06:27
2025-07-30 08:21:09.728 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 9.0, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.7, lr: 1.301e-03, size: 256, ETA: 1:06:23
2025-07-30 08:21:13.050 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 127/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 1.299e-03, size: 544, ETA: 1:06:19
2025-07-30 08:21:14.568 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:21:21.298 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:21:21.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:21:21.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.1853
2025-07-30 08:21:21.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3452
2025-07-30 08:21:21.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1046
2025-07-30 08:21:21.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2117
2025-07-30 08:21:21.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:21:21.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.185
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.105
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.212
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:21:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:21:21.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:21:21.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:21:21.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:21:21.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:21:22.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:21:22.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:21:22.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:21:22.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:21:22.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:21:23.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:21:23.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:21:23.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:21:23.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 08:21:23.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.21
2025-07-30 08:21:23.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:21:23.369 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.70 ms, Average inference time: 8.20 ms

2025-07-30 08:21:23.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:21:23.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:21:23.519 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch128
2025-07-30 08:21:26.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 1.0, lr: 1.297e-03, size: 320, ETA: 1:06:13
2025-07-30 08:21:30.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 1.295e-03, size: 448, ETA: 1:06:09
2025-07-30 08:21:33.558 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 0.8, lr: 1.294e-03, size: 512, ETA: 1:06:05
2025-07-30 08:21:36.867 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.8, lr: 1.292e-03, size: 480, ETA: 1:06:01
2025-07-30 08:21:40.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 1.1, lr: 1.291e-03, size: 512, ETA: 1:05:57
2025-07-30 08:21:43.580 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 128/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 1.0, lr: 1.289e-03, size: 512, ETA: 1:05:53
2025-07-30 08:21:45.205 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:21:52.015 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:21:52.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:21:52.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3914
2025-07-30 08:21:53.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2926
2025-07-30 08:21:53.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1796
2025-07-30 08:21:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2879
2025-07-30 08:21:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:21:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:21:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-07-30 08:21:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-07-30 08:21:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.180
2025-07-30 08:21:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.288
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:21:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:21:53.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:21:53.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:21:54.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:21:54.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:21:54.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:21:55.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:21:56.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:21:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:21:57.134 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:21:57.134 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:21:57.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:21:57.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:21:57.142 | 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

2025-07-30 08:21:57.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:21:57.263 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:21:57.338 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch129
2025-07-30 08:22:00.492 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.6, conf_loss: 2.6, cls_loss: 0.7, lr: 1.287e-03, size: 576, ETA: 1:05:47
2025-07-30 08:22:03.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 1.285e-03, size: 256, ETA: 1:05:43
2025-07-30 08:22:07.049 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 1.284e-03, size: 352, ETA: 1:05:39
2025-07-30 08:22:10.282 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 1.282e-03, size: 512, ETA: 1:05:35
2025-07-30 08:22:13.709 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 1.4, lr: 1.280e-03, size: 448, ETA: 1:05:31
2025-07-30 08:22:16.963 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 129/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.9, lr: 1.279e-03, size: 384, ETA: 1:05:27
2025-07-30 08:22:18.385 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:22:25.163 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:22:26.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:22:26.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3531
2025-07-30 08:22:26.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3376
2025-07-30 08:22:26.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1791
2025-07-30 08:22:26.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2899
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.290
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:22:26.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:22:26.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:22:26.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:22:26.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:22:26.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:22:27.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:22:27.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:22:28.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:22:29.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:22:29.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:22:30.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:22:30.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:22:31.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:22:32.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:22:32.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:22:32.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:22:32.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:22:32.017 | 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

2025-07-30 08:22:32.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:22:32.092 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:22:32.169 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch130
2025-07-30 08:22:35.491 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 1.276e-03, size: 416, ETA: 1:05:21
2025-07-30 08:22:38.674 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 1.1, lr: 1.275e-03, size: 352, ETA: 1:05:17
2025-07-30 08:22:41.924 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, 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: 1.273e-03, size: 512, ETA: 1:05:13
2025-07-30 08:22:45.387 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 1.271e-03, size: 384, ETA: 1:05:09
2025-07-30 08:22:48.775 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 1.270e-03, size: 384, ETA: 1:05:05
2025-07-30 08:22:52.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 130/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, 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: 1.268e-03, size: 416, ETA: 1:05:01
2025-07-30 08:22:53.815 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:23:00.657 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:23:01.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:23:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3642
2025-07-30 08:23:01.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2972
2025-07-30 08:23:02.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1683
2025-07-30 08:23:02.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2766
2025-07-30 08:23:02.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:23:02.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:23:02.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-07-30 08:23:02.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-07-30 08:23:02.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.168
2025-07-30 08:23:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.277
2025-07-30 08:23:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:23:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:23:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:23:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:23:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:23:02.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:23:02.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:23:02.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:23:02.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:23:02.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:23:03.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:23:03.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:23:04.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:23:05.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:23:05.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:23:06.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:23:06.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:23:07.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:23:07.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:23:07.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 08:23:07.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:23:07.365 | 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

2025-07-30 08:23:07.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:23:07.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:23:07.524 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch131
2025-07-30 08:23:10.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.5, l1_loss: 1.6, conf_loss: 4.0, cls_loss: 0.8, lr: 1.266e-03, size: 576, ETA: 1:04:56
2025-07-30 08:23:14.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 1.264e-03, size: 352, ETA: 1:04:51
2025-07-30 08:23:17.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.004s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.263e-03, size: 416, ETA: 1:04:47
2025-07-30 08:23:20.604 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, 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: 1.261e-03, size: 384, ETA: 1:04:44
2025-07-30 08:23:24.057 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, 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: 1.259e-03, size: 512, ETA: 1:04:40
2025-07-30 08:23:27.306 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 131/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.258e-03, size: 480, ETA: 1:04:36
2025-07-30 08:23:28.730 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:23:35.718 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:23:36.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:23:37.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2306
2025-07-30 08:23:37.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2373
2025-07-30 08:23:37.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1378
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2019
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.237
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.138
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.202
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:23:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:23:37.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:23:37.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:23:37.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:23:37.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:23:37.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:23:37.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:23:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:23:39.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:23:40.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:23:40.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:23:41.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:23:42.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:23:43.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:23:43.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:23:44.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:23:44.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:23:44.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.20
2025-07-30 08:23:44.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:23:44.564 | 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

2025-07-30 08:23:44.565 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:23:44.637 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:23:44.714 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch132
2025-07-30 08:23:47.881 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.6, lr: 1.255e-03, size: 512, ETA: 1:04:30
2025-07-30 08:23:51.094 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.8, lr: 1.254e-03, size: 288, ETA: 1:04:26
2025-07-30 08:23:54.438 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.252e-03, size: 288, ETA: 1:04:22
2025-07-30 08:23:57.552 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.251e-03, size: 288, ETA: 1:04:17
2025-07-30 08:24:01.058 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 1.249e-03, size: 288, ETA: 1:04:14
2025-07-30 08:24:04.365 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 132/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.1, lr: 1.247e-03, size: 288, ETA: 1:04:10
2025-07-30 08:24:05.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:24:12.874 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:24:13.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:24:14.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3577
2025-07-30 08:24:14.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3022
2025-07-30 08:24:14.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1764
2025-07-30 08:24:14.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2788
2025-07-30 08:24:14.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:24:14.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:24:14.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-07-30 08:24:14.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-07-30 08:24:14.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.176
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.279
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:24:14.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:24:14.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:24:15.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:24:15.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:24:16.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:24:17.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:24:17.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:24:18.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:24:18.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:24:19.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:24:19.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:24:19.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 08:24:19.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:24:19.266 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.91 ms, Average inference time: 8.41 ms

2025-07-30 08:24:19.267 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:24:19.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:24:19.465 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch133
2025-07-30 08:24:22.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.9, cls_loss: 0.7, lr: 1.245e-03, size: 480, ETA: 1:04:04
2025-07-30 08:24:26.017 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 0.7, lr: 1.243e-03, size: 448, ETA: 1:04:00
2025-07-30 08:24:29.181 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 1.242e-03, size: 288, ETA: 1:03:56
2025-07-30 08:24:32.444 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.240e-03, size: 288, ETA: 1:03:52
2025-07-30 08:24:35.768 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 9.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 4.5, cls_loss: 0.9, lr: 1.238e-03, size: 416, ETA: 1:03:48
2025-07-30 08:24:39.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 133/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 1.237e-03, size: 480, ETA: 1:03:44
2025-07-30 08:24:40.860 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:24:47.821 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:24:48.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:24:49.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4676
2025-07-30 08:24:49.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3668
2025-07-30 08:24:49.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2305
2025-07-30 08:24:49.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3549
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.230
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.355
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:24:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:24:49.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:24:49.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:24:49.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:24:49.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:24:49.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:24:49.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:24:50.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:24:50.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:24:51.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:24:51.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:24:52.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:24:53.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:24:53.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:24:54.381 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:24:54.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:24:54.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 08:24:54.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:24:54.390 | 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

2025-07-30 08:24:54.391 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:24:54.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:24:54.539 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch134
2025-07-30 08:24:57.597 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 1.234e-03, size: 256, ETA: 1:03:38
2025-07-30 08:25:00.787 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 3.2, cls_loss: 0.8, lr: 1.233e-03, size: 512, ETA: 1:03:34
2025-07-30 08:25:04.113 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.231e-03, size: 544, ETA: 1:03:30
2025-07-30 08:25:07.480 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.230e-03, size: 256, ETA: 1:03:26
2025-07-30 08:25:10.627 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.228e-03, size: 288, ETA: 1:03:22
2025-07-30 08:25:13.712 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 134/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 10.0, iou_loss: 3.3, l1_loss: 1.5, conf_loss: 4.4, cls_loss: 0.9, lr: 1.226e-03, size: 448, ETA: 1:03:18
2025-07-30 08:25:15.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:25:21.978 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:25:22.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:25:23.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4590
2025-07-30 08:25:23.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4227
2025-07-30 08:25:23.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2297
2025-07-30 08:25:23.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3705
2025-07-30 08:25:23.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:25:23.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:25:23.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-07-30 08:25:23.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-07-30 08:25:23.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.230
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:25:23.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:25:23.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:25:24.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:25:24.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:25:25.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:25:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:25:26.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:25:27.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:25:27.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:25:28.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:25:28.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 08:25:28.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 08:25:28.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:25:28.253 | 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

2025-07-30 08:25:28.258 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:25:28.388 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:25:28.471 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch135
2025-07-30 08:25:31.799 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.7, conf_loss: 2.6, cls_loss: 1.2, lr: 1.224e-03, size: 448, ETA: 1:03:12
2025-07-30 08:25:34.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 4.6, cls_loss: 0.7, lr: 1.222e-03, size: 352, ETA: 1:03:08
2025-07-30 08:25:38.216 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, 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: 1.221e-03, size: 480, ETA: 1:03:04
2025-07-30 08:25:41.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.6, lr: 1.219e-03, size: 576, ETA: 1:03:00
2025-07-30 08:25:45.204 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 8.5, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 1.2, lr: 1.217e-03, size: 576, ETA: 1:02:57
2025-07-30 08:25:48.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 135/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 1.0, lr: 1.216e-03, size: 320, ETA: 1:02:53
2025-07-30 08:25:50.051 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:25:56.871 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:25:57.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:25:57.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4197
2025-07-30 08:25:57.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3753
2025-07-30 08:25:57.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1859
2025-07-30 08:25:57.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3270
2025-07-30 08:25:57.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:25:57.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:25:57.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-07-30 08:25:57.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-07-30 08:25:57.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-07-30 08:25:57.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.327
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:25:57.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:25:58.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:25:58.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:25:59.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:25:59.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:26:00.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:26:00.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:26:01.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:26:01.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:26:02.137 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:26:02.137 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:26:02.137 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 08:26:02.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:26:02.145 | 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

2025-07-30 08:26:02.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:26:02.265 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:26:02.343 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch136
2025-07-30 08:26:05.672 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, 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: 1.213e-03, size: 352, ETA: 1:02:47
2025-07-30 08:26:09.029 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.212e-03, size: 256, ETA: 1:02:43
2025-07-30 08:26:12.386 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.8, lr: 1.210e-03, size: 384, ETA: 1:02:39
2025-07-30 08:26:15.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 1.208e-03, size: 288, ETA: 1:02:35
2025-07-30 08:26:18.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.207e-03, size: 352, ETA: 1:02:31
2025-07-30 08:26:22.301 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 136/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.205e-03, size: 288, ETA: 1:02:27
2025-07-30 08:26:23.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:26:30.566 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:26:32.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:26:33.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4210
2025-07-30 08:26:33.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3598
2025-07-30 08:26:33.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2261
2025-07-30 08:26:33.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3356
2025-07-30 08:26:33.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:26:33.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:26:33.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-07-30 08:26:33.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-07-30 08:26:33.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.226
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:26:33.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:26:35.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:26:36.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:26:38.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:26:39.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:26:41.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:26:42.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:26:43.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:26:45.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:26:46.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:26:46.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:26:46.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:26:46.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:26:46.874 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.92 ms, Average inference time: 8.38 ms

2025-07-30 08:26:46.877 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:26:46.954 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:26:47.032 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch137
2025-07-30 08:26:50.394 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 1.0, lr: 1.203e-03, size: 512, ETA: 1:02:21
2025-07-30 08:26:53.878 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.9, lr: 1.201e-03, size: 384, ETA: 1:02:18
2025-07-30 08:26:57.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 4.5, cls_loss: 0.8, lr: 1.199e-03, size: 576, ETA: 1:02:14
2025-07-30 08:27:00.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 1.198e-03, size: 352, ETA: 1:02:10
2025-07-30 08:27:03.825 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.6, lr: 1.196e-03, size: 480, ETA: 1:02:06
2025-07-30 08:27:07.072 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 137/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 1.195e-03, size: 256, ETA: 1:02:02
2025-07-30 08:27:08.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:27:15.335 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:27:16.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:27:17.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2526
2025-07-30 08:27:17.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1816
2025-07-30 08:27:17.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1221
2025-07-30 08:27:17.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1854
2025-07-30 08:27:17.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.253
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.182
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.122
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.185
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:27:17.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:27:17.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:27:17.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:27:17.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:27:18.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:27:18.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:27:19.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:27:20.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:27:21.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:27:22.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:27:22.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:27:23.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:27:24.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:27:24.628 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-07-30 08:27:24.628 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.19
2025-07-30 08:27:24.628 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:27:24.639 | 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

2025-07-30 08:27:24.641 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:27:24.805 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:27:24.889 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch138
2025-07-30 08:27:28.074 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, 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: 1.192e-03, size: 256, ETA: 1:01:56
2025-07-30 08:27:31.457 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 1.190e-03, size: 352, ETA: 1:01:52
2025-07-30 08:27:34.951 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.189e-03, size: 576, ETA: 1:01:48
2025-07-30 08:27:38.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, 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: 1.187e-03, size: 320, ETA: 1:01:44
2025-07-30 08:27:41.503 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, 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: 1.186e-03, size: 544, ETA: 1:01:40
2025-07-30 08:27:44.900 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 138/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.184e-03, size: 544, ETA: 1:01:36
2025-07-30 08:27:46.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:27:53.237 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:27:53.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:27:54.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4077
2025-07-30 08:27:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3232
2025-07-30 08:27:54.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2437
2025-07-30 08:27:54.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3249
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.325
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:27:54.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:27:54.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:27:54.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:27:54.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:27:54.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:27:55.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:27:55.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:27:55.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:27:56.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:27:56.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:27:57.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:27:57.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:27:57.988 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:27:57.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:27:57.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 08:27:57.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:27:57.996 | 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

2025-07-30 08:27:57.996 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:27:58.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:27:58.144 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch139
2025-07-30 08:28:01.314 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 3.3, cls_loss: 0.7, lr: 1.181e-03, size: 320, ETA: 1:01:31
2025-07-30 08:28:04.461 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.180e-03, size: 416, ETA: 1:01:27
2025-07-30 08:28:07.879 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 10.1, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 4.4, cls_loss: 2.2, lr: 1.178e-03, size: 512, ETA: 1:01:23
2025-07-30 08:28:11.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 1.4, lr: 1.177e-03, size: 352, ETA: 1:01:19
2025-07-30 08:28:14.344 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.175e-03, size: 256, ETA: 1:01:15
2025-07-30 08:28:17.551 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 139/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 1.0, lr: 1.173e-03, size: 448, ETA: 1:01:11
2025-07-30 08:28:19.033 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:28:25.888 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:28:26.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:28:27.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4441
2025-07-30 08:28:27.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3722
2025-07-30 08:28:27.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2115
2025-07-30 08:28:27.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3426
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.343
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:28:27.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:28:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:28:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:28:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:28:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:28:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:28:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:28:27.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:28:28.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:28:29.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:28:29.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:28:30.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:28:31.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:28:31.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:28:32.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:28:32.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:28:32.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:28:32.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:28:32.975 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:28:32.986 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.90 ms, Average inference time: 8.50 ms

2025-07-30 08:28:32.991 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:28:33.103 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:28:33.219 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch140
2025-07-30 08:28:36.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 1.0, lr: 1.171e-03, size: 320, ETA: 1:01:05
2025-07-30 08:28:40.040 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 9.6, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 4.3, cls_loss: 0.8, lr: 1.169e-03, size: 256, ETA: 1:01:01
2025-07-30 08:28:43.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.153s, data_time: 0.005s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.167e-03, size: 576, ETA: 1:00:57
2025-07-30 08:28:46.701 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.166e-03, size: 288, ETA: 1:00:53
2025-07-30 08:28:49.992 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.164e-03, size: 320, ETA: 1:00:50
2025-07-30 08:28:53.436 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 140/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.163e-03, size: 352, ETA: 1:00:46
2025-07-30 08:28:54.895 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:29:01.705 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:29:03.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:29:04.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4861
2025-07-30 08:29:04.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4124
2025-07-30 08:29:04.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2134
2025-07-30 08:29:04.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3706
2025-07-30 08:29:04.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:29:04.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:29:04.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-07-30 08:29:04.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-07-30 08:29:04.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:29:04.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:29:05.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:29:06.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:29:07.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:29:08.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:29:09.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:29:11.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:29:12.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:29:13.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:29:14.461 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:29:14.461 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:29:14.461 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 08:29:14.461 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:29:14.469 | 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

2025-07-30 08:29:14.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:29:14.553 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:29:14.631 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch141
2025-07-30 08:29:17.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.160e-03, size: 480, ETA: 1:00:40
2025-07-30 08:29:21.046 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 1.2, lr: 1.158e-03, size: 256, ETA: 1:00:36
2025-07-30 08:29:24.336 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 9.3, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 4.3, cls_loss: 1.1, lr: 1.157e-03, size: 416, ETA: 1:00:32
2025-07-30 08:29:27.514 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.155e-03, size: 416, ETA: 1:00:28
2025-07-30 08:29:30.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.153e-03, size: 352, ETA: 1:00:24
2025-07-30 08:29:34.339 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 141/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 4.3, cls_loss: 0.7, lr: 1.152e-03, size: 288, ETA: 1:00:21
2025-07-30 08:29:35.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:29:42.677 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:29:43.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:29:43.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4359
2025-07-30 08:29:44.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3962
2025-07-30 08:29:44.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2353
2025-07-30 08:29:44.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3558
2025-07-30 08:29:44.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:29:44.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:29:44.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:29:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:29:44.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:29:44.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:29:44.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:29:45.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:29:46.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:29:46.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:29:47.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:29:48.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:29:48.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:29:49.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:29:50.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:29:50.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:29:50.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:29:50.086 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:29:50.095 | 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: 8.37 ms

2025-07-30 08:29:50.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:29:50.177 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:29:50.304 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch142
2025-07-30 08:29:53.620 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 1.149e-03, size: 256, ETA: 1:00:15
2025-07-30 08:29:56.912 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 9.1, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 4.1, cls_loss: 0.9, lr: 1.148e-03, size: 544, ETA: 1:00:11
2025-07-30 08:30:00.406 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 10.8, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 5.0, cls_loss: 1.5, lr: 1.146e-03, size: 544, ETA: 1:00:07
2025-07-30 08:30:03.961 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.5, lr: 1.144e-03, size: 448, ETA: 1:00:04
2025-07-30 08:30:07.168 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.9, lr: 1.143e-03, size: 416, ETA: 1:00:00
2025-07-30 08:30:10.412 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 142/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 1.141e-03, size: 288, ETA: 0:59:56
2025-07-30 08:30:11.935 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:30:18.720 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:30:19.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:30:20.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4232
2025-07-30 08:30:20.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3284
2025-07-30 08:30:20.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1816
2025-07-30 08:30:20.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3111
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.182
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.311
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:30:20.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:30:20.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:30:20.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:30:20.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:30:20.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:30:20.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:30:20.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:30:21.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:30:21.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:30:22.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:30:22.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:30:23.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:30:23.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:30:24.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:30:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:30:25.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:30:25.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:30:25.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:30:25.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:30:25.637 | 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: 8.31 ms

2025-07-30 08:30:25.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:30:25.725 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:30:25.817 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch143
2025-07-30 08:30:29.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 3.4, cls_loss: 1.1, lr: 1.139e-03, size: 576, ETA: 0:59:51
2025-07-30 08:30:32.850 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.9, lr: 1.137e-03, size: 544, ETA: 0:59:47
2025-07-30 08:30:36.073 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 1.1, lr: 1.135e-03, size: 512, ETA: 0:59:43
2025-07-30 08:30:39.360 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.8, lr: 1.134e-03, size: 416, ETA: 0:59:39
2025-07-30 08:30:42.671 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.132e-03, size: 448, ETA: 0:59:35
2025-07-30 08:30:45.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 143/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.130e-03, size: 384, ETA: 0:59:31
2025-07-30 08:30:47.540 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:30:54.296 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:30:54.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:30:54.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3557
2025-07-30 08:30:54.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3121
2025-07-30 08:30:54.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2328
2025-07-30 08:30:54.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3002
2025-07-30 08:30:54.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.233
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.300
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:30:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:30:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:30:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:30:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:30:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:30:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:30:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:30:55.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:30:55.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:30:55.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:30:55.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:30:56.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:30:56.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:30:56.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:30:56.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:30:57.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:30:57.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:30:57.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 08:30:57.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:30:57.171 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.51 ms, Average NMS time: 0.75 ms, Average inference time: 8.26 ms

2025-07-30 08:30:57.175 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:30:57.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:30:57.324 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch144
2025-07-30 08:31:00.592 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.7, lr: 1.128e-03, size: 544, ETA: 0:59:26
2025-07-30 08:31:04.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.6, conf_loss: 2.4, cls_loss: 0.8, lr: 1.126e-03, size: 544, ETA: 0:59:22
2025-07-30 08:31:07.634 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 1.125e-03, size: 448, ETA: 0:59:18
2025-07-30 08:31:10.857 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.123e-03, size: 512, ETA: 0:59:15
2025-07-30 08:31:14.216 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.8, lr: 1.121e-03, size: 576, ETA: 0:59:11
2025-07-30 08:31:17.607 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 144/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.120e-03, size: 416, ETA: 0:59:07
2025-07-30 08:31:19.134 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:31:25.893 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:31:26.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:31:26.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4190
2025-07-30 08:31:26.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3945
2025-07-30 08:31:26.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2201
2025-07-30 08:31:26.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3445
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.220
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:31:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:31:26.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:31:26.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:31:26.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:31:26.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:31:26.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:31:27.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:31:27.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:31:28.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:31:28.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:31:29.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:31:29.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:31:29.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:31:30.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:31:30.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:31:30.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:31:30.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:31:30.792 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:31:30.799 | 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

2025-07-30 08:31:30.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:31:30.927 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:31:31.004 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch145
2025-07-30 08:31:34.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.117e-03, size: 512, ETA: 0:59:01
2025-07-30 08:31:37.765 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.9, lr: 1.116e-03, size: 480, ETA: 0:58:58
2025-07-30 08:31:41.068 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.7, lr: 1.114e-03, size: 480, ETA: 0:58:54
2025-07-30 08:31:44.490 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.112e-03, size: 576, ETA: 0:58:50
2025-07-30 08:31:47.928 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.111e-03, size: 352, ETA: 0:58:46
2025-07-30 08:31:50.995 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 145/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.6, lr: 1.109e-03, size: 320, ETA: 0:58:42
2025-07-30 08:31:52.564 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:31:59.437 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:32:00.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:32:00.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4927
2025-07-30 08:32:01.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4218
2025-07-30 08:32:01.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3085
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4077
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.408
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:32:01.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:32:01.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:32:01.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:32:01.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:32:01.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:32:01.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:32:01.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:32:01.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:32:02.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:32:03.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:32:03.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:32:04.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:32:05.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:32:06.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:32:06.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:32:07.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:32:07.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 08:32:07.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-07-30 08:32:07.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:32:07.486 | 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

2025-07-30 08:32:07.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:32:07.562 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:32:07.667 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch146
2025-07-30 08:32:10.847 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 1.0, lr: 1.106e-03, size: 288, ETA: 0:58:37
2025-07-30 08:32:14.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 1.105e-03, size: 320, ETA: 0:58:33
2025-07-30 08:32:17.319 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.9, lr: 1.103e-03, size: 384, ETA: 0:58:29
2025-07-30 08:32:20.462 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 1.101e-03, size: 352, ETA: 0:58:25
2025-07-30 08:32:23.693 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.100e-03, size: 352, ETA: 0:58:21
2025-07-30 08:32:27.012 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 146/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 0.8, lr: 1.098e-03, size: 448, ETA: 0:58:17
2025-07-30 08:32:28.557 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:32:35.440 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:32:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:32:36.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4300
2025-07-30 08:32:36.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3674
2025-07-30 08:32:36.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2023
2025-07-30 08:32:36.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3333
2025-07-30 08:32:36.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:32:36.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:32:36.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-07-30 08:32:36.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-07-30 08:32:36.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-07-30 08:32:36.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.333
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:32:36.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:32:36.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:32:37.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:32:37.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:32:38.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:32:38.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:32:39.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:32:39.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:32:40.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:32:40.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:32:41.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:32:41.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:32:41.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 08:32:41.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:32:41.220 | 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

2025-07-30 08:32:41.221 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:32:41.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:32:41.369 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch147
2025-07-30 08:32:44.658 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.8, lr: 1.096e-03, size: 448, ETA: 0:58:12
2025-07-30 08:32:47.877 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.004s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.094e-03, size: 352, ETA: 0:58:08
2025-07-30 08:32:51.131 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 1.092e-03, size: 512, ETA: 0:58:04
2025-07-30 08:32:54.319 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.091e-03, size: 352, ETA: 0:58:00
2025-07-30 08:32:57.546 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 1.089e-03, size: 320, ETA: 0:57:56
2025-07-30 08:33:00.745 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 147/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.087e-03, size: 352, ETA: 0:57:52
2025-07-30 08:33:02.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:33:08.998 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:33:09.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:33:10.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4493
2025-07-30 08:33:10.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4121
2025-07-30 08:33:10.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2347
2025-07-30 08:33:10.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3654
2025-07-30 08:33:10.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:33:10.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:33:10.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-07-30 08:33:10.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-07-30 08:33:10.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-07-30 08:33:10.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.365
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:33:10.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:33:10.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:33:11.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:33:11.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:33:12.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:33:12.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:33:13.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:33:13.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:33:14.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:33:14.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:33:14.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:33:14.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 08:33:14.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:33:14.666 | 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: 8.46 ms

2025-07-30 08:33:14.667 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:33:14.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:33:14.822 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch148
2025-07-30 08:33:17.973 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.085e-03, size: 288, ETA: 0:57:46
2025-07-30 08:33:21.298 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 1.083e-03, size: 480, ETA: 0:57:42
2025-07-30 08:33:24.465 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 1.082e-03, size: 352, ETA: 0:57:38
2025-07-30 08:33:27.678 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.8, lr: 1.080e-03, size: 352, ETA: 0:57:34
2025-07-30 08:33:31.067 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 1.078e-03, size: 320, ETA: 0:57:31
2025-07-30 08:33:34.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 148/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.8, lr: 1.077e-03, size: 448, ETA: 0:57:27
2025-07-30 08:33:35.860 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:33:42.729 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:33:43.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:33:43.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3269
2025-07-30 08:33:43.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2627
2025-07-30 08:33:43.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0809
2025-07-30 08:33:43.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2235
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.081
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.224
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:33:43.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:33:43.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:33:43.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:33:43.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:33:43.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:33:43.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:33:43.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:33:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:33:45.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:33:45.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:33:46.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:33:46.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:33:46.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:33:47.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:33:47.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:33:48.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:33:48.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:33:48.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-07-30 08:33:48.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:33:48.489 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.39 ms, Average NMS time: 0.85 ms, Average inference time: 8.24 ms

2025-07-30 08:33:48.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:33:48.597 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:33:48.703 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch149
2025-07-30 08:33:51.902 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 1.0, lr: 1.074e-03, size: 256, ETA: 0:57:21
2025-07-30 08:33:55.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.7, lr: 1.072e-03, size: 384, ETA: 0:57:17
2025-07-30 08:33:58.550 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 0.8, lr: 1.071e-03, size: 288, ETA: 0:57:13
2025-07-30 08:34:01.781 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 2.6, cls_loss: 1.0, lr: 1.069e-03, size: 512, ETA: 0:57:10
2025-07-30 08:34:05.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.067e-03, size: 352, ETA: 0:57:06
2025-07-30 08:34:08.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 149/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.9, lr: 1.066e-03, size: 512, ETA: 0:57:02
2025-07-30 08:34:10.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:34:16.773 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:34:17.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:34:17.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2831
2025-07-30 08:34:17.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2692
2025-07-30 08:34:17.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0961
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2161
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.096
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.216
2025-07-30 08:34:17.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:34:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:34:17.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:34:18.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:34:18.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:34:18.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:34:19.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:34:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:34:19.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:34:20.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:34:20.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:34:20.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:34:20.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.22
2025-07-30 08:34:20.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:34:20.546 | 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

2025-07-30 08:34:20.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:34:20.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:34:20.705 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch150
2025-07-30 08:34:23.932 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.063e-03, size: 352, ETA: 0:56:57
2025-07-30 08:34:27.105 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.062e-03, size: 320, ETA: 0:56:53
2025-07-30 08:34:30.324 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 1.0, lr: 1.060e-03, size: 288, ETA: 0:56:49
2025-07-30 08:34:33.501 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.5, cls_loss: 1.1, lr: 1.058e-03, size: 576, ETA: 0:56:45
2025-07-30 08:34:36.844 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 1.1, lr: 1.057e-03, size: 480, ETA: 0:56:41
2025-07-30 08:34:40.102 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 150/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 1.055e-03, size: 512, ETA: 0:56:37
2025-07-30 08:34:41.607 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:34:48.532 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:34:49.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:34:49.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4115
2025-07-30 08:34:49.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3831
2025-07-30 08:34:49.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1687
2025-07-30 08:34:49.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3211
2025-07-30 08:34:49.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.321
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:34:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:34:49.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:34:49.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:34:49.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:34:49.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:34:49.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:34:50.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:34:50.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:34:50.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:34:51.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:34:51.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:34:52.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:34:52.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:34:53.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:34:53.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:34:53.489 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:34:53.489 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 08:34:53.489 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:34:53.498 | 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

2025-07-30 08:34:53.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:34:53.605 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:34:53.686 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch151
2025-07-30 08:34:56.914 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 1.0, lr: 1.052e-03, size: 256, ETA: 0:56:32
2025-07-30 08:35:00.508 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.051e-03, size: 256, ETA: 0:56:28
2025-07-30 08:35:03.802 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 4.0, cls_loss: 0.9, lr: 1.049e-03, size: 416, ETA: 0:56:24
2025-07-30 08:35:07.388 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.7, lr: 1.047e-03, size: 352, ETA: 0:56:21
2025-07-30 08:35:10.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.046e-03, size: 448, ETA: 0:56:17
2025-07-30 08:35:14.179 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 151/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.157s, 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: 1.044e-03, size: 512, ETA: 0:56:13
2025-07-30 08:35:15.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:35:22.703 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:35:23.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:35:24.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4160
2025-07-30 08:35:24.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3225
2025-07-30 08:35:24.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1794
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3060
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.179
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.306
2025-07-30 08:35:24.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:35:24.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:35:25.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:35:26.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:35:27.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:35:28.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:35:29.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:35:30.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:35:30.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:35:31.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:35:32.715 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:35:32.715 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:35:32.716 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:35:32.716 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:35:32.724 | 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

2025-07-30 08:35:32.730 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:35:32.804 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:35:32.881 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch152
2025-07-30 08:35:36.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.042e-03, size: 544, ETA: 0:56:08
2025-07-30 08:35:39.639 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.040e-03, size: 256, ETA: 0:56:04
2025-07-30 08:35:42.881 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.038e-03, size: 256, ETA: 0:56:00
2025-07-30 08:35:46.187 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.7, lr: 1.037e-03, size: 256, ETA: 0:55:56
2025-07-30 08:35:49.594 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, 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: 0.7, lr: 1.035e-03, size: 480, ETA: 0:55:53
2025-07-30 08:35:53.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 152/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.033e-03, size: 512, ETA: 0:55:49
2025-07-30 08:35:54.692 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:36:01.638 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:36:02.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:36:02.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4261
2025-07-30 08:36:02.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4017
2025-07-30 08:36:02.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2124
2025-07-30 08:36:02.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3468
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.212
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.347
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:36:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:36:02.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:36:02.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:36:02.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:36:02.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:36:02.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:36:03.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:36:03.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:36:04.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:36:05.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:36:05.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:36:06.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:36:06.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:36:07.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:36:07.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:36:07.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:36:07.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 08:36:07.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:36:07.582 | 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

2025-07-30 08:36:07.584 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:36:07.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:36:07.734 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch153
2025-07-30 08:36:10.921 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 1.0, lr: 1.031e-03, size: 352, ETA: 0:55:44
2025-07-30 08:36:14.403 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, 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: 1.029e-03, size: 256, ETA: 0:55:40
2025-07-30 08:36:17.562 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.027e-03, size: 288, ETA: 0:55:36
2025-07-30 08:36:20.831 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.026e-03, size: 288, ETA: 0:55:32
2025-07-30 08:36:24.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.9, lr: 1.024e-03, size: 320, ETA: 0:55:28
2025-07-30 08:36:27.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 153/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 1.022e-03, size: 544, ETA: 0:55:25
2025-07-30 08:36:28.957 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:36:35.863 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:36:36.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:36:36.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4395
2025-07-30 08:36:37.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3822
2025-07-30 08:36:37.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2307
2025-07-30 08:36:37.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3508
2025-07-30 08:36:37.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:36:37.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:36:37.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-07-30 08:36:37.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.351
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:36:37.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:36:37.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:36:37.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:36:38.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:36:38.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:36:39.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:36:39.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:36:40.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:36:41.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:36:41.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:36:42.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:36:42.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:36:42.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 08:36:42.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:36:42.103 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.87 ms, Average inference time: 8.21 ms

2025-07-30 08:36:42.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:36:42.191 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:36:42.280 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch154
2025-07-30 08:36:45.603 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.020e-03, size: 384, ETA: 0:55:19
2025-07-30 08:36:49.119 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 1.0, lr: 1.018e-03, size: 448, ETA: 0:55:15
2025-07-30 08:36:52.436 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 1.017e-03, size: 576, ETA: 0:55:12
2025-07-30 08:36:55.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.164s, 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: 1.015e-03, size: 544, ETA: 0:55:08
2025-07-30 08:36:59.162 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 9.4, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 4.7, cls_loss: 0.8, lr: 1.013e-03, size: 512, ETA: 0:55:04
2025-07-30 08:37:02.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 154/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.7, lr: 1.012e-03, size: 544, ETA: 0:55:00
2025-07-30 08:37:03.932 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:37:10.754 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:37:12.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:37:12.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4611
2025-07-30 08:37:13.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3338
2025-07-30 08:37:13.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1714
2025-07-30 08:37:13.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3221
2025-07-30 08:37:13.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:37:13.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:37:13.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.171
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:37:13.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:37:13.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:37:14.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:37:15.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:37:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:37:18.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:37:19.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:37:20.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:37:21.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:37:22.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:37:24.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:37:24.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:37:24.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 08:37:24.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:37:24.142 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.64 ms, Average NMS time: 0.91 ms, Average inference time: 8.55 ms

2025-07-30 08:37:24.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:37:24.219 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:37:24.310 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch155
2025-07-30 08:37:27.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.7, lr: 1.009e-03, size: 416, ETA: 0:54:55
2025-07-30 08:37:30.912 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 1.0, lr: 1.007e-03, size: 416, ETA: 0:54:51
2025-07-30 08:37:34.258 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 3.8, cls_loss: 1.1, lr: 1.006e-03, size: 480, ETA: 0:54:47
2025-07-30 08:37:37.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 4.0, cls_loss: 1.3, lr: 1.004e-03, size: 448, ETA: 0:54:43
2025-07-30 08:37:40.970 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 1.002e-03, size: 480, ETA: 0:54:40
2025-07-30 08:37:44.447 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 155/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.8, lr: 1.001e-03, size: 256, ETA: 0:54:36
2025-07-30 08:37:45.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:37:52.880 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:37:53.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:37:54.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4803
2025-07-30 08:37:54.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4186
2025-07-30 08:37:54.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2511
2025-07-30 08:37:54.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3833
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.251
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.383
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:37:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:37:54.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:37:54.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:37:54.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:37:54.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:37:54.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:37:55.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:37:56.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:37:57.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:37:57.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:37:58.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:37:59.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:38:00.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:38:01.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:38:01.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:38:01.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:38:01.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 08:38:01.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:38:01.896 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.91 ms, Average inference time: 8.47 ms

2025-07-30 08:38:01.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:38:02.006 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:38:02.129 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch156
2025-07-30 08:38:05.254 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, 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: 9.983e-04, size: 416, ETA: 0:54:30
2025-07-30 08:38:08.634 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 9.966e-04, size: 384, ETA: 0:54:27
2025-07-30 08:38:11.956 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 9.950e-04, size: 288, ETA: 0:54:23
2025-07-30 08:38:15.193 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.9, lr: 9.933e-04, size: 544, ETA: 0:54:19
2025-07-30 08:38:18.525 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.9, lr: 9.916e-04, size: 416, ETA: 0:54:15
2025-07-30 08:38:21.634 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 156/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 9.899e-04, size: 416, ETA: 0:54:11
2025-07-30 08:38:23.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:38:29.834 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:38:30.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:38:31.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3999
2025-07-30 08:38:31.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3350
2025-07-30 08:38:31.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1418
2025-07-30 08:38:31.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2922
2025-07-30 08:38:31.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.335
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.142
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.292
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:38:31.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:38:31.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:38:31.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:38:31.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:38:31.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:38:32.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:38:33.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:38:33.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:38:34.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:38:34.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:38:35.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:38:36.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:38:36.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:38:36.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:38:36.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:38:36.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:38:36.637 | 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

2025-07-30 08:38:36.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:38:36.762 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:38:36.837 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch157
2025-07-30 08:38:39.961 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.875e-04, size: 416, ETA: 0:54:06
2025-07-30 08:38:43.269 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, 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: 9.858e-04, size: 320, ETA: 0:54:02
2025-07-30 08:38:46.467 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 9.841e-04, size: 256, ETA: 0:53:58
2025-07-30 08:38:49.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 9.824e-04, size: 512, ETA: 0:53:54
2025-07-30 08:38:53.075 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 9.808e-04, size: 576, ETA: 0:53:51
2025-07-30 08:38:56.511 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 157/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.7, lr: 9.791e-04, size: 512, ETA: 0:53:47
2025-07-30 08:38:57.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:39:04.854 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:39:05.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:39:05.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3784
2025-07-30 08:39:06.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2803
2025-07-30 08:39:06.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1510
2025-07-30 08:39:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2699
2025-07-30 08:39:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:39:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:39:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-07-30 08:39:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-07-30 08:39:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.151
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.270
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:39:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:39:06.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:39:07.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:39:07.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:39:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:39:08.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:39:09.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:39:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:39:09.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:39:10.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:39:10.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:39:10.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 08:39:10.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:39:10.475 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.84 ms, Average inference time: 8.45 ms

2025-07-30 08:39:10.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:39:10.550 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:39:10.627 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch158
2025-07-30 08:39:13.782 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 9.767e-04, size: 416, ETA: 0:53:41
2025-07-30 08:39:17.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 9.750e-04, size: 544, ETA: 0:53:38
2025-07-30 08:39:20.521 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 4.3, cls_loss: 0.8, lr: 9.733e-04, size: 384, ETA: 0:53:34
2025-07-30 08:39:23.791 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 9.3, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 4.3, cls_loss: 0.8, lr: 9.716e-04, size: 288, ETA: 0:53:30
2025-07-30 08:39:27.059 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 9.699e-04, size: 416, ETA: 0:53:26
2025-07-30 08:39:30.518 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 158/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.9, lr: 9.683e-04, size: 576, ETA: 0:53:23
2025-07-30 08:39:32.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:39:38.816 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:39:39.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:39:40.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4150
2025-07-30 08:39:40.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4009
2025-07-30 08:39:40.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2166
2025-07-30 08:39:40.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3442
2025-07-30 08:39:40.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:39:40.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:39:40.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:39:40.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:39:40.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:39:41.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:39:42.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:39:43.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:39:44.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:39:45.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:39:45.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:39:46.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:39:47.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:39:48.484 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:39:48.484 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:39:48.485 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:39:48.485 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:39:48.492 | 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

2025-07-30 08:39:48.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:39:48.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:39:48.650 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch159
2025-07-30 08:39:52.025 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 9.658e-04, size: 512, ETA: 0:53:17
2025-07-30 08:39:55.473 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 9.641e-04, size: 288, ETA: 0:53:14
2025-07-30 08:39:58.626 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 9.625e-04, size: 576, ETA: 0:53:10
2025-07-30 08:40:01.916 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 9.608e-04, size: 576, ETA: 0:53:06
2025-07-30 08:40:05.267 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 1.1, lr: 9.591e-04, size: 352, ETA: 0:53:02
2025-07-30 08:40:08.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 159/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.9, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 3.7, cls_loss: 0.7, lr: 9.574e-04, size: 544, ETA: 0:52:59
2025-07-30 08:40:10.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:40:17.050 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:40:17.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:40:18.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4703
2025-07-30 08:40:18.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4134
2025-07-30 08:40:18.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2522
2025-07-30 08:40:18.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3786
2025-07-30 08:40:18.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:40:18.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:40:18.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-07-30 08:40:18.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-07-30 08:40:18.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.252
2025-07-30 08:40:18.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.379
2025-07-30 08:40:18.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:40:18.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:40:18.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:40:18.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:40:18.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:40:18.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:40:18.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:40:18.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:40:18.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:40:19.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:40:19.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:40:20.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:40:20.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:40:21.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:40:21.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:40:22.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:40:22.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:40:23.000 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:40:23.000 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:40:23.000 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 08:40:23.000 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:40:23.008 | 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

2025-07-30 08:40:23.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:40:23.126 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:40:23.204 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch160
2025-07-30 08:40:26.453 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 9.550e-04, size: 512, ETA: 0:52:53
2025-07-30 08:40:29.810 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 9.533e-04, size: 384, ETA: 0:52:49
2025-07-30 08:40:33.176 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 9.516e-04, size: 320, ETA: 0:52:46
2025-07-30 08:40:36.365 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 9.500e-04, size: 480, ETA: 0:52:42
2025-07-30 08:40:39.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.8, lr: 9.483e-04, size: 480, ETA: 0:52:38
2025-07-30 08:40:43.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 160/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.7, lr: 9.466e-04, size: 256, ETA: 0:52:35
2025-07-30 08:40:44.482 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:40:51.315 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:40:52.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:40:53.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3754
2025-07-30 08:40:54.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3705
2025-07-30 08:40:54.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1300
2025-07-30 08:40:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2920
2025-07-30 08:40:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:40:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:40:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-07-30 08:40:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-07-30 08:40:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.130
2025-07-30 08:40:54.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.292
2025-07-30 08:40:54.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:40:54.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:40:54.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:40:54.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:40:54.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:40:54.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:40:54.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:40:54.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:40:54.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:40:55.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:40:56.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:40:58.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:40:59.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:41:00.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:41:02.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:41:03.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:41:04.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:41:05.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:41:05.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:41:05.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:41:05.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:41:05.939 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.89 ms, Average inference time: 8.25 ms

2025-07-30 08:41:05.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:41:06.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:41:06.090 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch161
2025-07-30 08:41:09.457 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 0.7, lr: 9.442e-04, size: 544, ETA: 0:52:29
2025-07-30 08:41:12.697 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.004s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 9.425e-04, size: 416, ETA: 0:52:25
2025-07-30 08:41:16.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 1.0, lr: 9.408e-04, size: 544, ETA: 0:52:22
2025-07-30 08:41:19.540 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.5, lr: 9.392e-04, size: 416, ETA: 0:52:18
2025-07-30 08:41:22.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 1.0, lr: 9.375e-04, size: 384, ETA: 0:52:14
2025-07-30 08:41:25.957 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 161/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 9.358e-04, size: 256, ETA: 0:52:10
2025-07-30 08:41:27.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:41:34.239 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:41:35.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:41:36.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4328
2025-07-30 08:41:36.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3826
2025-07-30 08:41:37.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2190
2025-07-30 08:41:37.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3448
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.219
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:41:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:41:37.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:41:37.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:41:37.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:41:37.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:41:38.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:41:39.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:41:40.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:41:42.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:41:43.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:41:44.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:41:45.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:41:46.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:41:47.938 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:41:47.938 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:41:47.938 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:41:47.938 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:41:47.946 | 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

2025-07-30 08:41:47.947 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:41:48.018 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:41:48.137 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch162
2025-07-30 08:41:51.391 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.158s, 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: 9.334e-04, size: 416, ETA: 0:52:05
2025-07-30 08:41:54.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 9.317e-04, size: 256, ETA: 0:52:01
2025-07-30 08:41:57.830 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 1.1, lr: 9.300e-04, size: 512, ETA: 0:51:57
2025-07-30 08:42:01.154 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.161s, 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: 9.283e-04, size: 320, ETA: 0:51:54
2025-07-30 08:42:04.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 1.2, lr: 9.267e-04, size: 256, ETA: 0:51:50
2025-07-30 08:42:07.570 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 162/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.7, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.5, lr: 9.250e-04, size: 576, ETA: 0:51:46
2025-07-30 08:42:09.027 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:42:15.838 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:42:16.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:42:16.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4459
2025-07-30 08:42:17.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3846
2025-07-30 08:42:17.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2156
2025-07-30 08:42:17.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3487
2025-07-30 08:42:17.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:42:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:42:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-07-30 08:42:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-07-30 08:42:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-07-30 08:42:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.349
2025-07-30 08:42:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:42:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:42:17.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:42:17.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:42:17.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:42:17.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:42:17.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:42:17.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:42:17.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:42:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:42:18.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:42:18.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:42:19.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:42:19.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:42:20.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:42:20.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:42:21.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:42:21.737 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:42:21.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:42:21.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 08:42:21.739 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:42:21.754 | 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

2025-07-30 08:42:21.755 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:42:21.884 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:42:22.025 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch163
2025-07-30 08:42:25.140 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 9.226e-04, size: 480, ETA: 0:51:41
2025-07-30 08:42:28.484 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.6, lr: 9.209e-04, size: 288, ETA: 0:51:37
2025-07-30 08:42:31.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 1.1, lr: 9.192e-04, size: 512, ETA: 0:51:33
2025-07-30 08:42:35.103 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 4.1, cls_loss: 0.9, lr: 9.175e-04, size: 416, ETA: 0:51:29
2025-07-30 08:42:38.431 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.8, lr: 9.159e-04, size: 256, ETA: 0:51:26
2025-07-30 08:42:41.708 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 163/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 9.142e-04, size: 416, ETA: 0:51:22
2025-07-30 08:42:43.137 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:42:50.026 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:42:51.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:42:51.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4688
2025-07-30 08:42:51.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3881
2025-07-30 08:42:51.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1497
2025-07-30 08:42:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3355
2025-07-30 08:42:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:42:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:42:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-07-30 08:42:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-07-30 08:42:51.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.150
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:42:51.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:42:52.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:42:53.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:42:54.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:42:55.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:42:55.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:42:56.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:42:57.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:42:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:42:59.122 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:42:59.122 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:42:59.122 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:42:59.122 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:42:59.129 | 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

2025-07-30 08:42:59.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:42:59.202 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:42:59.279 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch164
2025-07-30 08:43:02.562 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 9.118e-04, size: 384, ETA: 0:51:16
2025-07-30 08:43:05.841 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.9, lr: 9.101e-04, size: 448, ETA: 0:51:13
2025-07-30 08:43:09.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.7, lr: 9.084e-04, size: 256, ETA: 0:51:09
2025-07-30 08:43:12.130 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 9.068e-04, size: 352, ETA: 0:51:05
2025-07-30 08:43:15.264 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 9.051e-04, size: 288, ETA: 0:51:01
2025-07-30 08:43:18.535 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 164/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 9.034e-04, size: 320, ETA: 0:50:57
2025-07-30 08:43:19.904 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:43:26.771 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:43:27.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:43:28.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3697
2025-07-30 08:43:28.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3051
2025-07-30 08:43:28.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1675
2025-07-30 08:43:28.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2808
2025-07-30 08:43:28.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.167
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.281
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:43:28.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:43:28.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:43:28.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:43:28.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:43:28.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:43:28.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:43:29.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:43:30.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:43:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:43:32.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:43:33.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:43:34.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:43:34.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:43:35.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:43:36.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:43:36.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:43:36.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 08:43:36.656 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:43:36.664 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.91 ms, Average inference time: 8.47 ms

2025-07-30 08:43:36.665 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:43:36.742 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:43:36.818 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch165
2025-07-30 08:43:39.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 9.010e-04, size: 384, ETA: 0:50:52
2025-07-30 08:43:43.304 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 8.993e-04, size: 288, ETA: 0:50:48
2025-07-30 08:43:46.803 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 1.2, lr: 8.976e-04, size: 416, ETA: 0:50:44
2025-07-30 08:43:49.934 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 1.1, lr: 8.960e-04, size: 384, ETA: 0:50:41
2025-07-30 08:43:53.378 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 8.943e-04, size: 544, ETA: 0:50:37
2025-07-30 08:43:56.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 165/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.7, lr: 8.926e-04, size: 416, ETA: 0:50:33
2025-07-30 08:43:58.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:44:05.035 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:44:05.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:44:06.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4349
2025-07-30 08:44:06.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3800
2025-07-30 08:44:06.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2290
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3480
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.229
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.348
2025-07-30 08:44:06.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:44:06.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:44:07.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:44:07.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:44:08.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:44:08.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:44:09.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:44:10.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:44:10.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:44:11.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:44:12.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:44:12.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:44:12.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 08:44:12.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:44:12.039 | 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

2025-07-30 08:44:12.042 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:44:12.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:44:12.198 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch166
2025-07-30 08:44:15.488 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 8.902e-04, size: 576, ETA: 0:50:28
2025-07-30 08:44:18.996 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 8.885e-04, size: 448, ETA: 0:50:24
2025-07-30 08:44:22.202 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 8.869e-04, size: 320, ETA: 0:50:21
2025-07-30 08:44:25.457 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 9.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 4.8, cls_loss: 0.8, lr: 8.852e-04, size: 480, ETA: 0:50:17
2025-07-30 08:44:28.779 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 8.835e-04, size: 576, ETA: 0:50:13
2025-07-30 08:44:32.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 166/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 8.819e-04, size: 512, ETA: 0:50:09
2025-07-30 08:44:33.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:44:40.375 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:44:40.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:44:41.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3825
2025-07-30 08:44:41.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3058
2025-07-30 08:44:41.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1281
2025-07-30 08:44:41.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2721
2025-07-30 08:44:41.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:44:41.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.128
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.272
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:44:41.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:44:41.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:44:41.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:44:41.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:44:41.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:44:41.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:44:42.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:44:42.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:44:42.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:44:43.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:44:43.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:44:43.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:44:44.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:44:44.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:44:44.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 08:44:44.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:44:44.273 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.41 ms, Average NMS time: 0.78 ms, Average inference time: 8.18 ms

2025-07-30 08:44:44.274 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:44:44.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:44:44.448 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch167
2025-07-30 08:44:47.642 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.7, lr: 8.795e-04, size: 416, ETA: 0:50:04
2025-07-30 08:44:51.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 8.778e-04, size: 288, ETA: 0:50:00
2025-07-30 08:44:54.370 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 8.761e-04, size: 448, ETA: 0:49:57
2025-07-30 08:44:57.863 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, 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: 8.745e-04, size: 544, ETA: 0:49:53
2025-07-30 08:45:01.138 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 8.728e-04, size: 576, ETA: 0:49:49
2025-07-30 08:45:04.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 167/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, 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: 8.711e-04, size: 544, ETA: 0:49:46
2025-07-30 08:45:06.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:45:12.914 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:45:13.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:45:13.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.0903
2025-07-30 08:45:14.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1135
2025-07-30 08:45:14.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0189
2025-07-30 08:45:14.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.0743
2025-07-30 08:45:14.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:45:14.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:45:14.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.090
2025-07-30 08:45:14.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.114
2025-07-30 08:45:14.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.019
2025-07-30 08:45:14.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.074
2025-07-30 08:45:14.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:45:14.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:45:14.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:45:14.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:45:14.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:45:14.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:45:14.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:45:14.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:45:14.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:45:14.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:45:15.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:45:15.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:45:16.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:45:16.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:45:16.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:45:17.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:45:17.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:45:18.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:45:18.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.04
2025-07-30 08:45:18.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.07
2025-07-30 08:45:18.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:45:18.424 | 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: 8.34 ms

2025-07-30 08:45:18.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:45:18.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:45:18.577 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch168
2025-07-30 08:45:21.747 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.8, lr: 8.687e-04, size: 576, ETA: 0:49:40
2025-07-30 08:45:25.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 8.670e-04, size: 416, ETA: 0:49:37
2025-07-30 08:45:28.325 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.9, lr: 8.654e-04, size: 544, ETA: 0:49:33
2025-07-30 08:45:31.661 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 8.637e-04, size: 480, ETA: 0:49:29
2025-07-30 08:45:34.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 8.620e-04, size: 480, ETA: 0:49:26
2025-07-30 08:45:38.393 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 168/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 8.604e-04, size: 544, ETA: 0:49:22
2025-07-30 08:45:39.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:45:46.517 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:45:47.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:45:47.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4531
2025-07-30 08:45:47.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3951
2025-07-30 08:45:47.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2493
2025-07-30 08:45:47.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3658
2025-07-30 08:45:47.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:45:47.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:45:47.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-07-30 08:45:47.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-07-30 08:45:47.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-07-30 08:45:47.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-07-30 08:45:47.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:45:47.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:45:47.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:45:47.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:45:47.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:45:47.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:45:47.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:45:47.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:45:47.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:45:48.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:45:48.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:45:49.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:45:49.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:45:49.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:45:50.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:45:50.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:45:51.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:45:51.798 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:45:51.799 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:45:51.799 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 08:45:51.799 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:45:51.806 | 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

2025-07-30 08:45:51.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:45:51.883 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:45:51.959 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch169
2025-07-30 08:45:55.321 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.8, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 1.1, lr: 8.580e-04, size: 448, ETA: 0:49:17
2025-07-30 08:45:58.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 8.563e-04, size: 480, ETA: 0:49:13
2025-07-30 08:46:01.872 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.6, lr: 8.546e-04, size: 544, ETA: 0:49:09
2025-07-30 08:46:05.307 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 1.2, lr: 8.530e-04, size: 416, ETA: 0:49:06
2025-07-30 08:46:08.706 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.9, lr: 8.513e-04, size: 256, ETA: 0:49:02
2025-07-30 08:46:12.077 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 169/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 8.497e-04, size: 576, ETA: 0:48:59
2025-07-30 08:46:13.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:46:20.670 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:46:21.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:46:21.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4664
2025-07-30 08:46:22.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3603
2025-07-30 08:46:22.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2350
2025-07-30 08:46:22.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3539
2025-07-30 08:46:22.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:46:22.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.354
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:46:22.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:46:22.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:46:22.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:46:23.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:46:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:46:24.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:46:25.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:46:25.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:46:26.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:46:27.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:46:27.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:46:27.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:46:27.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 08:46:27.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:46:27.763 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.25 ms, Average NMS time: 0.86 ms, Average inference time: 8.11 ms

2025-07-30 08:46:27.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:46:27.855 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:46:27.946 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch170
2025-07-30 08:46:30.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 8.473e-04, size: 320, ETA: 0:48:53
2025-07-30 08:46:34.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.004s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 8.456e-04, size: 320, ETA: 0:48:49
2025-07-30 08:46:37.700 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 8.439e-04, size: 448, ETA: 0:48:46
2025-07-30 08:46:41.002 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.9, lr: 8.423e-04, size: 256, ETA: 0:48:42
2025-07-30 08:46:44.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 1.1, lr: 8.406e-04, size: 256, ETA: 0:48:38
2025-07-30 08:46:47.519 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 170/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 9.2, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 4.4, cls_loss: 0.9, lr: 8.390e-04, size: 576, ETA: 0:48:35
2025-07-30 08:46:49.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:46:55.941 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:46:56.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:46:57.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3253
2025-07-30 08:46:57.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2667
2025-07-30 08:46:57.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0865
2025-07-30 08:46:57.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2262
2025-07-30 08:46:57.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:46:57.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:46:57.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.325
2025-07-30 08:46:57.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-07-30 08:46:57.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.086
2025-07-30 08:46:57.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.226
2025-07-30 08:46:57.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:46:57.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:46:57.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:46:57.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:46:57.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:46:57.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:46:57.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:46:57.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:46:57.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:46:57.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:46:58.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:46:58.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:46:59.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:46:59.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:47:00.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:47:00.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:47:01.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:47:01.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:47:01.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:47:01.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-07-30 08:47:01.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:47:01.542 | 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: 8.46 ms

2025-07-30 08:47:01.543 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:47:01.616 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:47:01.690 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch171
2025-07-30 08:47:04.833 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 8.366e-04, size: 352, ETA: 0:48:29
2025-07-30 08:47:08.081 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 8.349e-04, size: 320, ETA: 0:48:26
2025-07-30 08:47:11.191 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 1.1, lr: 8.332e-04, size: 384, ETA: 0:48:22
2025-07-30 08:47:14.619 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.0, lr: 8.316e-04, size: 576, ETA: 0:48:18
2025-07-30 08:47:18.122 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 8.299e-04, size: 544, ETA: 0:48:15
2025-07-30 08:47:21.416 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 171/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 8.283e-04, size: 416, ETA: 0:48:11
2025-07-30 08:47:22.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:47:29.671 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:47:30.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:47:31.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4016
2025-07-30 08:47:31.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3390
2025-07-30 08:47:31.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1233
2025-07-30 08:47:31.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2880
2025-07-30 08:47:31.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:47:31.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.123
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.288
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:47:31.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:47:31.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:47:31.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:47:31.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:47:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:47:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:47:33.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:47:34.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:47:35.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:47:35.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:47:36.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:47:37.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:47:37.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:47:37.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:47:37.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:47:37.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:47:37.930 | 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

2025-07-30 08:47:37.932 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:47:38.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:47:38.080 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch172
2025-07-30 08:47:41.288 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 8.259e-04, size: 448, ETA: 0:48:06
2025-07-30 08:47:44.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 8.242e-04, size: 384, ETA: 0:48:02
2025-07-30 08:47:48.075 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.4, lr: 8.226e-04, size: 256, ETA: 0:47:58
2025-07-30 08:47:51.263 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 8.209e-04, size: 256, ETA: 0:47:55
2025-07-30 08:47:54.520 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 8.193e-04, size: 416, ETA: 0:47:51
2025-07-30 08:47:58.075 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 172/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 9.4, iou_loss: 3.0, l1_loss: 1.6, conf_loss: 3.9, cls_loss: 0.9, lr: 8.176e-04, size: 576, ETA: 0:47:48
2025-07-30 08:47:59.552 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:48:06.314 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:48:07.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:48:07.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4340
2025-07-30 08:48:07.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3554
2025-07-30 08:48:07.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2212
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3369
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:48:07.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:48:07.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:48:07.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:48:07.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:48:07.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:48:07.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:48:08.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:48:08.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:48:09.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:48:09.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:48:10.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:48:10.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:48:11.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:48:11.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:48:12.290 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:48:12.290 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 08:48:12.290 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:48:12.290 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:48:12.297 | 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

2025-07-30 08:48:12.300 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:48:12.376 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:48:12.451 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch173
2025-07-30 08:48:15.528 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 1.0, lr: 8.152e-04, size: 320, ETA: 0:47:42
2025-07-30 08:48:18.760 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 8.136e-04, size: 576, ETA: 0:47:38
2025-07-30 08:48:22.226 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 8.119e-04, size: 384, ETA: 0:47:35
2025-07-30 08:48:25.472 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.7, lr: 8.103e-04, size: 512, ETA: 0:47:31
2025-07-30 08:48:28.842 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 9.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 4.8, cls_loss: 1.1, lr: 8.086e-04, size: 256, ETA: 0:47:28
2025-07-30 08:48:32.077 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 173/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 8.070e-04, size: 384, ETA: 0:47:24
2025-07-30 08:48:33.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:48:40.388 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:48:41.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:48:41.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4241
2025-07-30 08:48:42.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3878
2025-07-30 08:48:42.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2181
2025-07-30 08:48:42.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3433
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.218
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.343
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:48:42.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:48:42.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:48:42.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:48:42.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:48:42.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:48:42.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:48:42.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:48:43.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:48:44.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:48:45.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:48:46.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:48:46.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:48:47.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:48:48.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:48:49.070 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:48:49.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:48:49.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 08:48:49.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:48:49.079 | 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: 8.36 ms

2025-07-30 08:48:49.080 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:48:49.212 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:48:49.292 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch174
2025-07-30 08:48:52.497 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 8.046e-04, size: 256, ETA: 0:47:18
2025-07-30 08:48:55.962 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 8.029e-04, size: 512, ETA: 0:47:15
2025-07-30 08:48:59.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.8, lr: 8.013e-04, size: 576, ETA: 0:47:12
2025-07-30 08:49:03.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.8, lr: 7.997e-04, size: 288, ETA: 0:47:08
2025-07-30 08:49:06.510 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 7.980e-04, size: 480, ETA: 0:47:04
2025-07-30 08:49:09.891 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 174/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.004s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 1.1, lr: 7.964e-04, size: 288, ETA: 0:47:01
2025-07-30 08:49:11.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:49:18.320 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:49:19.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:49:19.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3545
2025-07-30 08:49:19.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3634
2025-07-30 08:49:19.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1723
2025-07-30 08:49:19.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2967
2025-07-30 08:49:19.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:49:19.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:49:19.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-07-30 08:49:19.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-07-30 08:49:19.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.172
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.297
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:49:19.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:49:19.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:49:20.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:49:20.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:49:20.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:49:21.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:49:21.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:49:21.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:49:22.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:49:22.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:49:22.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:49:22.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 08:49:22.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:49:22.686 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.85 ms, Average inference time: 8.20 ms

2025-07-30 08:49:22.692 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:49:22.812 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:49:22.885 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch175
2025-07-30 08:49:26.295 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 7.940e-04, size: 512, ETA: 0:46:56
2025-07-30 08:49:29.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, 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: 7.923e-04, size: 352, ETA: 0:46:52
2025-07-30 08:49:32.930 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 7.907e-04, size: 256, ETA: 0:46:48
2025-07-30 08:49:36.238 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.9, lr: 7.891e-04, size: 512, ETA: 0:46:45
2025-07-30 08:49:39.486 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 7.874e-04, size: 384, ETA: 0:46:41
2025-07-30 08:49:42.998 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 175/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.0, lr: 7.858e-04, size: 288, ETA: 0:46:38
2025-07-30 08:49:44.469 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:49:51.309 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:49:51.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:49:52.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4368
2025-07-30 08:49:52.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4343
2025-07-30 08:49:52.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2248
2025-07-30 08:49:52.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3653
2025-07-30 08:49:52.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:49:52.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:49:52.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-07-30 08:49:52.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-07-30 08:49:52.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.225
2025-07-30 08:49:52.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.365
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:49:52.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:49:52.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:49:53.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:49:53.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:49:54.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:49:54.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:49:55.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:49:55.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:49:55.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:49:56.322 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:49:56.323 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:49:56.323 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 08:49:56.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:49:56.336 | 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

2025-07-30 08:49:56.337 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:49:56.495 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:49:56.585 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch176
2025-07-30 08:49:59.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 7.834e-04, size: 448, ETA: 0:46:32
2025-07-30 08:50:02.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.7, lr: 7.818e-04, size: 288, ETA: 0:46:28
2025-07-30 08:50:06.324 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 7.801e-04, size: 256, ETA: 0:46:25
2025-07-30 08:50:09.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 1.0, lr: 7.785e-04, size: 288, ETA: 0:46:21
2025-07-30 08:50:12.913 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 3.3, cls_loss: 0.7, lr: 7.768e-04, size: 480, ETA: 0:46:17
2025-07-30 08:50:16.374 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 176/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 7.752e-04, size: 320, ETA: 0:46:14
2025-07-30 08:50:17.753 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:50:24.564 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:50:25.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:50:26.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3654
2025-07-30 08:50:26.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3243
2025-07-30 08:50:26.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1814
2025-07-30 08:50:26.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2903
2025-07-30 08:50:26.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:50:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:50:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-07-30 08:50:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-07-30 08:50:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-07-30 08:50:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.290
2025-07-30 08:50:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:50:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:50:26.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:50:26.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:50:26.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:50:26.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:50:26.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:50:26.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:50:26.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:50:27.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:50:28.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:50:29.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:50:30.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:50:31.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:50:32.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:50:32.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:50:33.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:50:34.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:50:34.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:50:34.720 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:50:34.720 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:50:34.727 | 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: 8.36 ms

2025-07-30 08:50:34.728 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:50:34.800 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:50:34.915 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch177
2025-07-30 08:50:38.237 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 7.728e-04, size: 448, ETA: 0:46:08
2025-07-30 08:50:41.493 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 7.712e-04, size: 448, ETA: 0:46:05
2025-07-30 08:50:44.876 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 3.8, cls_loss: 0.6, lr: 7.696e-04, size: 512, ETA: 0:46:01
2025-07-30 08:50:48.428 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 7.679e-04, size: 320, ETA: 0:45:58
2025-07-30 08:50:51.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 3.3, cls_loss: 0.6, lr: 7.663e-04, size: 320, ETA: 0:45:54
2025-07-30 08:50:54.993 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 177/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 9.4, iou_loss: 3.0, l1_loss: 1.7, conf_loss: 3.6, cls_loss: 1.1, lr: 7.647e-04, size: 512, ETA: 0:45:50
2025-07-30 08:50:56.527 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:51:03.213 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:51:04.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:51:04.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4407
2025-07-30 08:51:04.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4107
2025-07-30 08:51:04.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2274
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3596
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.360
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:51:04.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:51:04.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:51:04.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:51:04.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:51:04.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:51:04.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:51:04.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:51:04.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:51:05.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:51:06.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:51:06.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:51:07.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:51:08.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:51:09.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:51:10.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:51:11.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:51:11.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:51:11.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:51:11.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:51:11.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:51:11.954 | 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: 8.30 ms

2025-07-30 08:51:11.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:51:12.039 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:51:12.114 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch178
2025-07-30 08:51:15.148 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 7.623e-04, size: 480, ETA: 0:45:45
2025-07-30 08:51:18.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 7.607e-04, size: 416, ETA: 0:45:41
2025-07-30 08:51:21.771 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 7.590e-04, size: 288, ETA: 0:45:38
2025-07-30 08:51:25.063 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 7.574e-04, size: 384, ETA: 0:45:34
2025-07-30 08:51:28.148 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 7.558e-04, size: 384, ETA: 0:45:30
2025-07-30 08:51:31.354 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 178/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 1.0, lr: 7.541e-04, size: 384, ETA: 0:45:26
2025-07-30 08:51:32.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:51:39.440 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:51:40.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:51:40.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4494
2025-07-30 08:51:40.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3745
2025-07-30 08:51:40.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1701
2025-07-30 08:51:40.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3313
2025-07-30 08:51:40.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:51:40.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:51:40.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-07-30 08:51:40.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-07-30 08:51:40.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.170
2025-07-30 08:51:40.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.331
2025-07-30 08:51:40.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:51:40.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:51:40.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:51:40.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:51:40.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:51:40.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:51:40.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:51:40.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:51:40.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:51:41.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:51:42.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:51:42.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:51:43.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:51:44.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:51:44.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:51:45.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:51:46.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:51:46.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:51:46.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 08:51:46.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 08:51:46.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:51:46.661 | 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

2025-07-30 08:51:46.664 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:51:46.734 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:51:46.810 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch179
2025-07-30 08:51:50.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 8.2, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.8, lr: 7.518e-04, size: 576, ETA: 0:45:21
2025-07-30 08:51:53.445 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, 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: 7.502e-04, size: 544, ETA: 0:45:18
2025-07-30 08:51:56.793 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 7.485e-04, size: 544, ETA: 0:45:14
2025-07-30 08:52:00.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 9.8, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 5.0, cls_loss: 0.8, lr: 7.469e-04, size: 576, ETA: 0:45:10
2025-07-30 08:52:03.821 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.6, lr: 7.453e-04, size: 512, ETA: 0:45:07
2025-07-30 08:52:07.274 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 179/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.9, lr: 7.437e-04, size: 512, ETA: 0:45:03
2025-07-30 08:52:08.811 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:52:15.562 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:52:16.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:52:16.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4670
2025-07-30 08:52:16.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4439
2025-07-30 08:52:16.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2345
2025-07-30 08:52:16.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3818
2025-07-30 08:52:16.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:52:16.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:52:16.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-07-30 08:52:16.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-07-30 08:52:16.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.234
2025-07-30 08:52:16.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:52:16.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:52:17.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:52:17.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:52:18.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:52:18.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:52:19.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:52:19.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:52:20.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:52:20.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:52:21.408 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:52:21.408 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 08:52:21.408 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 08:52:21.409 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:52:21.419 | 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: 8.34 ms

2025-07-30 08:52:21.421 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:52:21.520 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:52:21.597 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch180
2025-07-30 08:52:24.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 7.413e-04, size: 384, ETA: 0:44:58
2025-07-30 08:52:28.060 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 7.397e-04, size: 256, ETA: 0:44:54
2025-07-30 08:52:31.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.9, lr: 7.381e-04, size: 288, ETA: 0:44:51
2025-07-30 08:52:34.815 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.6, conf_loss: 2.4, cls_loss: 0.7, lr: 7.364e-04, size: 544, ETA: 0:44:47
2025-07-30 08:52:38.148 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 7.348e-04, size: 576, ETA: 0:44:44
2025-07-30 08:52:41.672 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 180/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.9, lr: 7.332e-04, size: 320, ETA: 0:44:40
2025-07-30 08:52:43.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:52:49.886 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:52:50.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:52:51.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4720
2025-07-30 08:52:51.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4019
2025-07-30 08:52:51.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2321
2025-07-30 08:52:51.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3687
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.369
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:52:51.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:52:51.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:52:51.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:52:51.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:52:51.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:52:52.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:52:52.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:52:53.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:52:54.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:52:54.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:52:55.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:52:56.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:52:56.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:52:57.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:52:57.303 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:52:57.303 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 08:52:57.303 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:52:57.310 | 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: 8.43 ms

2025-07-30 08:52:57.311 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:52:57.399 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:52:57.490 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch181
2025-07-30 08:53:00.640 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 7.309e-04, size: 352, ETA: 0:44:35
2025-07-30 08:53:03.988 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.6, lr: 7.292e-04, size: 448, ETA: 0:44:31
2025-07-30 08:53:07.391 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.8, lr: 7.276e-04, size: 448, ETA: 0:44:28
2025-07-30 08:53:10.720 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 7.260e-04, size: 544, ETA: 0:44:24
2025-07-30 08:53:14.299 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 4.0, cls_loss: 0.7, lr: 7.244e-04, size: 576, ETA: 0:44:21
2025-07-30 08:53:17.696 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 181/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 7.228e-04, size: 480, ETA: 0:44:17
2025-07-30 08:53:19.138 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:53:25.901 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:53:27.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:53:27.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4708
2025-07-30 08:53:28.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4193
2025-07-30 08:53:28.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2450
2025-07-30 08:53:28.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3784
2025-07-30 08:53:28.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:53:28.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:53:28.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-07-30 08:53:28.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-07-30 08:53:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.245
2025-07-30 08:53:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.378
2025-07-30 08:53:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:53:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:53:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:53:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:53:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:53:28.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:53:28.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:53:28.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:53:28.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:53:29.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:53:30.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:53:31.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:53:32.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:53:33.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:53:34.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:53:34.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:53:35.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:53:36.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:53:36.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:53:36.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 08:53:36.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:53:36.901 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.89 ms, Average inference time: 8.25 ms

2025-07-30 08:53:36.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:53:36.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:53:37.052 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch182
2025-07-30 08:53:40.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 10.1, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 5.0, cls_loss: 1.0, lr: 7.204e-04, size: 256, ETA: 0:44:12
2025-07-30 08:53:43.315 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 7.188e-04, size: 448, ETA: 0:44:08
2025-07-30 08:53:46.657 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 7.172e-04, size: 288, ETA: 0:44:04
2025-07-30 08:53:49.833 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 7.156e-04, size: 448, ETA: 0:44:01
2025-07-30 08:53:53.111 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.9, cls_loss: 0.7, lr: 7.140e-04, size: 480, ETA: 0:43:57
2025-07-30 08:53:56.293 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 182/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, 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: 7.124e-04, size: 352, ETA: 0:43:53
2025-07-30 08:53:57.693 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:54:04.540 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:54:05.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:54:05.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4255
2025-07-30 08:54:06.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3315
2025-07-30 08:54:06.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1811
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3127
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-07-30 08:54:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:54:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:54:06.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:54:07.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:54:08.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:54:08.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:54:09.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:54:10.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:54:11.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:54:11.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:54:12.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:54:12.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 08:54:12.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:54:12.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:54:12.398 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.93 ms, Average inference time: 8.47 ms

2025-07-30 08:54:12.399 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:54:12.474 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:54:12.561 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch183
2025-07-30 08:54:15.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.7, lr: 7.101e-04, size: 256, ETA: 0:43:48
2025-07-30 08:54:18.888 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, 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: 7.084e-04, size: 320, ETA: 0:43:44
2025-07-30 08:54:22.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 9.0, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 4.1, cls_loss: 0.8, lr: 7.068e-04, size: 256, ETA: 0:43:41
2025-07-30 08:54:25.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.3, cls_loss: 0.9, lr: 7.052e-04, size: 576, ETA: 0:43:37
2025-07-30 08:54:29.284 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 3.5, cls_loss: 0.6, lr: 7.036e-04, size: 544, ETA: 0:43:34
2025-07-30 08:54:32.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 183/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 7.020e-04, size: 416, ETA: 0:43:30
2025-07-30 08:54:34.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:54:41.120 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:54:41.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:54:41.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4276
2025-07-30 08:54:41.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4064
2025-07-30 08:54:41.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2366
2025-07-30 08:54:41.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3569
2025-07-30 08:54:41.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:54:41.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:54:41.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-07-30 08:54:41.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-07-30 08:54:41.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.237
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:54:41.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:54:42.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:54:42.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:54:43.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:54:43.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:54:43.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:54:44.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:54:44.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:54:44.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:54:45.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:54:45.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 08:54:45.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:54:45.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:54:45.031 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.66 ms, Average NMS time: 0.84 ms, Average inference time: 8.49 ms

2025-07-30 08:54:45.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:54:45.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:54:45.187 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch184
2025-07-30 08:54:48.228 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 6.997e-04, size: 288, ETA: 0:43:25
2025-07-30 08:54:51.547 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.7, lr: 6.981e-04, size: 576, ETA: 0:43:21
2025-07-30 08:54:54.883 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.7, lr: 6.965e-04, size: 256, ETA: 0:43:17
2025-07-30 08:54:58.410 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 4.1, cls_loss: 0.7, lr: 6.949e-04, size: 576, ETA: 0:43:14
2025-07-30 08:55:01.958 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 6.933e-04, size: 352, ETA: 0:43:11
2025-07-30 08:55:05.364 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 184/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 6.917e-04, size: 576, ETA: 0:43:07
2025-07-30 08:55:06.849 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:55:13.556 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:55:14.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:55:14.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4677
2025-07-30 08:55:14.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3894
2025-07-30 08:55:15.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2158
2025-07-30 08:55:15.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3577
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.358
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:55:15.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:55:15.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:55:15.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:55:15.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:55:15.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:55:15.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:55:15.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:55:15.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:55:15.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:55:16.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:55:16.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:55:17.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:55:18.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:55:18.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:55:19.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:55:19.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:55:20.360 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:55:20.360 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 08:55:20.360 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:55:20.360 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:55:20.373 | 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

2025-07-30 08:55:20.374 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:55:20.482 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:55:20.560 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch185
2025-07-30 08:55:23.812 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 6.894e-04, size: 352, ETA: 0:43:02
2025-07-30 08:55:27.278 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 1.0, lr: 6.878e-04, size: 416, ETA: 0:42:58
2025-07-30 08:55:30.576 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 3.8, cls_loss: 1.7, lr: 6.862e-04, size: 352, ETA: 0:42:55
2025-07-30 08:55:33.862 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 6.846e-04, size: 448, ETA: 0:42:51
2025-07-30 08:55:37.072 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 6.830e-04, size: 480, ETA: 0:42:47
2025-07-30 08:55:40.532 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 185/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.8, lr: 6.814e-04, size: 512, ETA: 0:42:44
2025-07-30 08:55:42.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:55:48.972 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:55:50.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:55:50.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3847
2025-07-30 08:55:51.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3160
2025-07-30 08:55:51.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1513
2025-07-30 08:55:51.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2840
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.151
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.284
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:55:51.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:55:51.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:55:51.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:55:51.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:55:51.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:55:51.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:55:51.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:55:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:55:53.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:55:54.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:55:54.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:55:55.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:55:56.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:55:57.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:55:58.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:55:59.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:55:59.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:55:59.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 08:55:59.748 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:55:59.755 | 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

2025-07-30 08:55:59.756 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:55:59.827 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:55:59.908 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch186
2025-07-30 08:56:03.128 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 6.791e-04, size: 544, ETA: 0:42:38
2025-07-30 08:56:06.645 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.5, lr: 6.775e-04, size: 480, ETA: 0:42:35
2025-07-30 08:56:09.863 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 6.759e-04, size: 448, ETA: 0:42:31
2025-07-30 08:56:13.353 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 3.4, cls_loss: 0.7, lr: 6.743e-04, size: 512, ETA: 0:42:28
2025-07-30 08:56:16.661 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, 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: 6.728e-04, size: 288, ETA: 0:42:24
2025-07-30 08:56:19.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 186/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.7, lr: 6.712e-04, size: 288, ETA: 0:42:20
2025-07-30 08:56:21.168 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:56:27.811 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:56:28.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:56:28.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3919
2025-07-30 08:56:28.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3614
2025-07-30 08:56:28.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1902
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3145
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.190
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.315
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:56:28.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:56:29.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:56:29.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:56:29.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:56:29.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:56:29.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:56:29.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:56:29.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:56:29.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:56:30.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:56:30.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:56:31.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:56:31.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:56:32.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:56:32.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:56:32.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:56:32.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:56:32.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 08:56:32.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:56:32.983 | 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

2025-07-30 08:56:32.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:56:33.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:56:33.131 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch187
2025-07-30 08:56:36.400 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.7, lr: 6.689e-04, size: 544, ETA: 0:42:15
2025-07-30 08:56:39.768 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.7, lr: 6.673e-04, size: 544, ETA: 0:42:12
2025-07-30 08:56:43.326 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.173s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.8, lr: 6.657e-04, size: 448, ETA: 0:42:08
2025-07-30 08:56:46.791 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 1.2, lr: 6.641e-04, size: 512, ETA: 0:42:05
2025-07-30 08:56:50.192 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 6.625e-04, size: 416, ETA: 0:42:01
2025-07-30 08:56:53.380 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 187/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.6, lr: 6.610e-04, size: 256, ETA: 0:41:57
2025-07-30 08:56:54.838 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:57:01.644 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:57:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:57:03.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4274
2025-07-30 08:57:03.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2641
2025-07-30 08:57:03.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1707
2025-07-30 08:57:03.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2874
2025-07-30 08:57:03.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:57:03.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:57:03.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-07-30 08:57:03.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-07-30 08:57:03.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.171
2025-07-30 08:57:03.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.287
2025-07-30 08:57:03.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:57:03.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:57:03.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:57:03.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:57:03.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:57:03.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:57:03.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:57:03.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:57:03.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:57:04.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:57:04.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:57:05.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:57:06.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:57:07.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:57:07.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:57:08.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:57:09.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:57:10.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:57:10.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 08:57:10.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 08:57:10.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:57:10.220 | 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: 8.32 ms

2025-07-30 08:57:10.226 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:57:10.302 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:57:10.378 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch188
2025-07-30 08:57:13.629 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 6.587e-04, size: 320, ETA: 0:41:52
2025-07-30 08:57:17.114 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 9.5, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 4.4, cls_loss: 1.1, lr: 6.571e-04, size: 448, ETA: 0:41:49
2025-07-30 08:57:20.329 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 6.555e-04, size: 544, ETA: 0:41:45
2025-07-30 08:57:23.766 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.8, lr: 6.539e-04, size: 256, ETA: 0:41:42
2025-07-30 08:57:26.985 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 6.524e-04, size: 384, ETA: 0:41:38
2025-07-30 08:57:30.194 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 188/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 6.508e-04, size: 320, ETA: 0:41:34
2025-07-30 08:57:31.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:57:38.659 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:57:39.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:57:40.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3886
2025-07-30 08:57:40.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3485
2025-07-30 08:57:40.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1482
2025-07-30 08:57:40.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2951
2025-07-30 08:57:40.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:57:40.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:57:40.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-07-30 08:57:40.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-07-30 08:57:40.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.148
2025-07-30 08:57:40.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.295
2025-07-30 08:57:40.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:57:40.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:57:40.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:57:40.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:57:40.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:57:40.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:57:40.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:57:40.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:57:40.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:57:41.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:57:42.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:57:43.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:57:44.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:57:45.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:57:46.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:57:47.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:57:47.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:57:48.832 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:57:48.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 08:57:48.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 08:57:48.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:57:48.840 | 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

2025-07-30 08:57:48.842 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:57:48.963 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:57:49.038 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch189
2025-07-30 08:57:52.549 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.7, lr: 6.485e-04, size: 384, ETA: 0:41:29
2025-07-30 08:57:55.784 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 6.469e-04, size: 288, ETA: 0:41:25
2025-07-30 08:57:58.926 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 6.454e-04, size: 288, ETA: 0:41:22
2025-07-30 08:58:02.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 0.7, lr: 6.438e-04, size: 448, ETA: 0:41:18
2025-07-30 08:58:05.589 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 6.422e-04, size: 320, ETA: 0:41:15
2025-07-30 08:58:08.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 189/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.407e-04, size: 480, ETA: 0:41:11
2025-07-30 08:58:10.260 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:58:17.066 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:58:17.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:58:17.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4369
2025-07-30 08:58:17.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4004
2025-07-30 08:58:17.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2554
2025-07-30 08:58:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3642
2025-07-30 08:58:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:58:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:58:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-07-30 08:58:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-07-30 08:58:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-07-30 08:58:17.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.364
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:58:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:58:18.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:58:18.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:58:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:58:19.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:58:19.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:58:19.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:58:20.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:58:20.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:58:20.686 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:58:20.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 08:58:20.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 08:58:20.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:58:20.693 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.80 ms, Average inference time: 8.38 ms

2025-07-30 08:58:20.699 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:58:20.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:58:20.847 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch190
2025-07-30 08:58:23.985 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 6.384e-04, size: 352, ETA: 0:41:06
2025-07-30 08:58:27.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 6.368e-04, size: 384, ETA: 0:41:02
2025-07-30 08:58:30.552 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 6.353e-04, size: 480, ETA: 0:40:58
2025-07-30 08:58:33.795 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 9.7, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 4.6, cls_loss: 0.8, lr: 6.337e-04, size: 352, ETA: 0:40:55
2025-07-30 08:58:37.012 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 6.321e-04, size: 512, ETA: 0:40:51
2025-07-30 08:58:40.300 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 190/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.9, lr: 6.306e-04, size: 288, ETA: 0:40:48
2025-07-30 08:58:41.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:58:48.588 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:58:49.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:58:49.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2524
2025-07-30 08:58:49.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2704
2025-07-30 08:58:49.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1125
2025-07-30 08:58:49.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2118
2025-07-30 08:58:49.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:58:49.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:58:49.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.252
2025-07-30 08:58:49.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-07-30 08:58:49.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.112
2025-07-30 08:58:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.212
2025-07-30 08:58:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:58:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:58:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:58:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:58:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:58:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:58:49.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:58:49.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:58:49.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:58:50.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:58:50.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:58:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:58:51.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:58:52.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:58:52.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:58:53.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:58:53.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:58:54.146 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:58:54.146 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:58:54.146 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.21
2025-07-30 08:58:54.146 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:58:54.153 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.73 ms, Average NMS time: 0.89 ms, Average inference time: 8.63 ms

2025-07-30 08:58:54.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:58:54.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:58:54.302 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch191
2025-07-30 08:58:57.557 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 6.283e-04, size: 512, ETA: 0:40:42
2025-07-30 08:59:00.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 6.267e-04, size: 544, ETA: 0:40:39
2025-07-30 08:59:04.260 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 9.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 4.4, cls_loss: 0.8, lr: 6.252e-04, size: 384, ETA: 0:40:35
2025-07-30 08:59:07.400 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, 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: 6.236e-04, size: 320, ETA: 0:40:32
2025-07-30 08:59:10.932 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 0.7, lr: 6.221e-04, size: 576, ETA: 0:40:28
2025-07-30 08:59:14.242 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 191/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.7, lr: 6.205e-04, size: 256, ETA: 0:40:25
2025-07-30 08:59:15.707 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:59:22.754 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:59:23.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:59:24.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2709
2025-07-30 08:59:24.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2654
2025-07-30 08:59:24.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0812
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2058
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.265
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.081
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.206
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:59:24.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:59:24.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:59:24.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:59:24.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:59:24.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:59:24.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:59:24.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 08:59:25.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 08:59:25.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 08:59:26.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 08:59:27.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 08:59:27.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 08:59:28.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 08:59:29.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 08:59:29.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 08:59:30.559 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 08:59:30.559 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.10
2025-07-30 08:59:30.559 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.21
2025-07-30 08:59:30.559 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 08:59:30.567 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.47 ms, Average NMS time: 0.93 ms, Average inference time: 8.39 ms

2025-07-30 08:59:30.568 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:59:30.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:59:30.716 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch192
2025-07-30 08:59:33.913 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.8, cls_loss: 0.9, lr: 6.183e-04, size: 512, ETA: 0:40:19
2025-07-30 08:59:37.242 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 6.167e-04, size: 480, ETA: 0:40:16
2025-07-30 08:59:40.686 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.9, lr: 6.152e-04, size: 576, ETA: 0:40:12
2025-07-30 08:59:44.119 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 6.136e-04, size: 320, ETA: 0:40:09
2025-07-30 08:59:47.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.9, lr: 6.121e-04, size: 384, ETA: 0:40:05
2025-07-30 08:59:50.466 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 192/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.8, lr: 6.105e-04, size: 512, ETA: 0:40:01
2025-07-30 08:59:52.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 08:59:58.693 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 08:59:59.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 08:59:59.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3581
2025-07-30 08:59:59.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3320
2025-07-30 08:59:59.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1523
2025-07-30 08:59:59.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2808
2025-07-30 08:59:59.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 08:59:59.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 08:59:59.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-07-30 08:59:59.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-07-30 08:59:59.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.152
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.281
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 08:59:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:00:00.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:00:00.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:00:01.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:00:01.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:00:01.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:00:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:00:02.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:00:03.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:00:03.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:00:03.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 09:00:03.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 09:00:03.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:00:03.599 | 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

2025-07-30 09:00:03.600 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:00:03.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:00:03.799 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch193
2025-07-30 09:00:06.859 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.9, lr: 6.083e-04, size: 320, ETA: 0:39:56
2025-07-30 09:00:10.248 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, 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: 6.067e-04, size: 544, ETA: 0:39:53
2025-07-30 09:00:13.489 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 6.052e-04, size: 320, ETA: 0:39:49
2025-07-30 09:00:16.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.7, lr: 6.036e-04, size: 320, ETA: 0:39:45
2025-07-30 09:00:20.038 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 6.021e-04, size: 544, ETA: 0:39:42
2025-07-30 09:00:23.189 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 193/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.8, cls_loss: 0.8, lr: 6.006e-04, size: 320, ETA: 0:39:38
2025-07-30 09:00:24.584 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:00:31.407 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:00:32.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:00:33.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4497
2025-07-30 09:00:33.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3565
2025-07-30 09:00:33.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1855
2025-07-30 09:00:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3306
2025-07-30 09:00:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:00:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:00:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-07-30 09:00:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-07-30 09:00:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-07-30 09:00:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.331
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:00:33.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:00:34.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:00:35.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:00:36.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:00:37.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:00:38.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:00:39.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:00:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:00:41.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:00:41.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:00:41.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:00:41.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 09:00:41.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:00:41.921 | 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

2025-07-30 09:00:41.922 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:00:42.041 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:00:42.121 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch194
2025-07-30 09:00:45.258 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 5.983e-04, size: 288, ETA: 0:39:33
2025-07-30 09:00:48.599 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, 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: 5.968e-04, size: 320, ETA: 0:39:29
2025-07-30 09:00:51.813 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 5.953e-04, size: 352, ETA: 0:39:26
2025-07-30 09:00:55.125 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, 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: 5.937e-04, size: 320, ETA: 0:39:22
2025-07-30 09:00:58.179 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, 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: 5.922e-04, size: 320, ETA: 0:39:18
2025-07-30 09:01:01.303 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 194/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 5.907e-04, size: 320, ETA: 0:39:15
2025-07-30 09:01:02.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:01:09.608 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:01:10.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:01:11.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4456
2025-07-30 09:01:11.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4252
2025-07-30 09:01:11.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2271
2025-07-30 09:01:11.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3660
2025-07-30 09:01:11.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:01:11.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:01:11.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-07-30 09:01:11.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-07-30 09:01:11.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-07-30 09:01:11.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:01:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:01:12.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:01:12.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:01:13.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:01:14.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:01:14.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:01:15.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:01:16.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:01:17.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:01:17.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:01:17.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:01:17.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:01:17.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:01:17.876 | 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: 8.34 ms

2025-07-30 09:01:17.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:01:17.987 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:01:18.092 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch195
2025-07-30 09:01:21.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.9, lr: 5.884e-04, size: 352, ETA: 0:39:09
2025-07-30 09:01:24.379 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 5.869e-04, size: 256, ETA: 0:39:06
2025-07-30 09:01:27.813 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.7, lr: 5.854e-04, size: 544, ETA: 0:39:02
2025-07-30 09:01:31.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.7, lr: 5.838e-04, size: 480, ETA: 0:38:59
2025-07-30 09:01:34.465 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.9, lr: 5.823e-04, size: 320, ETA: 0:38:55
2025-07-30 09:01:37.799 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 195/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.9, lr: 5.808e-04, size: 320, ETA: 0:38:52
2025-07-30 09:01:39.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:01:45.883 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:01:46.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:01:46.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4838
2025-07-30 09:01:46.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4305
2025-07-30 09:01:46.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2865
2025-07-30 09:01:46.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4002
2025-07-30 09:01:46.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:01:46.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:01:46.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-07-30 09:01:46.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-07-30 09:01:46.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-07-30 09:01:46.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-07-30 09:01:46.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:01:46.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:01:46.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:01:46.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:01:46.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:01:46.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:01:46.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:01:46.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:01:46.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:01:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:01:47.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:01:48.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:01:48.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:01:48.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:01:49.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:01:49.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:01:50.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:01:50.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:01:50.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 09:01:50.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-07-30 09:01:50.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:01:50.614 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.84 ms, Average inference time: 8.27 ms

2025-07-30 09:01:50.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:01:50.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:01:50.849 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch196
2025-07-30 09:01:54.204 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 5.786e-04, size: 320, ETA: 0:38:46
2025-07-30 09:01:57.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 5.771e-04, size: 480, ETA: 0:38:43
2025-07-30 09:02:01.101 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.7, lr: 5.755e-04, size: 416, ETA: 0:38:39
2025-07-30 09:02:04.477 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 5.740e-04, size: 544, ETA: 0:38:36
2025-07-30 09:02:07.827 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, 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: 5.725e-04, size: 448, ETA: 0:38:32
2025-07-30 09:02:11.101 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 196/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.9, lr: 5.710e-04, size: 256, ETA: 0:38:29
2025-07-30 09:02:12.513 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:02:19.451 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:02:20.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:02:21.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5235
2025-07-30 09:02:21.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4587
2025-07-30 09:02:21.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2780
2025-07-30 09:02:21.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4200
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.420
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:02:21.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:02:21.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:02:21.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:02:21.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:02:21.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:02:22.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:02:23.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:02:24.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:02:25.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:02:25.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:02:26.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:02:27.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:02:28.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:02:29.458 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:02:29.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-07-30 09:02:29.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-07-30 09:02:29.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:02:29.466 | 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

2025-07-30 09:02:29.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:02:29.550 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:02:29.627 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch197
2025-07-30 09:02:33.021 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 1.9, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 0.6, lr: 5.688e-04, size: 576, ETA: 0:38:24
2025-07-30 09:02:36.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.9, lr: 5.673e-04, size: 544, ETA: 0:38:20
2025-07-30 09:02:39.851 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.2, l1_loss: 1.6, conf_loss: 3.3, cls_loss: 0.7, lr: 5.658e-04, size: 512, ETA: 0:38:17
2025-07-30 09:02:43.277 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.6, lr: 5.642e-04, size: 448, ETA: 0:38:13
2025-07-30 09:02:46.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 1.2, lr: 5.627e-04, size: 288, ETA: 0:38:09
2025-07-30 09:02:49.766 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 197/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 5.612e-04, size: 384, ETA: 0:38:06
2025-07-30 09:02:51.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:02:57.855 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:02:59.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:03:00.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5027
2025-07-30 09:03:00.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4082
2025-07-30 09:03:00.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2169
2025-07-30 09:03:00.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3759
2025-07-30 09:03:00.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.376
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:03:00.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:03:00.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:03:01.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:03:02.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:03:03.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:03:04.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:03:05.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:03:06.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:03:07.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:03:08.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:03:09.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:03:09.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:03:09.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 09:03:09.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:03:09.873 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.89 ms, Average inference time: 8.32 ms

2025-07-30 09:03:09.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:03:09.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:03:10.028 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch198
2025-07-30 09:03:13.472 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.5, lr: 5.590e-04, size: 480, ETA: 0:38:01
2025-07-30 09:03:16.828 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 5.575e-04, size: 416, ETA: 0:37:57
2025-07-30 09:03:20.181 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 5.560e-04, size: 256, ETA: 0:37:54
2025-07-30 09:03:23.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.7, lr: 5.545e-04, size: 512, ETA: 0:37:50
2025-07-30 09:03:26.723 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 5.530e-04, size: 384, ETA: 0:37:46
2025-07-30 09:03:29.967 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 198/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 5.515e-04, size: 256, ETA: 0:37:43
2025-07-30 09:03:31.441 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:03:38.295 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:03:39.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:03:40.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4406
2025-07-30 09:03:40.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3793
2025-07-30 09:03:40.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2282
2025-07-30 09:03:40.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3494
2025-07-30 09:03:40.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:03:40.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:03:40.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.228
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.349
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:03:40.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:03:40.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:03:41.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:03:43.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:03:44.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:03:45.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:03:46.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:03:47.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:03:48.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:03:49.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:03:51.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:03:51.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:03:51.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:03:51.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:03:51.043 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.57 ms, Average NMS time: 0.93 ms, Average inference time: 8.50 ms

2025-07-30 09:03:51.045 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:03:51.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:03:51.198 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch199
2025-07-30 09:03:54.280 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.004s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 4.0, cls_loss: 0.8, lr: 5.493e-04, size: 384, ETA: 0:37:38
2025-07-30 09:03:57.624 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.8, lr: 5.478e-04, size: 480, ETA: 0:37:34
2025-07-30 09:04:00.980 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 5.463e-04, size: 576, ETA: 0:37:31
2025-07-30 09:04:04.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.8, lr: 5.449e-04, size: 320, ETA: 0:37:27
2025-07-30 09:04:07.490 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 5.434e-04, size: 256, ETA: 0:37:23
2025-07-30 09:04:10.806 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 199/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 4.1, cls_loss: 0.8, lr: 5.419e-04, size: 256, ETA: 0:37:20
2025-07-30 09:04:12.201 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:04:19.008 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:04:19.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:04:20.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3917
2025-07-30 09:04:20.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2956
2025-07-30 09:04:20.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1534
2025-07-30 09:04:20.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2802
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.153
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.280
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:04:20.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:04:20.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:04:20.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:04:20.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:04:20.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:04:21.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:04:21.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:04:22.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:04:22.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:04:23.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:04:24.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:04:24.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:04:25.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:04:25.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:04:25.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 09:04:25.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 09:04:25.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:04:25.938 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.89 ms, Average inference time: 8.27 ms

2025-07-30 09:04:25.940 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:04:26.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:04:26.144 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch200
2025-07-30 09:04:29.501 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.9, lr: 5.397e-04, size: 512, ETA: 0:37:15
2025-07-30 09:04:32.890 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 5.382e-04, size: 288, ETA: 0:37:11
2025-07-30 09:04:36.028 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 5.367e-04, size: 544, ETA: 0:37:08
2025-07-30 09:04:39.390 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 3.7, iou_loss: 1.1, l1_loss: 0.4, conf_loss: 1.8, cls_loss: 0.4, lr: 5.352e-04, size: 448, ETA: 0:37:04
2025-07-30 09:04:42.756 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 5.337e-04, size: 416, ETA: 0:37:01
2025-07-30 09:04:45.998 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 200/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 5.323e-04, size: 384, ETA: 0:36:57
2025-07-30 09:04:47.393 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:04:54.391 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:04:55.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:04:56.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4886
2025-07-30 09:04:56.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3839
2025-07-30 09:04:56.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2375
2025-07-30 09:04:56.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3700
2025-07-30 09:04:56.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:04:56.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:04:56.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-07-30 09:04:56.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-07-30 09:04:56.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-07-30 09:04:56.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:04:56.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:04:57.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:04:58.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:04:58.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:04:59.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:05:00.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:05:01.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:05:02.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:05:03.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:05:04.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:05:04.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:05:04.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:05:04.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:05:04.065 | 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

2025-07-30 09:05:04.066 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:05:04.185 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:05:04.260 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch201
2025-07-30 09:05:07.444 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, 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: 5.301e-04, size: 416, ETA: 0:36:52
2025-07-30 09:05:10.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 5.286e-04, size: 288, ETA: 0:36:48
2025-07-30 09:05:14.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 5.271e-04, size: 576, ETA: 0:36:45
2025-07-30 09:05:17.512 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 5.257e-04, size: 576, ETA: 0:36:41
2025-07-30 09:05:20.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 5.242e-04, size: 384, ETA: 0:36:38
2025-07-30 09:05:23.995 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 201/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 5.227e-04, size: 320, ETA: 0:36:34
2025-07-30 09:05:25.569 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:05:32.472 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:05:33.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:05:34.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3044
2025-07-30 09:05:34.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2865
2025-07-30 09:05:34.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1376
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2428
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.138
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.243
2025-07-30 09:05:34.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:05:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:05:35.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:05:36.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:05:37.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:05:38.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:05:38.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:05:39.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:05:40.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:05:41.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:05:42.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:05:42.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:05:42.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-07-30 09:05:42.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:05:42.382 | 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

2025-07-30 09:05:42.383 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:05:42.455 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:05:42.533 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch202
2025-07-30 09:05:45.667 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 1.0, lr: 5.206e-04, size: 384, ETA: 0:36:29
2025-07-30 09:05:49.011 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 5.191e-04, size: 352, ETA: 0:36:25
2025-07-30 09:05:52.510 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 5.176e-04, size: 448, ETA: 0:36:22
2025-07-30 09:05:55.781 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 5.162e-04, size: 352, ETA: 0:36:18
2025-07-30 09:05:59.089 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.7, lr: 5.147e-04, size: 288, ETA: 0:36:15
2025-07-30 09:06:02.415 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 202/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 5.132e-04, size: 384, ETA: 0:36:11
2025-07-30 09:06:03.870 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:06:10.830 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:06:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:06:12.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4467
2025-07-30 09:06:12.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3736
2025-07-30 09:06:12.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2037
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3413
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.204
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.341
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:06:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:06:12.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:06:12.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:06:12.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:06:12.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:06:12.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:06:12.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:06:13.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:06:13.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:06:14.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:06:15.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:06:15.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:06:16.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:06:17.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:06:18.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:06:18.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:06:18.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:06:18.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:06:18.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:06:18.695 | 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

2025-07-30 09:06:18.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:06:18.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:06:18.850 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch203
2025-07-30 09:06:22.229 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.6, lr: 5.111e-04, size: 256, ETA: 0:36:06
2025-07-30 09:06:25.500 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 9.9, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 5.0, cls_loss: 1.0, lr: 5.096e-04, size: 512, ETA: 0:36:03
2025-07-30 09:06:28.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, 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: 5.082e-04, size: 288, ETA: 0:35:59
2025-07-30 09:06:32.196 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 5.067e-04, size: 416, ETA: 0:35:56
2025-07-30 09:06:35.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 5.052e-04, size: 384, ETA: 0:35:52
2025-07-30 09:06:38.820 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 203/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 5.038e-04, size: 480, ETA: 0:35:48
2025-07-30 09:06:40.217 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:06:46.993 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:06:47.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:06:48.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4561
2025-07-30 09:06:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3680
2025-07-30 09:06:48.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1856
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3365
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:06:48.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:06:48.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:06:48.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:06:48.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:06:48.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:06:48.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:06:48.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:06:49.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:06:50.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:06:50.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:06:51.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:06:52.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:06:52.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:06:53.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:06:54.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:06:54.763 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:06:54.763 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:06:54.764 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:06:54.764 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:06:54.771 | 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

2025-07-30 09:06:54.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:06:54.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:06:54.975 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch204
2025-07-30 09:06:58.118 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 5.017e-04, size: 544, ETA: 0:35:43
2025-07-30 09:07:01.715 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 8.9, iou_loss: 2.2, l1_loss: 1.5, conf_loss: 4.2, cls_loss: 1.0, lr: 5.002e-04, size: 480, ETA: 0:35:40
2025-07-30 09:07:04.957 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 4.988e-04, size: 384, ETA: 0:35:36
2025-07-30 09:07:08.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.4, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 4.973e-04, size: 352, ETA: 0:35:33
2025-07-30 09:07:11.560 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, 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: 4.959e-04, size: 448, ETA: 0:35:29
2025-07-30 09:07:14.786 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 204/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 4.944e-04, size: 512, ETA: 0:35:26
2025-07-30 09:07:16.271 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:07:22.972 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:07:23.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:07:24.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4881
2025-07-30 09:07:24.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4383
2025-07-30 09:07:24.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2614
2025-07-30 09:07:24.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3959
2025-07-30 09:07:24.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:07:24.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:07:24.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-07-30 09:07:24.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-07-30 09:07:24.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.261
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:07:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:07:25.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:07:26.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:07:26.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:07:27.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:07:28.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:07:28.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:07:29.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:07:30.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:07:30.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:07:30.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 09:07:30.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-07-30 09:07:30.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:07:30.930 | 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

2025-07-30 09:07:30.932 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:07:31.005 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:07:31.083 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch205
2025-07-30 09:07:34.474 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.1, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 4.923e-04, size: 384, ETA: 0:35:20
2025-07-30 09:07:37.819 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 4.909e-04, size: 512, ETA: 0:35:17
2025-07-30 09:07:41.216 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 4.894e-04, size: 448, ETA: 0:35:13
2025-07-30 09:07:44.588 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.6, lr: 4.880e-04, size: 576, ETA: 0:35:10
2025-07-30 09:07:47.898 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 4.865e-04, size: 288, ETA: 0:35:06
2025-07-30 09:07:51.084 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 205/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 4.851e-04, size: 288, ETA: 0:35:03
2025-07-30 09:07:52.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:07:59.272 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:08:00.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:08:00.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3657
2025-07-30 09:08:01.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2569
2025-07-30 09:08:01.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1619
2025-07-30 09:08:01.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2615
2025-07-30 09:08:01.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:08:01.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:08:01.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-07-30 09:08:01.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-07-30 09:08:01.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.162
2025-07-30 09:08:01.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.261
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:08:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:08:01.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:08:02.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:08:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:08:03.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:08:04.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:08:05.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:08:06.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:08:06.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:08:07.530 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:08:07.530 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.11
2025-07-30 09:08:07.530 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 09:08:07.530 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:08:07.537 | 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

2025-07-30 09:08:07.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:08:07.622 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:08:07.711 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch206
2025-07-30 09:08:10.762 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 4.830e-04, size: 416, ETA: 0:34:58
2025-07-30 09:08:14.000 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 4.816e-04, size: 352, ETA: 0:34:54
2025-07-30 09:08:17.403 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 1.0, lr: 4.801e-04, size: 576, ETA: 0:34:50
2025-07-30 09:08:20.829 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.8, lr: 4.787e-04, size: 384, ETA: 0:34:47
2025-07-30 09:08:24.203 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.8, lr: 4.773e-04, size: 448, ETA: 0:34:43
2025-07-30 09:08:27.597 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 206/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, 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: 4.758e-04, size: 480, ETA: 0:34:40
2025-07-30 09:08:29.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:08:36.141 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:08:36.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:08:37.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5213
2025-07-30 09:08:37.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4327
2025-07-30 09:08:37.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2741
2025-07-30 09:08:37.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4094
2025-07-30 09:08:37.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.274
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:08:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:08:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:08:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:08:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:08:38.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:08:38.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:08:39.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:08:40.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:08:40.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:08:41.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:08:42.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:08:42.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:08:43.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:08:43.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-07-30 09:08:43.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-07-30 09:08:43.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:08:43.549 | 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

2025-07-30 09:08:43.551 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:08:43.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:08:43.746 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch207
2025-07-30 09:08:47.083 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.6, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 0.8, lr: 4.738e-04, size: 352, ETA: 0:34:35
2025-07-30 09:08:50.521 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 9.7, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 4.4, cls_loss: 0.7, lr: 4.723e-04, size: 288, ETA: 0:34:31
2025-07-30 09:08:53.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, 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: 4.709e-04, size: 384, ETA: 0:34:28
2025-07-30 09:08:57.190 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 1.1, lr: 4.695e-04, size: 416, ETA: 0:34:24
2025-07-30 09:09:00.476 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 4.681e-04, size: 448, ETA: 0:34:21
2025-07-30 09:09:03.602 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 207/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 4.666e-04, size: 352, ETA: 0:34:17
2025-07-30 09:09:05.047 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:09:11.821 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:09:12.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:09:13.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4564
2025-07-30 09:09:13.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3950
2025-07-30 09:09:13.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2048
2025-07-30 09:09:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3521
2025-07-30 09:09:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:09:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:09:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-07-30 09:09:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-07-30 09:09:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.205
2025-07-30 09:09:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:09:13.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:09:14.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:09:14.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:09:15.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:09:16.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:09:16.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:09:17.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:09:18.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:09:18.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:09:19.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:09:19.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:09:19.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:09:19.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:09:19.463 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.85 ms, Average inference time: 8.37 ms

2025-07-30 09:09:19.464 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:09:19.535 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:09:19.660 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch208
2025-07-30 09:09:22.731 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 4.646e-04, size: 416, ETA: 0:34:12
2025-07-30 09:09:26.056 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 4.632e-04, size: 448, ETA: 0:34:09
2025-07-30 09:09:29.214 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 4.618e-04, size: 512, ETA: 0:34:05
2025-07-30 09:09:32.534 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.4, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.6, lr: 4.603e-04, size: 576, ETA: 0:34:01
2025-07-30 09:09:35.845 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 4.589e-04, size: 544, ETA: 0:33:58
2025-07-30 09:09:39.220 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 208/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 0.7, lr: 4.575e-04, size: 544, ETA: 0:33:54
2025-07-30 09:09:40.670 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:09:47.575 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:09:48.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:09:48.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4498
2025-07-30 09:09:48.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4101
2025-07-30 09:09:48.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2400
2025-07-30 09:09:48.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3666
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.367
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:09:48.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:09:48.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:09:48.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:09:48.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:09:48.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:09:48.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:09:48.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:09:49.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:09:49.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:09:50.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:09:50.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:09:50.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:09:51.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:09:51.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:09:52.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:09:52.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:09:52.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:09:52.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:09:52.118 | 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

2025-07-30 09:09:52.119 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:09:52.202 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:09:52.293 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch209
2025-07-30 09:09:55.330 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 4.555e-04, size: 288, ETA: 0:33:49
2025-07-30 09:09:58.515 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.8, cls_loss: 0.6, lr: 4.541e-04, size: 352, ETA: 0:33:46
2025-07-30 09:10:01.825 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 4.527e-04, size: 416, ETA: 0:33:42
2025-07-30 09:10:05.253 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, 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: 4.512e-04, size: 256, ETA: 0:33:39
2025-07-30 09:10:08.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.8, lr: 4.498e-04, size: 576, ETA: 0:33:35
2025-07-30 09:10:12.006 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 209/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 15.2, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 15.2, cls_loss: 0.0, lr: 4.484e-04, size: 288, ETA: 0:33:32
2025-07-30 09:10:13.369 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:10:20.302 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:10:21.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:10:21.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4447
2025-07-30 09:10:21.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4016
2025-07-30 09:10:21.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2231
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3565
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-07-30 09:10:21.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:10:21.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:10:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:10:22.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:10:22.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:10:23.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:10:23.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:10:24.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:10:24.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:10:25.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:10:25.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:10:25.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:10:25.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:10:25.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:10:25.539 | 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

2025-07-30 09:10:25.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:10:25.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:10:25.797 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch210
2025-07-30 09:10:28.916 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 4.5, cls_loss: 0.8, lr: 4.464e-04, size: 480, ETA: 0:33:26
2025-07-30 09:10:32.407 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 1.2, lr: 4.450e-04, size: 352, ETA: 0:33:23
2025-07-30 09:10:35.687 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 4.436e-04, size: 256, ETA: 0:33:19
2025-07-30 09:10:38.869 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.7, lr: 4.422e-04, size: 480, ETA: 0:33:16
2025-07-30 09:10:42.121 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 4.408e-04, size: 352, ETA: 0:33:12
2025-07-30 09:10:45.417 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 210/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 4.394e-04, size: 288, ETA: 0:33:09
2025-07-30 09:10:46.795 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:10:53.823 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:10:55.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:10:56.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5070
2025-07-30 09:10:56.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4501
2025-07-30 09:10:56.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2549
2025-07-30 09:10:56.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4040
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.404
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:10:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:10:56.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:10:56.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:10:56.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:10:56.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:10:56.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:10:56.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:10:57.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:10:58.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:10:59.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:11:00.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:11:01.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:11:02.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:11:03.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:11:04.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:11:05.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:11:05.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:11:05.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-07-30 09:11:05.637 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:11:05.648 | 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

2025-07-30 09:11:05.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:11:05.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:11:05.821 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch211
2025-07-30 09:11:09.056 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.8, lr: 4.374e-04, size: 576, ETA: 0:33:04
2025-07-30 09:11:12.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 4.360e-04, size: 512, ETA: 0:33:00
2025-07-30 09:11:15.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.9, lr: 4.346e-04, size: 352, ETA: 0:32:57
2025-07-30 09:11:19.103 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, 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: 4.333e-04, size: 288, ETA: 0:32:53
2025-07-30 09:11:22.462 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 4.319e-04, size: 544, ETA: 0:32:50
2025-07-30 09:11:25.648 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 211/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 4.305e-04, size: 416, ETA: 0:32:46
2025-07-30 09:11:27.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:11:33.895 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:11:34.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:11:35.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4446
2025-07-30 09:11:35.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4223
2025-07-30 09:11:35.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1951
2025-07-30 09:11:35.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3540
2025-07-30 09:11:35.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:11:35.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:11:35.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.354
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:11:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:11:35.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:11:35.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:11:36.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:11:37.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:11:37.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:11:38.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:11:39.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:11:39.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:11:40.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:11:40.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:11:40.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:11:40.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:11:40.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:11:40.901 | 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

2025-07-30 09:11:40.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:11:41.007 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:11:41.081 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch212
2025-07-30 09:11:44.215 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 4.285e-04, size: 384, ETA: 0:32:41
2025-07-30 09:11:47.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.9, lr: 4.271e-04, size: 576, ETA: 0:32:37
2025-07-30 09:11:51.031 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, 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: 4.257e-04, size: 512, ETA: 0:32:34
2025-07-30 09:11:54.418 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.7, lr: 4.244e-04, size: 576, ETA: 0:32:31
2025-07-30 09:11:57.661 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 4.230e-04, size: 288, ETA: 0:32:27
2025-07-30 09:12:00.938 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 212/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 4.216e-04, size: 320, ETA: 0:32:24
2025-07-30 09:12:02.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:12:09.290 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:12:10.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:12:11.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3258
2025-07-30 09:12:11.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3170
2025-07-30 09:12:11.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1517
2025-07-30 09:12:11.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2648
2025-07-30 09:12:11.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.317
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.152
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.265
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:12:11.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:12:11.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:12:11.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:12:11.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:12:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:12:13.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:12:13.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:12:14.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:12:15.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:12:16.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:12:16.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:12:17.485 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:12:17.485 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:12:17.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 09:12:17.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:12:17.493 | 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: 8.35 ms

2025-07-30 09:12:17.495 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:12:17.579 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:12:17.669 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch213
2025-07-30 09:12:21.010 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 0.7, lr: 4.196e-04, size: 544, ETA: 0:32:19
2025-07-30 09:12:24.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, 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: 4.183e-04, size: 480, ETA: 0:32:15
2025-07-30 09:12:27.692 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 4.169e-04, size: 576, ETA: 0:32:12
2025-07-30 09:12:30.927 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.6, lr: 4.155e-04, size: 352, ETA: 0:32:08
2025-07-30 09:12:34.370 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.6, lr: 4.142e-04, size: 576, ETA: 0:32:05
2025-07-30 09:12:37.699 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 213/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.7, cls_loss: 1.2, lr: 4.128e-04, size: 256, ETA: 0:32:01
2025-07-30 09:12:39.198 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:12:46.022 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:12:47.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:12:47.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3360
2025-07-30 09:12:47.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2942
2025-07-30 09:12:47.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1857
2025-07-30 09:12:47.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2720
2025-07-30 09:12:47.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:12:47.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:12:47.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.186
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.272
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:12:47.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:12:47.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:12:48.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:12:49.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:12:50.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:12:50.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:12:51.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:12:52.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:12:53.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:12:54.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:12:54.789 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:12:54.789 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:12:54.789 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 09:12:54.789 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:12:54.797 | 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: 8.30 ms

2025-07-30 09:12:54.798 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:12:54.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:12:54.949 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch214
2025-07-30 09:12:58.121 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 4.109e-04, size: 448, ETA: 0:31:56
2025-07-30 09:13:01.357 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.9, lr: 4.095e-04, size: 256, ETA: 0:31:52
2025-07-30 09:13:04.539 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, 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: 4.081e-04, size: 256, ETA: 0:31:49
2025-07-30 09:13:07.974 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.7, lr: 4.068e-04, size: 352, ETA: 0:31:45
2025-07-30 09:13:11.452 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 4.054e-04, size: 416, ETA: 0:31:42
2025-07-30 09:13:14.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 214/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 9.1, iou_loss: 2.6, l1_loss: 1.6, conf_loss: 4.0, cls_loss: 0.9, lr: 4.041e-04, size: 544, ETA: 0:31:38
2025-07-30 09:13:16.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:13:23.265 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:13:23.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:13:24.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4254
2025-07-30 09:13:24.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3176
2025-07-30 09:13:24.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1850
2025-07-30 09:13:24.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3093
2025-07-30 09:13:24.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:13:24.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:13:24.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.185
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:13:24.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:13:24.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:13:24.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:13:24.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:13:24.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:13:24.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:13:25.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:13:25.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:13:26.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:13:26.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:13:27.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:13:27.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:13:28.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:13:28.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:13:28.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:13:28.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 09:13:28.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:13:28.924 | 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: 8.31 ms

2025-07-30 09:13:28.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:13:29.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:13:29.078 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch215
2025-07-30 09:13:32.377 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, 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: 4.021e-04, size: 320, ETA: 0:31:33
2025-07-30 09:13:35.581 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 4.008e-04, size: 512, ETA: 0:31:30
2025-07-30 09:13:38.811 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 3.994e-04, size: 320, ETA: 0:31:26
2025-07-30 09:13:42.358 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 3.981e-04, size: 544, ETA: 0:31:23
2025-07-30 09:13:45.644 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 1.0, lr: 3.968e-04, size: 256, ETA: 0:31:19
2025-07-30 09:13:48.863 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 215/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.8, lr: 3.954e-04, size: 352, ETA: 0:31:16
2025-07-30 09:13:50.451 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:13:57.233 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:13:58.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:13:58.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4355
2025-07-30 09:13:58.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3703
2025-07-30 09:13:58.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2110
2025-07-30 09:13:58.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3389
2025-07-30 09:13:58.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:13:58.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:13:58.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-07-30 09:13:58.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-07-30 09:13:58.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-07-30 09:13:58.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:13:58.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:13:58.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:13:59.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:14:00.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:14:00.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:14:01.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:14:02.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:14:02.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:14:03.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:14:03.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:14:04.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:14:04.595 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:14:04.595 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:14:04.595 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:14:04.602 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.90 ms, Average inference time: 8.50 ms

2025-07-30 09:14:04.609 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:14:04.685 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:14:04.761 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch216
2025-07-30 09:14:07.962 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.5, iou_loss: 2.9, l1_loss: 1.8, conf_loss: 3.1, cls_loss: 0.8, lr: 3.935e-04, size: 512, ETA: 0:31:11
2025-07-30 09:14:11.375 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 3.922e-04, size: 544, ETA: 0:31:07
2025-07-30 09:14:14.505 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.8, lr: 3.908e-04, size: 256, ETA: 0:31:04
2025-07-30 09:14:17.755 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.895e-04, size: 416, ETA: 0:31:00
2025-07-30 09:14:21.161 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 3.882e-04, size: 416, ETA: 0:30:57
2025-07-30 09:14:24.292 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 216/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.9, lr: 3.868e-04, size: 288, ETA: 0:30:53
2025-07-30 09:14:25.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:14:32.443 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:14:33.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:14:33.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3501
2025-07-30 09:14:33.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2727
2025-07-30 09:14:33.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1588
2025-07-30 09:14:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2605
2025-07-30 09:14:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:14:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:14:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-07-30 09:14:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.273
2025-07-30 09:14:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.159
2025-07-30 09:14:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.261
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:14:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:14:34.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:14:35.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:14:35.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:14:36.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:14:37.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:14:37.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:14:38.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:14:39.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:14:39.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:14:39.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:14:39.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 09:14:39.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:14:39.791 | 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

2025-07-30 09:14:39.795 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:14:39.919 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:14:39.994 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch217
2025-07-30 09:14:43.343 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.6, lr: 3.849e-04, size: 352, ETA: 0:30:48
2025-07-30 09:14:46.697 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 3.836e-04, size: 448, ETA: 0:30:45
2025-07-30 09:14:50.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 3.823e-04, size: 544, ETA: 0:30:41
2025-07-30 09:14:53.592 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.9, lr: 3.809e-04, size: 448, ETA: 0:30:38
2025-07-30 09:14:56.938 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 3.2, cls_loss: 1.1, lr: 3.796e-04, size: 288, ETA: 0:30:34
2025-07-30 09:15:00.237 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 217/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 3.783e-04, size: 576, ETA: 0:30:31
2025-07-30 09:15:01.893 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:15:08.685 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:15:09.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:15:09.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4292
2025-07-30 09:15:09.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3784
2025-07-30 09:15:09.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2156
2025-07-30 09:15:09.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3411
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.341
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:15:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:15:09.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:15:09.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:15:09.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:15:09.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:15:09.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:15:10.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:15:10.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:15:10.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:15:11.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:15:11.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:15:12.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:15:12.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:15:12.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:15:13.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:15:13.325 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:15:13.325 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:15:13.325 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:15:13.332 | 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

2025-07-30 09:15:13.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:15:13.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:15:13.486 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch218
2025-07-30 09:15:16.666 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.6, lr: 3.764e-04, size: 512, ETA: 0:30:26
2025-07-30 09:15:19.961 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 3.751e-04, size: 480, ETA: 0:30:22
2025-07-30 09:15:23.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, 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: 3.738e-04, size: 288, ETA: 0:30:19
2025-07-30 09:15:26.462 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 9.8, iou_loss: 3.1, l1_loss: 1.7, conf_loss: 4.1, cls_loss: 0.9, lr: 3.725e-04, size: 512, ETA: 0:30:15
2025-07-30 09:15:29.732 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 3.712e-04, size: 320, ETA: 0:30:12
2025-07-30 09:15:32.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 218/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 2.1, cls_loss: 0.7, lr: 3.699e-04, size: 576, ETA: 0:30:08
2025-07-30 09:15:34.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:15:41.335 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:15:41.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:15:42.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4300
2025-07-30 09:15:42.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3503
2025-07-30 09:15:42.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1909
2025-07-30 09:15:42.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3237
2025-07-30 09:15:42.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:15:42.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.191
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.324
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:15:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:15:42.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:15:42.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:15:42.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:15:42.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:15:43.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:15:43.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:15:43.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:15:44.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:15:44.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:15:44.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:15:45.342 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:15:45.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:15:45.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 09:15:45.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:15:45.349 | 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

2025-07-30 09:15:45.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:15:45.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:15:45.499 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch219
2025-07-30 09:15:48.578 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.147s, 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: 3.680e-04, size: 352, ETA: 0:30:03
2025-07-30 09:15:51.774 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.6, lr: 3.667e-04, size: 384, ETA: 0:29:59
2025-07-30 09:15:55.105 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.8, lr: 3.654e-04, size: 288, ETA: 0:29:56
2025-07-30 09:15:58.297 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 3.641e-04, size: 416, ETA: 0:29:52
2025-07-30 09:16:01.517 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 3.628e-04, size: 320, ETA: 0:29:49
2025-07-30 09:16:04.670 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 219/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 3.615e-04, size: 384, ETA: 0:29:45
2025-07-30 09:16:06.086 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:16:12.932 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:16:13.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:16:14.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4894
2025-07-30 09:16:14.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4299
2025-07-30 09:16:14.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2237
2025-07-30 09:16:14.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3810
2025-07-30 09:16:14.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:16:14.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:16:14.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-07-30 09:16:14.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-07-30 09:16:14.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.224
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.381
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:16:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:16:14.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:16:14.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:16:14.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:16:15.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:16:16.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:16:16.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:16:17.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:16:17.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:16:18.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:16:18.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:16:19.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:16:19.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:16:19.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 09:16:19.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:16:19.561 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.89 ms, Average inference time: 8.32 ms

2025-07-30 09:16:19.562 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:16:19.632 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:16:19.708 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch220
2025-07-30 09:16:22.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.596e-04, size: 416, ETA: 0:29:40
2025-07-30 09:16:26.134 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.8, lr: 3.583e-04, size: 416, ETA: 0:29:37
2025-07-30 09:16:29.410 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.9, lr: 3.570e-04, size: 576, ETA: 0:29:33
2025-07-30 09:16:32.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 3.558e-04, size: 480, ETA: 0:29:30
2025-07-30 09:16:36.372 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 3.545e-04, size: 544, ETA: 0:29:26
2025-07-30 09:16:39.663 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 220/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, 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: 3.532e-04, size: 416, ETA: 0:29:23
2025-07-30 09:16:41.174 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:16:48.032 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:16:48.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:16:49.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2784
2025-07-30 09:16:49.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.1783
2025-07-30 09:16:49.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.0531
2025-07-30 09:16:49.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1699
2025-07-30 09:16:49.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.178
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.053
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.170
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:16:49.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:16:49.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:16:49.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:16:49.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:16:49.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:16:50.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:16:50.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:16:51.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:16:51.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:16:52.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:16:52.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:16:53.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:16:53.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:16:54.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:16:54.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.07
2025-07-30 09:16:54.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.17
2025-07-30 09:16:54.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:16:54.464 | 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

2025-07-30 09:16:54.465 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:16:54.545 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:16:54.674 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch221
2025-07-30 09:16:57.784 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 2.0, cls_loss: 0.7, lr: 3.513e-04, size: 544, ETA: 0:29:18
2025-07-30 09:17:01.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 3.501e-04, size: 384, ETA: 0:29:14
2025-07-30 09:17:04.577 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.9, lr: 3.488e-04, size: 448, ETA: 0:29:11
2025-07-30 09:17:07.738 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 3.475e-04, size: 256, ETA: 0:29:07
2025-07-30 09:17:11.229 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 3.462e-04, size: 576, ETA: 0:29:04
2025-07-30 09:17:14.618 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 221/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 3.450e-04, size: 544, ETA: 0:29:00
2025-07-30 09:17:16.177 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:17:22.997 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:17:23.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:17:24.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4941
2025-07-30 09:17:24.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4482
2025-07-30 09:17:24.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2517
2025-07-30 09:17:24.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3980
2025-07-30 09:17:24.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:17:24.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:17:24.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-07-30 09:17:24.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-07-30 09:17:24.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.252
2025-07-30 09:17:24.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:17:24.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:17:24.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:17:25.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:17:26.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:17:26.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:17:27.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:17:28.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:17:29.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:17:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:17:30.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:17:31.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:17:31.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 09:17:31.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-07-30 09:17:31.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:17:31.249 | 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

2025-07-30 09:17:31.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:17:31.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:17:31.473 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch222
2025-07-30 09:17:34.675 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 9.2, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 4.1, cls_loss: 0.8, lr: 3.431e-04, size: 416, ETA: 0:28:55
2025-07-30 09:17:37.908 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 3.419e-04, size: 448, ETA: 0:28:52
2025-07-30 09:17:41.502 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 1.4, lr: 3.406e-04, size: 512, ETA: 0:28:48
2025-07-30 09:17:44.934 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 3.393e-04, size: 384, ETA: 0:28:45
2025-07-30 09:17:48.223 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.9, lr: 3.381e-04, size: 544, ETA: 0:28:42
2025-07-30 09:17:51.658 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 222/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 3.4, cls_loss: 0.7, lr: 3.368e-04, size: 576, ETA: 0:28:38
2025-07-30 09:17:53.318 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:18:00.166 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:18:00.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:18:00.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3550
2025-07-30 09:18:01.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3372
2025-07-30 09:18:01.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1410
2025-07-30 09:18:01.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2777
2025-07-30 09:18:01.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:18:01.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:18:01.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-07-30 09:18:01.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-07-30 09:18:01.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.141
2025-07-30 09:18:01.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-07-30 09:18:01.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:18:01.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:18:01.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:18:01.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:18:01.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:18:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:18:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:18:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:18:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:18:01.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:18:01.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:18:02.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:18:02.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:18:03.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:18:03.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:18:03.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:18:04.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:18:04.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:18:04.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 09:18:04.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 09:18:04.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:18:04.500 | 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

2025-07-30 09:18:04.501 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:18:04.626 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:18:04.701 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch223
2025-07-30 09:18:07.901 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 3.350e-04, size: 544, ETA: 0:28:33
2025-07-30 09:18:11.239 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 3.337e-04, size: 320, ETA: 0:28:30
2025-07-30 09:18:14.646 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.8, lr: 3.325e-04, size: 512, ETA: 0:28:26
2025-07-30 09:18:17.971 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.2, l1_loss: 0.3, conf_loss: 3.0, cls_loss: 0.4, lr: 3.312e-04, size: 256, ETA: 0:28:23
2025-07-30 09:18:21.271 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 3.300e-04, size: 448, ETA: 0:28:19
2025-07-30 09:18:24.461 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 223/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 3.288e-04, size: 320, ETA: 0:28:16
2025-07-30 09:18:26.031 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:18:32.820 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:18:33.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:18:34.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2119
2025-07-30 09:18:34.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2189
2025-07-30 09:18:34.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1306
2025-07-30 09:18:34.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.1871
2025-07-30 09:18:34.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:18:34.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:18:34.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.212
2025-07-30 09:18:34.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.219
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.131
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.187
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:18:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:18:34.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:18:34.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:18:34.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:18:34.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:18:35.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:18:35.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:18:36.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:18:36.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:18:37.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:18:37.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:18:38.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:18:39.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:18:39.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.08
2025-07-30 09:18:39.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.19
2025-07-30 09:18:39.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:18:39.037 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.50 ms, Average NMS time: 0.90 ms, Average inference time: 8.39 ms

2025-07-30 09:18:39.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:18:39.116 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:18:39.192 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch224
2025-07-30 09:18:42.380 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 9.2, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 4.0, cls_loss: 0.7, lr: 3.269e-04, size: 256, ETA: 0:28:11
2025-07-30 09:18:45.621 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, 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: 3.257e-04, size: 384, ETA: 0:28:07
2025-07-30 09:18:48.983 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.8, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.8, cls_loss: 0.8, lr: 3.245e-04, size: 576, ETA: 0:28:04
2025-07-30 09:18:52.316 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 3.232e-04, size: 288, ETA: 0:28:00
2025-07-30 09:18:55.697 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 3.220e-04, size: 384, ETA: 0:27:57
2025-07-30 09:18:59.150 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 224/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 1.2, lr: 3.208e-04, size: 512, ETA: 0:27:53
2025-07-30 09:19:00.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:19:07.572 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:19:08.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:19:09.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5107
2025-07-30 09:19:09.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3926
2025-07-30 09:19:09.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2814
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3949
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-07-30 09:19:09.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:19:09.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:19:09.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:19:10.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:19:11.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:19:11.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:19:12.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:19:13.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:19:14.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:19:14.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:19:15.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:19:15.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-07-30 09:19:15.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-07-30 09:19:15.481 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:19:15.488 | 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: 8.30 ms

2025-07-30 09:19:15.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:19:15.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:19:15.649 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch225
2025-07-30 09:19:19.134 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 3.190e-04, size: 576, ETA: 0:27:48
2025-07-30 09:19:22.551 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.9, lr: 3.177e-04, size: 448, ETA: 0:27:45
2025-07-30 09:19:26.073 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.7, lr: 3.165e-04, size: 576, ETA: 0:27:42
2025-07-30 09:19:29.407 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.8, lr: 3.153e-04, size: 256, ETA: 0:27:38
2025-07-30 09:19:32.690 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 3.141e-04, size: 288, ETA: 0:27:35
2025-07-30 09:19:36.146 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 225/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 3.128e-04, size: 480, ETA: 0:27:31
2025-07-30 09:19:37.610 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:19:44.520 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:19:45.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:19:45.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4506
2025-07-30 09:19:45.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3991
2025-07-30 09:19:45.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2169
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3555
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:19:45.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:19:45.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:19:45.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:19:45.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:19:45.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:19:45.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:19:45.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:19:45.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:19:46.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:19:47.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:19:47.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:19:48.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:19:49.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:19:49.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:19:50.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:19:50.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:19:51.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:19:51.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:19:51.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:19:51.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:19:51.552 | 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

2025-07-30 09:19:51.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:19:51.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:19:51.756 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch226
2025-07-30 09:19:54.946 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 3.111e-04, size: 448, ETA: 0:27:26
2025-07-30 09:19:58.188 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 0.8, lr: 3.099e-04, size: 448, ETA: 0:27:23
2025-07-30 09:20:01.423 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 3.087e-04, size: 480, ETA: 0:27:19
2025-07-30 09:20:04.724 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 3.074e-04, size: 384, ETA: 0:27:16
2025-07-30 09:20:08.016 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 3.062e-04, size: 384, ETA: 0:27:12
2025-07-30 09:20:11.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 226/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 3.050e-04, size: 448, ETA: 0:27:09
2025-07-30 09:20:13.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:20:20.147 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:20:20.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:20:21.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4424
2025-07-30 09:20:21.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4248
2025-07-30 09:20:21.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1686
2025-07-30 09:20:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3453
2025-07-30 09:20:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:20:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:20:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-07-30 09:20:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-07-30 09:20:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.169
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:20:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:20:21.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:20:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:20:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:20:22.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:20:23.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:20:23.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:20:24.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:20:24.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:20:25.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:20:25.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:20:25.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:20:25.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:20:25.081 | 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

2025-07-30 09:20:25.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:20:25.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:20:25.288 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch227
2025-07-30 09:20:28.633 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 3.033e-04, size: 288, ETA: 0:27:04
2025-07-30 09:20:31.973 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.7, lr: 3.021e-04, size: 384, ETA: 0:27:00
2025-07-30 09:20:35.235 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.6, lr: 3.009e-04, size: 480, ETA: 0:26:57
2025-07-30 09:20:38.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 2.997e-04, size: 480, ETA: 0:26:53
2025-07-30 09:20:41.812 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.5, lr: 2.985e-04, size: 448, ETA: 0:26:50
2025-07-30 09:20:45.080 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 227/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 1.0, lr: 2.973e-04, size: 352, ETA: 0:26:46
2025-07-30 09:20:46.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:20:53.293 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:20:54.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:20:55.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4444
2025-07-30 09:20:55.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3588
2025-07-30 09:20:55.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2105
2025-07-30 09:20:55.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3379
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.338
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:20:55.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:20:55.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:20:55.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:20:55.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:20:55.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:20:56.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:20:56.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:20:57.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:20:58.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:20:59.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:21:00.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:21:01.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:21:01.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:21:02.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:21:02.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:21:02.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:21:02.659 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:21:02.667 | 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

2025-07-30 09:21:02.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:21:02.740 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:21:02.817 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch228
2025-07-30 09:21:05.920 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 2.955e-04, size: 544, ETA: 0:26:41
2025-07-30 09:21:09.167 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, 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: 2.943e-04, size: 320, ETA: 0:26:38
2025-07-30 09:21:12.320 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.9, lr: 2.932e-04, size: 512, ETA: 0:26:34
2025-07-30 09:21:15.671 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.9, lr: 2.920e-04, size: 576, ETA: 0:26:31
2025-07-30 09:21:19.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 1.1, lr: 2.908e-04, size: 288, ETA: 0:26:27
2025-07-30 09:21:22.427 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 228/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 1.0, lr: 2.896e-04, size: 384, ETA: 0:26:24
2025-07-30 09:21:23.965 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:21:30.896 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:21:31.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:21:32.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4100
2025-07-30 09:21:32.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3417
2025-07-30 09:21:32.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1679
2025-07-30 09:21:32.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3065
2025-07-30 09:21:32.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:21:32.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:21:32.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-07-30 09:21:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-07-30 09:21:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.168
2025-07-30 09:21:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.307
2025-07-30 09:21:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:21:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:21:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:21:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:21:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:21:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:21:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:21:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:21:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:21:32.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:21:33.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:21:34.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:21:34.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:21:35.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:21:36.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:21:36.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:21:37.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:21:38.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:21:38.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 09:21:38.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 09:21:38.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:21:38.059 | 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

2025-07-30 09:21:38.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:21:38.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:21:38.209 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch229
2025-07-30 09:21:41.556 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 2.879e-04, size: 480, ETA: 0:26:19
2025-07-30 09:21:44.874 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 2.867e-04, size: 448, ETA: 0:26:15
2025-07-30 09:21:48.483 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.8, lr: 2.855e-04, size: 256, ETA: 0:26:12
2025-07-30 09:21:51.905 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.9, lr: 2.844e-04, size: 384, ETA: 0:26:09
2025-07-30 09:21:55.278 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 9.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 5.0, cls_loss: 1.0, lr: 2.832e-04, size: 384, ETA: 0:26:05
2025-07-30 09:21:58.550 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 229/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.820e-04, size: 352, ETA: 0:26:02
2025-07-30 09:22:00.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:22:07.213 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:22:08.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:22:08.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4202
2025-07-30 09:22:08.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3531
2025-07-30 09:22:08.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2135
2025-07-30 09:22:08.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3289
2025-07-30 09:22:08.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:22:08.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:22:08.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-07-30 09:22:08.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-07-30 09:22:08.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.329
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:22:08.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:22:09.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:22:09.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:22:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:22:11.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:22:11.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:22:12.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:22:12.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:22:13.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:22:13.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:22:13.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:22:13.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 09:22:13.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:22:13.982 | 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: 8.51 ms

2025-07-30 09:22:13.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:22:14.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:22:14.248 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch230
2025-07-30 09:22:17.468 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 2.803e-04, size: 480, ETA: 0:25:57
2025-07-30 09:22:20.879 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 1.0, lr: 2.792e-04, size: 576, ETA: 0:25:53
2025-07-30 09:22:24.282 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 2.780e-04, size: 576, ETA: 0:25:50
2025-07-30 09:22:27.647 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 3.3, cls_loss: 0.9, lr: 2.768e-04, size: 448, ETA: 0:25:46
2025-07-30 09:22:30.889 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.4, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 2.757e-04, size: 384, ETA: 0:25:43
2025-07-30 09:22:34.229 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 230/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 2.745e-04, size: 448, ETA: 0:25:39
2025-07-30 09:22:35.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:22:42.483 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:22:43.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:22:44.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4581
2025-07-30 09:22:44.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3972
2025-07-30 09:22:44.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2011
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3521
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:22:44.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:22:44.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:22:44.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:22:44.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:22:44.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:22:44.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:22:44.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:22:44.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:22:45.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:22:46.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:22:47.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:22:47.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:22:48.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:22:49.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:22:49.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:22:50.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:22:50.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:22:50.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:22:50.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:22:50.648 | 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

2025-07-30 09:22:50.655 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:22:50.730 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:22:50.809 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch231
2025-07-30 09:22:54.173 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.7, lr: 2.729e-04, size: 512, ETA: 0:25:34
2025-07-30 09:22:57.479 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 2.717e-04, size: 384, ETA: 0:25:31
2025-07-30 09:23:00.833 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 2.705e-04, size: 448, ETA: 0:25:27
2025-07-30 09:23:04.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 1.0, lr: 2.694e-04, size: 256, ETA: 0:25:24
2025-07-30 09:23:07.121 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 2.683e-04, size: 352, ETA: 0:25:20
2025-07-30 09:23:10.364 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 231/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 2.671e-04, size: 320, ETA: 0:25:17
2025-07-30 09:23:11.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:23:18.646 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:23:19.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:23:20.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4372
2025-07-30 09:23:20.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3707
2025-07-30 09:23:20.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1742
2025-07-30 09:23:20.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3274
2025-07-30 09:23:20.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:23:20.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:23:20.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-07-30 09:23:20.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-07-30 09:23:20.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.174
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.327
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:23:20.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:23:21.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:23:22.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:23:23.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:23:24.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:23:24.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:23:25.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:23:26.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:23:27.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:23:28.392 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:23:28.392 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 09:23:28.393 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 09:23:28.393 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:23:28.401 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.91 ms, Average inference time: 8.37 ms

2025-07-30 09:23:28.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:23:28.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:23:28.552 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch232
2025-07-30 09:23:31.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 1.0, lr: 2.655e-04, size: 320, ETA: 0:25:12
2025-07-30 09:23:34.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.9, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.8, lr: 2.643e-04, size: 384, ETA: 0:25:08
2025-07-30 09:23:38.021 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.632e-04, size: 416, ETA: 0:25:05
2025-07-30 09:23:41.280 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.8, lr: 2.620e-04, size: 384, ETA: 0:25:01
2025-07-30 09:23:44.573 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.6, lr: 2.609e-04, size: 448, ETA: 0:24:58
2025-07-30 09:23:47.958 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 232/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.7, lr: 2.598e-04, size: 544, ETA: 0:24:54
2025-07-30 09:23:49.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:23:56.185 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:23:57.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:23:57.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4732
2025-07-30 09:23:57.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3965
2025-07-30 09:23:57.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2443
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3713
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:23:57.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:23:57.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:23:57.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:23:57.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:23:57.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:23:57.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:23:57.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:23:58.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:23:59.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:24:00.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:24:00.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:24:01.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:24:02.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:24:02.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:24:03.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:24:04.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:24:04.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:24:04.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:24:04.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:24:04.144 | 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

2025-07-30 09:24:04.145 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:24:04.255 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:24:04.390 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch233
2025-07-30 09:24:07.375 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.144s, 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: 2.581e-04, size: 256, ETA: 0:24:49
2025-07-30 09:24:10.716 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 1.0, lr: 2.570e-04, size: 352, ETA: 0:24:46
2025-07-30 09:24:14.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 1.3, lr: 2.559e-04, size: 384, ETA: 0:24:42
2025-07-30 09:24:17.333 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.6, lr: 2.548e-04, size: 544, ETA: 0:24:39
2025-07-30 09:24:20.609 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 2.537e-04, size: 320, ETA: 0:24:36
2025-07-30 09:24:24.100 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 233/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 2.525e-04, size: 448, ETA: 0:24:32
2025-07-30 09:24:25.704 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:24:32.626 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:24:33.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:24:33.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3984
2025-07-30 09:24:33.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3630
2025-07-30 09:24:33.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1655
2025-07-30 09:24:33.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3089
2025-07-30 09:24:33.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.165
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.309
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:24:33.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:24:33.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:24:33.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:24:34.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:24:34.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:24:35.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:24:35.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:24:36.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:24:36.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:24:37.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:24:37.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:24:38.276 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:24:38.276 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:24:38.276 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 09:24:38.276 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:24:38.283 | 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

2025-07-30 09:24:38.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:24:38.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:24:38.432 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch234
2025-07-30 09:24:41.711 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 10.7, iou_loss: 2.9, l1_loss: 2.4, conf_loss: 4.7, cls_loss: 0.7, lr: 2.509e-04, size: 512, ETA: 0:24:27
2025-07-30 09:24:44.899 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, 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: 2.498e-04, size: 416, ETA: 0:24:24
2025-07-30 09:24:48.137 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 2.487e-04, size: 288, ETA: 0:24:20
2025-07-30 09:24:51.471 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, 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: 2.476e-04, size: 288, ETA: 0:24:17
2025-07-30 09:24:54.839 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, 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: 2.465e-04, size: 544, ETA: 0:24:13
2025-07-30 09:24:58.309 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 234/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.8, lr: 2.454e-04, size: 512, ETA: 0:24:10
2025-07-30 09:24:59.913 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:25:06.692 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:25:07.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:25:08.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4236
2025-07-30 09:25:08.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3641
2025-07-30 09:25:08.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2023
2025-07-30 09:25:08.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3300
2025-07-30 09:25:08.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:25:08.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:25:08.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-07-30 09:25:08.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-07-30 09:25:08.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.202
2025-07-30 09:25:08.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-07-30 09:25:08.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:25:08.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:25:08.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:25:08.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:25:08.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:25:08.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:25:08.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:25:08.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:25:08.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:25:09.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:25:10.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:25:10.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:25:11.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:25:12.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:25:13.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:25:14.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:25:14.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:25:15.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:25:15.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:25:15.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 09:25:15.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:25:15.643 | 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

2025-07-30 09:25:15.644 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:25:15.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:25:15.797 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch235
2025-07-30 09:25:18.918 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 3.1, cls_loss: 0.8, lr: 2.438e-04, size: 384, ETA: 0:24:05
2025-07-30 09:25:22.121 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.7, cls_loss: 0.5, lr: 2.427e-04, size: 416, ETA: 0:24:01
2025-07-30 09:25:25.478 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 2.416e-04, size: 480, ETA: 0:23:58
2025-07-30 09:25:28.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 2.405e-04, size: 288, ETA: 0:23:54
2025-07-30 09:25:32.051 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 2.394e-04, size: 416, ETA: 0:23:51
2025-07-30 09:25:35.408 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 235/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 1.0, lr: 2.383e-04, size: 416, ETA: 0:23:48
2025-07-30 09:25:36.953 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:25:43.664 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:25:44.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:25:45.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4379
2025-07-30 09:25:45.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3672
2025-07-30 09:25:45.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2133
2025-07-30 09:25:45.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3394
2025-07-30 09:25:45.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:25:45.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:25:45.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:25:45.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:25:46.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:25:46.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:25:47.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:25:48.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:25:48.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:25:49.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:25:50.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:25:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:25:51.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:25:51.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:25:51.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:25:51.711 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:25:51.719 | 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

2025-07-30 09:25:51.720 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:25:51.791 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:25:51.867 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch236
2025-07-30 09:25:55.238 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 2.368e-04, size: 576, ETA: 0:23:43
2025-07-30 09:25:58.774 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 2.357e-04, size: 576, ETA: 0:23:39
2025-07-30 09:26:02.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.6, lr: 2.346e-04, size: 480, ETA: 0:23:36
2025-07-30 09:26:05.472 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 2.335e-04, size: 480, ETA: 0:23:32
2025-07-30 09:26:08.973 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.174s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 2.324e-04, size: 448, ETA: 0:23:29
2025-07-30 09:26:12.152 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 236/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 0.7, lr: 2.314e-04, size: 448, ETA: 0:23:25
2025-07-30 09:26:13.578 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:26:20.528 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:26:21.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:26:21.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4690
2025-07-30 09:26:21.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3961
2025-07-30 09:26:21.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2127
2025-07-30 09:26:21.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3593
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:26:21.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:26:21.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:26:21.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:26:21.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:26:21.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:26:21.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:26:21.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:26:22.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:26:22.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:26:23.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:26:24.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:26:24.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:26:25.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:26:25.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:26:26.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:26:26.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:26:26.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:26:26.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:26:26.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:26:27.000 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.87 ms, Average inference time: 8.23 ms

2025-07-30 09:26:27.000 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:26:27.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:26:27.254 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch237
2025-07-30 09:26:30.411 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.151s, 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: 2.298e-04, size: 512, ETA: 0:23:20
2025-07-30 09:26:33.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 2.287e-04, size: 256, ETA: 0:23:17
2025-07-30 09:26:37.237 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.9, lr: 2.277e-04, size: 576, ETA: 0:23:14
2025-07-30 09:26:40.595 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.9, lr: 2.266e-04, size: 416, ETA: 0:23:10
2025-07-30 09:26:44.001 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 8.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 0.8, lr: 2.255e-04, size: 448, ETA: 0:23:07
2025-07-30 09:26:47.423 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 237/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 1.0, lr: 2.245e-04, size: 288, ETA: 0:23:03
2025-07-30 09:26:49.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:26:55.900 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:26:57.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:26:57.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4910
2025-07-30 09:26:58.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3772
2025-07-30 09:26:58.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2421
2025-07-30 09:26:58.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3701
2025-07-30 09:26:58.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:26:58.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.242
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:26:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:26:58.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:26:59.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:27:00.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:27:01.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:27:02.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:27:03.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:27:04.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:27:05.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:27:06.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:27:07.381 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:27:07.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:27:07.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:27:07.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:27:07.397 | 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

2025-07-30 09:27:07.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:27:07.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:27:07.548 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch238
2025-07-30 09:27:10.909 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 2.229e-04, size: 544, ETA: 0:22:58
2025-07-30 09:27:14.227 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, 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: 2.219e-04, size: 320, ETA: 0:22:55
2025-07-30 09:27:17.450 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 2.208e-04, size: 384, ETA: 0:22:51
2025-07-30 09:27:20.705 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 2.198e-04, size: 512, ETA: 0:22:48
2025-07-30 09:27:24.076 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 9.4, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 4.0, cls_loss: 0.9, lr: 2.187e-04, size: 384, ETA: 0:22:44
2025-07-30 09:27:27.683 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 238/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.177s, data_time: 0.002s, total_loss: 10.6, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 5.3, cls_loss: 0.8, lr: 2.177e-04, size: 512, ETA: 0:22:41
2025-07-30 09:27:29.199 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:27:36.097 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:27:36.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:27:36.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2793
2025-07-30 09:27:36.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2678
2025-07-30 09:27:36.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1347
2025-07-30 09:27:36.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2273
2025-07-30 09:27:36.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:27:36.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.279
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.135
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.227
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:27:36.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:27:36.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:27:36.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:27:37.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:27:37.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:27:37.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:27:37.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:27:38.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:27:38.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:27:38.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:27:38.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:27:39.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:27:39.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:27:39.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.23
2025-07-30 09:27:39.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:27:39.234 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.78 ms, Average inference time: 8.16 ms

2025-07-30 09:27:39.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:27:39.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:27:39.428 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch239
2025-07-30 09:27:42.485 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 2.162e-04, size: 288, ETA: 0:22:36
2025-07-30 09:27:45.609 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.8, lr: 2.151e-04, size: 288, ETA: 0:22:33
2025-07-30 09:27:48.967 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 2.141e-04, size: 256, ETA: 0:22:29
2025-07-30 09:27:52.332 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, 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: 0.6, lr: 2.130e-04, size: 320, ETA: 0:22:26
2025-07-30 09:27:55.567 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 2.120e-04, size: 416, ETA: 0:22:22
2025-07-30 09:27:58.911 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 239/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.6, lr: 2.110e-04, size: 448, ETA: 0:22:19
2025-07-30 09:28:00.329 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:28:07.209 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:28:08.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:28:08.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4736
2025-07-30 09:28:08.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4420
2025-07-30 09:28:08.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2867
2025-07-30 09:28:08.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4008
2025-07-30 09:28:08.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:28:08.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:28:08.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-07-30 09:28:08.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-07-30 09:28:08.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-07-30 09:28:08.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.401
2025-07-30 09:28:08.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:28:08.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:28:08.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:28:08.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:28:08.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:28:08.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:28:08.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:28:08.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:28:08.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:28:09.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:28:10.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:28:11.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:28:12.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:28:12.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:28:13.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:28:14.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:28:15.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:28:16.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:28:16.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 09:28:16.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-07-30 09:28:16.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:28:16.124 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.34 ms, Average NMS time: 0.91 ms, Average inference time: 8.26 ms

2025-07-30 09:28:16.130 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:28:16.239 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:28:16.351 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch240
2025-07-30 09:28:19.593 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, 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: 2.095e-04, size: 352, ETA: 0:22:14
2025-07-30 09:28:22.767 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.5, lr: 2.085e-04, size: 512, ETA: 0:22:10
2025-07-30 09:28:26.151 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.8, lr: 2.074e-04, size: 256, ETA: 0:22:07
2025-07-30 09:28:29.231 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 2.064e-04, size: 352, ETA: 0:22:03
2025-07-30 09:28:32.652 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 2.054e-04, size: 320, ETA: 0:22:00
2025-07-30 09:28:35.940 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 240/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.006s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.6, lr: 2.044e-04, size: 576, ETA: 0:21:56
2025-07-30 09:28:37.511 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:28:44.329 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:28:45.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:28:45.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4587
2025-07-30 09:28:45.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3887
2025-07-30 09:28:45.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2003
2025-07-30 09:28:45.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3492
2025-07-30 09:28:45.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:28:45.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:28:45.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-07-30 09:28:45.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-07-30 09:28:45.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.200
2025-07-30 09:28:45.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.349
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:28:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:28:46.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:28:47.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:28:47.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:28:48.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:28:49.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:28:49.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:28:50.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:28:51.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:28:51.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:28:51.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:28:51.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:28:51.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:28:51.766 | 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

2025-07-30 09:28:51.766 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:28:51.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:28:51.978 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch241
2025-07-30 09:28:55.095 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 2.029e-04, size: 416, ETA: 0:21:51
2025-07-30 09:28:58.513 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 2.019e-04, size: 544, ETA: 0:21:48
2025-07-30 09:29:01.867 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 2.009e-04, size: 480, ETA: 0:21:45
2025-07-30 09:29:05.086 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 1.999e-04, size: 320, ETA: 0:21:41
2025-07-30 09:29:08.566 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 1.989e-04, size: 384, ETA: 0:21:38
2025-07-30 09:29:11.972 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 241/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 1.978e-04, size: 448, ETA: 0:21:34
2025-07-30 09:29:13.421 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:29:20.402 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:29:21.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:29:22.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4047
2025-07-30 09:29:22.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3276
2025-07-30 09:29:22.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1639
2025-07-30 09:29:22.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2987
2025-07-30 09:29:22.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:29:22.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:29:22.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.164
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.299
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:29:22.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:29:22.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:29:22.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:29:22.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:29:23.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:29:23.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:29:24.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:29:25.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:29:26.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:29:27.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:29:28.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:29:28.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:29:29.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:29:29.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 09:29:29.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 09:29:29.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:29:29.814 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.52 ms, Average NMS time: 0.91 ms, Average inference time: 8.43 ms

2025-07-30 09:29:29.816 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:29:29.889 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:29:29.965 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch242
2025-07-30 09:29:33.271 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 0.8, lr: 1.964e-04, size: 576, ETA: 0:21:29
2025-07-30 09:29:36.757 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.954e-04, size: 480, ETA: 0:21:26
2025-07-30 09:29:40.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.944e-04, size: 576, ETA: 0:21:23
2025-07-30 09:29:43.673 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 1.934e-04, size: 576, ETA: 0:21:19
2025-07-30 09:29:47.074 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 1.924e-04, size: 448, ETA: 0:21:16
2025-07-30 09:29:50.424 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 242/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.914e-04, size: 448, ETA: 0:21:12
2025-07-30 09:29:51.869 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:29:58.745 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:29:59.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:30:00.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3371
2025-07-30 09:30:00.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3091
2025-07-30 09:30:00.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1681
2025-07-30 09:30:00.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2714
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.168
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.271
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:30:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:30:00.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:30:00.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:30:00.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:30:00.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:30:00.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:30:00.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:30:01.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:30:02.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:30:02.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:30:03.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:30:04.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:30:04.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:30:05.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:30:05.973 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:30:05.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 09:30:05.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 09:30:05.974 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:30:05.982 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.60 ms, Average NMS time: 0.90 ms, Average inference time: 8.51 ms

2025-07-30 09:30:05.983 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:30:06.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:30:06.135 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch243
2025-07-30 09:30:09.327 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.900e-04, size: 416, ETA: 0:21:07
2025-07-30 09:30:12.393 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 1.0, lr: 1.890e-04, size: 416, ETA: 0:21:04
2025-07-30 09:30:15.792 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.880e-04, size: 544, ETA: 0:21:00
2025-07-30 09:30:19.057 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.871e-04, size: 384, ETA: 0:20:57
2025-07-30 09:30:22.219 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, 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: 1.861e-04, size: 352, ETA: 0:20:53
2025-07-30 09:30:25.507 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 243/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.005s, total_loss: 8.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.851e-04, size: 256, ETA: 0:20:50
2025-07-30 09:30:26.879 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:30:34.006 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:30:35.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:30:36.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4723
2025-07-30 09:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4033
2025-07-30 09:30:36.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2503
2025-07-30 09:30:36.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3753
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.250
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.375
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:30:36.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:30:36.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:30:36.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:30:36.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:30:36.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:30:36.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:30:37.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:30:37.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:30:38.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:30:39.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:30:40.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:30:41.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:30:41.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:30:42.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:30:43.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:30:43.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:30:43.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 09:30:43.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:30:43.345 | 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

2025-07-30 09:30:43.347 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:30:43.468 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:30:43.550 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch244
2025-07-30 09:30:46.543 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.146s, 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: 1.837e-04, size: 256, ETA: 0:20:45
2025-07-30 09:30:49.671 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.827e-04, size: 320, ETA: 0:20:41
2025-07-30 09:30:53.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.6, lr: 1.818e-04, size: 352, ETA: 0:20:38
2025-07-30 09:30:56.198 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.150s, 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: 1.808e-04, size: 352, ETA: 0:20:34
2025-07-30 09:30:59.259 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.8, lr: 1.798e-04, size: 352, ETA: 0:20:31
2025-07-30 09:31:02.480 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 244/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 1.789e-04, size: 256, ETA: 0:20:28
2025-07-30 09:31:03.813 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:31:10.684 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:31:11.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:31:12.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3927
2025-07-30 09:31:12.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3620
2025-07-30 09:31:12.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2030
2025-07-30 09:31:12.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3192
2025-07-30 09:31:12.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:31:12.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:31:12.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-07-30 09:31:12.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-07-30 09:31:12.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.203
2025-07-30 09:31:12.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.319
2025-07-30 09:31:12.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:31:12.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:31:12.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:31:12.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:31:12.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:31:12.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:31:12.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:31:12.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:31:12.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:31:13.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:31:14.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:31:15.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:31:16.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:31:17.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:31:18.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:31:18.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:31:19.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:31:20.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:31:20.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:31:20.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 09:31:20.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:31:20.600 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.61 ms, Average NMS time: 0.88 ms, Average inference time: 8.49 ms

2025-07-30 09:31:20.601 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:31:20.713 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:31:20.789 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch245
2025-07-30 09:31:24.026 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.1, l1_loss: 1.6, conf_loss: 2.9, cls_loss: 0.7, lr: 1.775e-04, size: 416, ETA: 0:20:23
2025-07-30 09:31:27.336 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.7, lr: 1.765e-04, size: 384, ETA: 0:20:19
2025-07-30 09:31:30.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.756e-04, size: 448, ETA: 0:20:16
2025-07-30 09:31:34.151 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 1.746e-04, size: 544, ETA: 0:20:12
2025-07-30 09:31:37.455 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.737e-04, size: 544, ETA: 0:20:09
2025-07-30 09:31:40.954 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 245/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.727e-04, size: 480, ETA: 0:20:05
2025-07-30 09:31:42.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:31:49.265 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:31:49.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:31:50.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4836
2025-07-30 09:31:50.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4114
2025-07-30 09:31:50.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2551
2025-07-30 09:31:50.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3834
2025-07-30 09:31:50.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:31:50.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:31:50.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-07-30 09:31:50.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-07-30 09:31:50.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-07-30 09:31:50.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.383
2025-07-30 09:31:50.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:31:50.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:31:50.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:31:50.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:31:50.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:31:50.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:31:50.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:31:50.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:31:50.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:31:51.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:31:51.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:31:52.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:31:52.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:31:53.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:31:53.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:31:54.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:31:54.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:31:55.245 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:31:55.245 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:31:55.245 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 09:31:55.245 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:31:55.253 | 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

2025-07-30 09:31:55.255 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:31:55.397 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:31:55.473 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch246
2025-07-30 09:31:58.904 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.8, lr: 1.714e-04, size: 576, ETA: 0:20:00
2025-07-30 09:32:02.346 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, 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: 1.704e-04, size: 256, ETA: 0:19:57
2025-07-30 09:32:05.590 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 0.6, lr: 1.695e-04, size: 576, ETA: 0:19:54
2025-07-30 09:32:09.014 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 1.686e-04, size: 384, ETA: 0:19:50
2025-07-30 09:32:12.419 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 3.5, cls_loss: 0.6, lr: 1.676e-04, size: 288, ETA: 0:19:47
2025-07-30 09:32:15.662 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 246/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.667e-04, size: 256, ETA: 0:19:43
2025-07-30 09:32:17.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:32:23.822 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:32:24.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:32:24.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4531
2025-07-30 09:32:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3911
2025-07-30 09:32:25.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2158
2025-07-30 09:32:25.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3533
2025-07-30 09:32:25.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:32:25.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:32:25.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-07-30 09:32:25.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-07-30 09:32:25.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:32:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:32:25.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:32:25.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:32:26.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:32:26.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:32:27.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:32:28.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:32:28.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:32:29.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:32:29.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:32:30.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:32:30.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:32:30.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:32:30.256 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:32:30.263 | 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

2025-07-30 09:32:30.265 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:32:30.337 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:32:30.412 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch247
2025-07-30 09:32:33.565 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 3.2, cls_loss: 0.9, lr: 1.654e-04, size: 416, ETA: 0:19:38
2025-07-30 09:32:36.895 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.644e-04, size: 288, ETA: 0:19:35
2025-07-30 09:32:40.081 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 3.8, cls_loss: 0.7, lr: 1.635e-04, size: 256, ETA: 0:19:31
2025-07-30 09:32:43.359 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.626e-04, size: 576, ETA: 0:19:28
2025-07-30 09:32:46.753 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.6, lr: 1.617e-04, size: 544, ETA: 0:19:25
2025-07-30 09:32:50.070 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 247/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.608e-04, size: 448, ETA: 0:19:21
2025-07-30 09:32:51.580 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:32:58.306 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:32:59.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:32:59.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3495
2025-07-30 09:32:59.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2527
2025-07-30 09:32:59.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1173
2025-07-30 09:32:59.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2398
2025-07-30 09:32:59.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.253
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.117
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.240
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:32:59.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:32:59.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:32:59.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:32:59.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:33:00.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:33:00.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:33:01.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:33:02.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:33:02.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:33:03.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:33:03.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:33:04.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:33:04.846 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:33:04.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:33:04.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.24
2025-07-30 09:33:04.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:33:04.854 | 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

2025-07-30 09:33:04.855 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:33:04.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:33:05.001 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch248
2025-07-30 09:33:08.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 1.594e-04, size: 256, ETA: 0:19:16
2025-07-30 09:33:11.471 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 1.585e-04, size: 512, ETA: 0:19:13
2025-07-30 09:33:14.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, 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: 1.576e-04, size: 256, ETA: 0:19:09
2025-07-30 09:33:18.130 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.567e-04, size: 256, ETA: 0:19:06
2025-07-30 09:33:21.371 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.558e-04, size: 576, ETA: 0:19:02
2025-07-30 09:33:24.864 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 248/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.549e-04, size: 576, ETA: 0:18:59
2025-07-30 09:33:26.403 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:33:33.253 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:33:33.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:33:34.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4152
2025-07-30 09:33:34.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3807
2025-07-30 09:33:34.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2134
2025-07-30 09:33:34.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3364
2025-07-30 09:33:34.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:33:34.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:33:34.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.213
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.336
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:33:34.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:33:34.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:33:34.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:33:34.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:33:34.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:33:35.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:33:35.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:33:36.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:33:36.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:33:37.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:33:37.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:33:38.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:33:38.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:33:39.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:33:39.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:33:39.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:33:39.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:33:39.175 | 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: 8.29 ms

2025-07-30 09:33:39.181 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:33:39.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:33:39.402 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch249
2025-07-30 09:33:42.763 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.6, lr: 1.536e-04, size: 512, ETA: 0:18:54
2025-07-30 09:33:46.127 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 4.2, cls_loss: 0.7, lr: 1.527e-04, size: 448, ETA: 0:18:51
2025-07-30 09:33:49.540 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, 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: 1.518e-04, size: 544, ETA: 0:18:47
2025-07-30 09:33:52.860 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.509e-04, size: 448, ETA: 0:18:44
2025-07-30 09:33:56.050 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.501e-04, size: 512, ETA: 0:18:40
2025-07-30 09:33:59.526 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 249/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 1.492e-04, size: 384, ETA: 0:18:37
2025-07-30 09:34:01.020 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:34:08.040 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:34:08.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:34:09.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4659
2025-07-30 09:34:09.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4098
2025-07-30 09:34:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2347
2025-07-30 09:34:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3702
2025-07-30 09:34:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:34:09.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:34:09.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:34:09.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:34:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:34:10.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:34:11.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:34:12.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:34:12.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:34:13.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:34:14.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:34:14.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:34:15.529 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:34:15.529 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:34:15.529 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:34:15.529 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:34:15.536 | 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: 8.41 ms

2025-07-30 09:34:15.538 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:34:15.617 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:34:15.693 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch250
2025-07-30 09:34:19.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, 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: 1.479e-04, size: 288, ETA: 0:18:32
2025-07-30 09:34:22.293 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, 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: 1.470e-04, size: 480, ETA: 0:18:29
2025-07-30 09:34:25.727 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 1.461e-04, size: 384, ETA: 0:18:25
2025-07-30 09:34:29.042 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.7, lr: 1.453e-04, size: 576, ETA: 0:18:22
2025-07-30 09:34:32.364 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 1.444e-04, size: 352, ETA: 0:18:18
2025-07-30 09:34:35.728 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 250/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.8, cls_loss: 0.5, lr: 1.435e-04, size: 480, ETA: 0:18:15
2025-07-30 09:34:37.236 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:34:43.977 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:34:44.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:34:45.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4958
2025-07-30 09:34:45.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4378
2025-07-30 09:34:45.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2082
2025-07-30 09:34:45.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3806
2025-07-30 09:34:45.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:34:45.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:34:45.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-07-30 09:34:45.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-07-30 09:34:45.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.208
2025-07-30 09:34:45.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.381
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:34:45.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:34:46.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:34:47.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:34:47.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:34:48.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:34:49.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:34:50.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:34:50.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:34:51.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:34:52.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:34:52.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:34:52.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 09:34:52.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:34:52.370 | 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

2025-07-30 09:34:52.371 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:34:52.445 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:34:52.520 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch251
2025-07-30 09:34:55.702 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 3.5, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.423e-04, size: 416, ETA: 0:18:10
2025-07-30 09:34:59.048 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.8, lr: 1.414e-04, size: 544, ETA: 0:18:06
2025-07-30 09:35:02.350 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, 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: 1.406e-04, size: 544, ETA: 0:18:03
2025-07-30 09:35:05.737 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, 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: 1.397e-04, size: 544, ETA: 0:18:00
2025-07-30 09:35:08.955 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 1.388e-04, size: 512, ETA: 0:17:56
2025-07-30 09:35:12.164 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 251/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 1.380e-04, size: 416, ETA: 0:17:53
2025-07-30 09:35:13.531 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:35:20.301 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:35:21.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:35:21.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4219
2025-07-30 09:35:21.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3363
2025-07-30 09:35:21.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2007
2025-07-30 09:35:21.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3196
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.320
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:35:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:35:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:35:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:35:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:35:22.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:35:22.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:35:23.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:35:24.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:35:24.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:35:25.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:35:25.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:35:26.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:35:27.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:35:27.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 09:35:27.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 09:35:27.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:35:27.054 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.30 ms, Average NMS time: 0.86 ms, Average inference time: 8.15 ms

2025-07-30 09:35:27.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:35:27.130 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:35:27.204 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch252
2025-07-30 09:35:30.206 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 1.368e-04, size: 320, ETA: 0:17:48
2025-07-30 09:35:33.410 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.5, lr: 1.359e-04, size: 544, ETA: 0:17:44
2025-07-30 09:35:36.746 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.6, lr: 1.351e-04, size: 512, ETA: 0:17:41
2025-07-30 09:35:40.136 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 1.0, lr: 1.342e-04, size: 576, ETA: 0:17:37
2025-07-30 09:35:43.520 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 1.334e-04, size: 576, ETA: 0:17:34
2025-07-30 09:35:46.790 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 252/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 1.326e-04, size: 352, ETA: 0:17:31
2025-07-30 09:35:48.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:35:55.106 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:35:56.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:35:56.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4254
2025-07-30 09:35:56.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3765
2025-07-30 09:35:56.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1733
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3251
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.173
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.325
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:35:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:35:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:35:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:35:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:35:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:35:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:35:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:35:56.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:35:57.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:35:58.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:35:59.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:36:00.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:36:00.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:36:01.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:36:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:36:03.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:36:04.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:36:04.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:36:04.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 09:36:04.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:36:04.018 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.89 ms, Average inference time: 8.33 ms

2025-07-30 09:36:04.020 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:36:04.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:36:04.172 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch253
2025-07-30 09:36:07.406 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, 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: 1.313e-04, size: 320, ETA: 0:17:26
2025-07-30 09:36:10.849 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.305e-04, size: 576, ETA: 0:17:22
2025-07-30 09:36:14.416 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 1.297e-04, size: 480, ETA: 0:17:19
2025-07-30 09:36:17.699 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.289e-04, size: 448, ETA: 0:17:15
2025-07-30 09:36:21.246 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.171s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.8, cls_loss: 0.6, lr: 1.280e-04, size: 544, ETA: 0:17:12
2025-07-30 09:36:24.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 253/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.272e-04, size: 288, ETA: 0:17:09
2025-07-30 09:36:26.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:36:32.970 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:36:34.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:36:35.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3575
2025-07-30 09:36:35.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.2611
2025-07-30 09:36:35.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1178
2025-07-30 09:36:35.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2455
2025-07-30 09:36:35.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:36:35.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:36:35.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-07-30 09:36:35.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.261
2025-07-30 09:36:35.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.118
2025-07-30 09:36:35.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.245
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:36:35.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:36:36.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:36:37.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:36:38.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:36:39.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:36:40.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:36:41.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:36:42.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:36:43.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:36:44.765 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:36:44.765 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:36:44.765 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.25
2025-07-30 09:36:44.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:36:44.773 | 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

2025-07-30 09:36:44.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:36:44.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:36:44.926 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch254
2025-07-30 09:36:48.100 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 1.260e-04, size: 512, ETA: 0:17:04
2025-07-30 09:36:51.301 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, 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: 0.6, lr: 1.252e-04, size: 256, ETA: 0:17:00
2025-07-30 09:36:54.793 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.170s, 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: 1.244e-04, size: 288, ETA: 0:16:57
2025-07-30 09:36:58.106 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 9.5, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 4.6, cls_loss: 0.8, lr: 1.236e-04, size: 544, ETA: 0:16:53
2025-07-30 09:37:01.381 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 15.5, iou_loss: 3.5, l1_loss: 0.7, conf_loss: 10.6, cls_loss: 0.7, lr: 1.228e-04, size: 480, ETA: 0:16:50
2025-07-30 09:37:04.672 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 254/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.220e-04, size: 416, ETA: 0:16:46
2025-07-30 09:37:06.138 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:37:12.895 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:37:13.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:37:13.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4309
2025-07-30 09:37:13.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4122
2025-07-30 09:37:13.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2143
2025-07-30 09:37:13.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3525
2025-07-30 09:37:13.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:37:13.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:37:13.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-07-30 09:37:13.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-07-30 09:37:13.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.352
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:37:13.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:37:14.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:37:14.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:37:15.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:37:15.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:37:16.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:37:16.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:37:17.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:37:17.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:37:18.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:37:18.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:37:18.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:37:18.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:37:18.205 | 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

2025-07-30 09:37:18.209 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:37:18.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:37:18.426 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch255
2025-07-30 09:37:21.801 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.208e-04, size: 256, ETA: 0:16:42
2025-07-30 09:37:25.271 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.200e-04, size: 416, ETA: 0:16:38
2025-07-30 09:37:28.433 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.192e-04, size: 256, ETA: 0:16:35
2025-07-30 09:37:31.676 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 1.184e-04, size: 512, ETA: 0:16:31
2025-07-30 09:37:35.091 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.9, lr: 1.176e-04, size: 576, ETA: 0:16:28
2025-07-30 09:37:38.516 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 255/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.005s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.168e-04, size: 448, ETA: 0:16:24
2025-07-30 09:37:40.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:37:46.737 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:37:47.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:37:48.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5195
2025-07-30 09:37:48.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4383
2025-07-30 09:37:48.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2382
2025-07-30 09:37:48.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3987
2025-07-30 09:37:48.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:37:48.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:37:48.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-07-30 09:37:48.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-07-30 09:37:48.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-07-30 09:37:48.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.399
2025-07-30 09:37:48.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:37:48.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:37:48.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:37:48.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:37:48.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:37:48.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:37:48.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:37:48.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:37:48.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:37:49.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:37:49.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:37:50.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:37:51.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:37:52.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:37:52.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:37:53.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:37:54.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:37:55.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:37:55.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:37:55.091 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-07-30 09:37:55.091 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:37:55.098 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.89 ms, Average inference time: 8.26 ms

2025-07-30 09:37:55.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:37:55.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:37:55.247 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch256
2025-07-30 09:37:58.473 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 1.157e-04, size: 256, ETA: 0:16:20
2025-07-30 09:38:01.799 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.8, lr: 1.149e-04, size: 544, ETA: 0:16:16
2025-07-30 09:38:05.084 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 1.141e-04, size: 544, ETA: 0:16:13
2025-07-30 09:38:08.451 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.8, cls_loss: 0.7, lr: 1.134e-04, size: 384, ETA: 0:16:09
2025-07-30 09:38:11.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.9, lr: 1.126e-04, size: 544, ETA: 0:16:06
2025-07-30 09:38:15.065 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 256/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.8, lr: 1.118e-04, size: 416, ETA: 0:16:02
2025-07-30 09:38:16.541 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:38:23.369 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:38:24.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:38:24.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4546
2025-07-30 09:38:24.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4138
2025-07-30 09:38:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2231
2025-07-30 09:38:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3638
2025-07-30 09:38:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:38:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.223
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.364
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:38:24.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:38:24.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:38:24.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:38:25.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:38:26.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:38:26.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:38:27.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:38:28.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:38:28.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:38:29.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:38:30.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:38:30.675 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:38:30.675 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:38:30.675 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:38:30.675 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:38:30.683 | 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: 8.35 ms

2025-07-30 09:38:30.684 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:38:30.762 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:38:30.838 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch257
2025-07-30 09:38:34.075 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 1.107e-04, size: 256, ETA: 0:15:57
2025-07-30 09:38:37.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 1.099e-04, size: 448, ETA: 0:15:54
2025-07-30 09:38:40.458 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.092e-04, size: 448, ETA: 0:15:51
2025-07-30 09:38:43.721 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 1.0, lr: 1.084e-04, size: 320, ETA: 0:15:47
2025-07-30 09:38:46.931 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 1.2, lr: 1.076e-04, size: 352, ETA: 0:15:44
2025-07-30 09:38:50.147 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 257/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 1.0, lr: 1.069e-04, size: 448, ETA: 0:15:40
2025-07-30 09:38:51.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:38:58.553 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:38:59.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:38:59.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4072
2025-07-30 09:38:59.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3773
2025-07-30 09:38:59.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2270
2025-07-30 09:38:59.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3372
2025-07-30 09:38:59.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:38:59.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:38:59.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-07-30 09:38:59.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-07-30 09:38:59.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.227
2025-07-30 09:38:59.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.337
2025-07-30 09:38:59.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:38:59.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:38:59.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:38:59.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:38:59.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:38:59.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:38:59.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:38:59.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:38:59.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:39:00.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:39:00.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:39:01.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:39:01.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:39:02.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:39:02.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:39:03.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:39:03.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:39:03.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:39:03.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:39:03.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:39:03.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:39:03.941 | 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

2025-07-30 09:39:03.942 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:39:04.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:39:04.093 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch258
2025-07-30 09:39:07.302 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.058e-04, size: 256, ETA: 0:15:35
2025-07-30 09:39:10.542 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.050e-04, size: 512, ETA: 0:15:32
2025-07-30 09:39:14.082 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.043e-04, size: 256, ETA: 0:15:29
2025-07-30 09:39:17.500 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.9, lr: 1.036e-04, size: 576, ETA: 0:15:25
2025-07-30 09:39:20.848 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 1.028e-04, size: 320, ETA: 0:15:22
2025-07-30 09:39:24.295 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 258/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.167s, 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: 1.021e-04, size: 384, ETA: 0:15:18
2025-07-30 09:39:25.835 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:39:32.555 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:39:33.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:39:34.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4284
2025-07-30 09:39:34.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3524
2025-07-30 09:39:34.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1850
2025-07-30 09:39:34.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3219
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.185
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:39:34.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:39:34.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:39:34.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:39:34.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:39:34.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:39:34.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:39:34.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:39:35.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:39:36.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:39:37.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:39:38.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:39:38.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:39:39.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:39:40.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:39:41.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:39:42.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:39:42.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:39:42.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 09:39:42.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:39:42.382 | 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

2025-07-30 09:39:42.387 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:39:42.510 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:39:42.589 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch259
2025-07-30 09:39:46.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.010e-04, size: 512, ETA: 0:15:13
2025-07-30 09:39:49.283 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 1.003e-04, size: 544, ETA: 0:15:10
2025-07-30 09:39:52.497 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.4, lr: 9.953e-05, size: 384, ETA: 0:15:07
2025-07-30 09:39:55.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 1.0, lr: 9.880e-05, size: 448, ETA: 0:15:03
2025-07-30 09:39:59.028 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 9.808e-05, size: 544, ETA: 0:15:00
2025-07-30 09:40:02.456 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 259/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.6, lr: 9.735e-05, size: 384, ETA: 0:14:56
2025-07-30 09:40:03.892 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:40:10.725 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:40:11.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:40:12.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4457
2025-07-30 09:40:12.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4014
2025-07-30 09:40:12.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2219
2025-07-30 09:40:12.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3563
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.222
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.356
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:40:12.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:40:12.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:40:12.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:40:12.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:40:12.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:40:12.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:40:12.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:40:12.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:40:13.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:40:14.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:40:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:40:15.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:40:16.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:40:16.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:40:17.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:40:17.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:40:17.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:40:17.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:40:17.853 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:40:17.861 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.92 ms, Average inference time: 8.44 ms

2025-07-30 09:40:17.866 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:40:17.990 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:40:18.066 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch260
2025-07-30 09:40:21.420 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.9, lr: 9.631e-05, size: 544, ETA: 0:14:51
2025-07-30 09:40:24.760 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.9, lr: 9.559e-05, size: 384, ETA: 0:14:48
2025-07-30 09:40:27.979 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 9.487e-05, size: 320, ETA: 0:14:45
2025-07-30 09:40:31.245 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.8, lr: 9.416e-05, size: 384, ETA: 0:14:41
2025-07-30 09:40:34.584 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 9.345e-05, size: 448, ETA: 0:14:38
2025-07-30 09:40:37.878 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 260/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 9.274e-05, size: 256, ETA: 0:14:34
2025-07-30 09:40:39.246 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:40:46.051 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:40:46.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:40:47.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3414
2025-07-30 09:40:47.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3546
2025-07-30 09:40:47.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1524
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2828
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.152
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.283
2025-07-30 09:40:47.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:40:47.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:40:47.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:40:48.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:40:48.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:40:49.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:40:49.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:40:49.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:40:50.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:40:50.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:40:51.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:40:51.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 09:40:51.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 09:40:51.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:40:51.343 | 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

2025-07-30 09:40:51.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:40:51.427 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:40:51.504 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch261
2025-07-30 09:40:54.734 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 9.172e-05, size: 320, ETA: 0:14:29
2025-07-30 09:40:58.081 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.9, lr: 9.102e-05, size: 480, ETA: 0:14:26
2025-07-30 09:41:01.404 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, 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: 9.032e-05, size: 576, ETA: 0:14:22
2025-07-30 09:41:04.688 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 8.962e-05, size: 288, ETA: 0:14:19
2025-07-30 09:41:08.212 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.5, lr: 8.893e-05, size: 544, ETA: 0:14:16
2025-07-30 09:41:11.504 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 261/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 8.824e-05, size: 256, ETA: 0:14:12
2025-07-30 09:41:12.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:41:19.801 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:41:20.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:41:21.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3536
2025-07-30 09:41:21.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3442
2025-07-30 09:41:21.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1806
2025-07-30 09:41:21.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2928
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.181
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.293
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:41:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:41:21.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:41:21.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:41:21.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:41:21.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:41:21.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:41:22.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:41:22.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:41:23.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:41:24.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:41:24.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:41:25.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:41:26.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:41:26.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:41:27.830 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:41:27.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:41:27.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 09:41:27.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:41:27.843 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.93 ms, Average inference time: 8.38 ms

2025-07-30 09:41:27.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:41:27.932 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:41:28.006 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch262
2025-07-30 09:41:31.266 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 4.2, cls_loss: 0.7, lr: 8.724e-05, size: 512, ETA: 0:14:07
2025-07-30 09:41:34.701 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 8.656e-05, size: 576, ETA: 0:14:04
2025-07-30 09:41:37.933 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.157s, 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: 8.587e-05, size: 288, ETA: 0:14:01
2025-07-30 09:41:41.208 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.8, lr: 8.520e-05, size: 480, ETA: 0:13:57
2025-07-30 09:41:44.506 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 8.452e-05, size: 384, ETA: 0:13:54
2025-07-30 09:41:47.847 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 262/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.3, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.5, lr: 8.384e-05, size: 512, ETA: 0:13:50
2025-07-30 09:41:49.286 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:41:56.046 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:41:56.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:41:56.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3826
2025-07-30 09:41:57.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3411
2025-07-30 09:41:57.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1865
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3034
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.187
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.303
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:41:57.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:41:57.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:41:57.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:41:57.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:41:57.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:41:57.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:41:57.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:41:57.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:41:58.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:41:58.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:41:58.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:41:59.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:41:59.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:42:00.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:42:00.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:42:01.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:42:01.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:42:01.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 09:42:01.111 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:42:01.118 | 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

2025-07-30 09:42:01.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:42:01.197 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:42:01.274 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch263
2025-07-30 09:42:04.368 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 8.287e-05, size: 320, ETA: 0:13:45
2025-07-30 09:42:07.591 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 8.220e-05, size: 320, ETA: 0:13:42
2025-07-30 09:42:10.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 8.154e-05, size: 320, ETA: 0:13:38
2025-07-30 09:42:14.089 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.8, lr: 8.087e-05, size: 512, ETA: 0:13:35
2025-07-30 09:42:17.273 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.7, lr: 8.021e-05, size: 384, ETA: 0:13:32
2025-07-30 09:42:20.637 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 263/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 7.956e-05, size: 320, ETA: 0:13:28
2025-07-30 09:42:22.049 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:42:29.003 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:42:29.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:42:30.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4261
2025-07-30 09:42:30.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4270
2025-07-30 09:42:30.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2072
2025-07-30 09:42:30.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3534
2025-07-30 09:42:30.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:42:30.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:42:30.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-07-30 09:42:30.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-07-30 09:42:30.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-07-30 09:42:30.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-07-30 09:42:30.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:42:30.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:42:30.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:42:30.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:42:30.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:42:30.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:42:30.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:42:30.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:42:30.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:42:31.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:42:31.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:42:32.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:42:33.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:42:33.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:42:34.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:42:34.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:42:35.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:42:36.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:42:36.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:42:36.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:42:36.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:42:36.114 | 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

2025-07-30 09:42:36.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:42:36.189 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:42:36.266 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch264
2025-07-30 09:42:39.615 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 7.861e-05, size: 416, ETA: 0:13:23
2025-07-30 09:42:42.952 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 7.795e-05, size: 512, ETA: 0:13:20
2025-07-30 09:42:46.169 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.8, lr: 7.731e-05, size: 512, ETA: 0:13:16
2025-07-30 09:42:49.492 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 7.666e-05, size: 512, ETA: 0:13:13
2025-07-30 09:42:52.776 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.162s, 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: 7.602e-05, size: 256, ETA: 0:13:10
2025-07-30 09:42:56.096 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 264/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.7, lr: 7.538e-05, size: 352, ETA: 0:13:06
2025-07-30 09:42:57.561 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:43:04.277 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:43:05.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:43:05.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4372
2025-07-30 09:43:05.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4052
2025-07-30 09:43:05.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2309
2025-07-30 09:43:05.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3578
2025-07-30 09:43:05.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:43:05.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:43:05.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-07-30 09:43:05.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-07-30 09:43:05.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.358
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:43:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:43:06.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:43:06.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:43:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:43:08.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:43:08.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:43:09.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:43:09.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:43:10.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:43:10.825 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:43:10.825 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:43:10.826 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:43:10.826 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:43:10.834 | 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: 8.29 ms

2025-07-30 09:43:10.835 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:43:10.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:43:10.991 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch265
2025-07-30 09:43:14.110 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.6, lr: 7.445e-05, size: 320, ETA: 0:13:01
2025-07-30 09:43:17.366 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 7.382e-05, size: 288, ETA: 0:12:58
2025-07-30 09:43:20.680 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 7.318e-05, size: 544, ETA: 0:12:54
2025-07-30 09:43:24.036 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 7.255e-05, size: 512, ETA: 0:12:51
2025-07-30 09:43:27.547 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.174s, 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: 7.193e-05, size: 576, ETA: 0:12:48
2025-07-30 09:43:31.157 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 265/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.175s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 7.130e-05, size: 544, ETA: 0:12:44
2025-07-30 09:43:32.648 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:43:39.612 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:43:40.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:43:40.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4399
2025-07-30 09:43:40.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3751
2025-07-30 09:43:40.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2436
2025-07-30 09:43:40.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3528
2025-07-30 09:43:40.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:43:40.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:43:40.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:43:40.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:43:40.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:43:40.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:43:41.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:43:41.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:43:42.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:43:43.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:43:43.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:43:44.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:43:44.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:43:45.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:43:45.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:43:45.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:43:45.680 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:43:45.680 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:43:45.687 | 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

2025-07-30 09:43:45.688 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:43:45.804 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:43:45.879 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch266
2025-07-30 09:43:49.014 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 7.040e-05, size: 544, ETA: 0:12:39
2025-07-30 09:43:52.523 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.8, lr: 6.979e-05, size: 352, ETA: 0:12:36
2025-07-30 09:43:56.111 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.175s, 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: 6.917e-05, size: 576, ETA: 0:12:33
2025-07-30 09:43:59.509 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 6.856e-05, size: 448, ETA: 0:12:29
2025-07-30 09:44:02.903 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.4, cls_loss: 0.7, lr: 6.795e-05, size: 288, ETA: 0:12:26
2025-07-30 09:44:06.050 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 266/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.9, lr: 6.734e-05, size: 480, ETA: 0:12:22
2025-07-30 09:44:07.689 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:44:14.535 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:44:15.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:44:16.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4290
2025-07-30 09:44:16.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3811
2025-07-30 09:44:16.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1307
2025-07-30 09:44:16.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3136
2025-07-30 09:44:16.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:44:16.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:44:16.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-07-30 09:44:16.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-07-30 09:44:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.131
2025-07-30 09:44:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.314
2025-07-30 09:44:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:44:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:44:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:44:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:44:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:44:16.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:44:16.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:44:16.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:44:16.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:44:17.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:44:17.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:44:18.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:44:19.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:44:19.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:44:20.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:44:21.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:44:22.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:44:22.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:44:22.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 09:44:22.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 09:44:22.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:44:22.848 | 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

2025-07-30 09:44:22.849 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:44:22.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:44:23.015 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch267
2025-07-30 09:44:26.249 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.7, lr: 6.646e-05, size: 256, ETA: 0:12:18
2025-07-30 09:44:29.535 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 6.586e-05, size: 448, ETA: 0:12:14
2025-07-30 09:44:32.779 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 6.527e-05, size: 544, ETA: 0:12:11
2025-07-30 09:44:36.210 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 6.467e-05, size: 384, ETA: 0:12:07
2025-07-30 09:44:39.501 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 6.408e-05, size: 416, ETA: 0:12:04
2025-07-30 09:44:42.780 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 267/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 6.349e-05, size: 320, ETA: 0:12:00
2025-07-30 09:44:44.218 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:44:51.098 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:44:51.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:44:52.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4158
2025-07-30 09:44:52.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3998
2025-07-30 09:44:52.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2171
2025-07-30 09:44:52.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3442
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.344
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:44:52.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:44:52.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:44:52.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:44:52.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:44:52.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:44:52.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:44:53.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:44:53.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:44:54.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:44:54.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:44:55.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:44:56.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:44:56.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:44:57.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:44:57.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:44:57.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:44:57.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:44:57.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:44:57.792 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.31 ms, Average NMS time: 0.91 ms, Average inference time: 8.22 ms

2025-07-30 09:44:57.793 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:44:57.869 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:44:57.946 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch268
2025-07-30 09:45:01.099 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, 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: 6.264e-05, size: 448, ETA: 0:11:56
2025-07-30 09:45:04.286 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, 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: 6.205e-05, size: 288, ETA: 0:11:52
2025-07-30 09:45:07.569 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 6.147e-05, size: 576, ETA: 0:11:49
2025-07-30 09:45:10.959 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 6.089e-05, size: 320, ETA: 0:11:45
2025-07-30 09:45:14.199 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 6.032e-05, size: 544, ETA: 0:11:42
2025-07-30 09:45:17.696 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 268/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.7, lr: 5.974e-05, size: 576, ETA: 0:11:39
2025-07-30 09:45:19.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:45:26.201 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:45:26.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:45:27.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4369
2025-07-30 09:45:27.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4055
2025-07-30 09:45:27.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1926
2025-07-30 09:45:27.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3450
2025-07-30 09:45:27.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:45:27.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:45:27.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-07-30 09:45:27.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-07-30 09:45:27.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.345
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:45:27.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:45:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:45:28.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:45:28.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:45:29.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:45:29.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:45:30.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:45:30.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:45:31.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:45:31.536 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:45:31.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:45:31.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:45:31.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:45:31.544 | 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

2025-07-30 09:45:31.545 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:45:31.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:45:31.760 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch269
2025-07-30 09:45:35.204 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 5.892e-05, size: 576, ETA: 0:11:34
2025-07-30 09:45:38.530 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.6, lr: 5.835e-05, size: 448, ETA: 0:11:30
2025-07-30 09:45:41.840 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 5.779e-05, size: 384, ETA: 0:11:27
2025-07-30 09:45:45.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 1.0, lr: 5.723e-05, size: 352, ETA: 0:11:23
2025-07-30 09:45:48.304 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.6, lr: 5.667e-05, size: 288, ETA: 0:11:20
2025-07-30 09:45:51.564 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 269/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 5.611e-05, size: 480, ETA: 0:11:17
2025-07-30 09:45:53.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:45:59.866 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:46:00.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:46:01.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4752
2025-07-30 09:46:01.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4232
2025-07-30 09:46:01.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2157
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3714
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-07-30 09:46:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:46:01.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:46:02.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:46:03.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:46:04.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:46:04.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:46:05.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:46:06.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:46:07.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:46:08.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:46:08.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:46:08.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:46:08.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:46:08.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:46:08.869 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.91 ms, Average inference time: 8.37 ms

2025-07-30 09:46:08.876 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:46:08.951 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:46:09.028 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch270
2025-07-30 09:46:12.174 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 5.531e-05, size: 448, ETA: 0:11:12
2025-07-30 09:46:15.494 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 5.476e-05, size: 512, ETA: 0:11:08
2025-07-30 09:46:18.654 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 1.1, lr: 5.421e-05, size: 480, ETA: 0:11:05
2025-07-30 09:46:21.935 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 5.367e-05, size: 416, ETA: 0:11:01
2025-07-30 09:46:25.240 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.6, lr: 5.313e-05, size: 480, ETA: 0:10:58
2025-07-30 09:46:28.575 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 270/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 5.259e-05, size: 256, ETA: 0:10:55
2025-07-30 09:46:29.972 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:46:36.905 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:46:37.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:46:38.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3503
2025-07-30 09:46:38.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3087
2025-07-30 09:46:38.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1226
2025-07-30 09:46:38.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2605
2025-07-30 09:46:38.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:46:38.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:46:38.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-07-30 09:46:38.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-07-30 09:46:38.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.123
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.261
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:46:38.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:46:39.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:46:39.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:46:40.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:46:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:46:41.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:46:42.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:46:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:46:43.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:46:44.305 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:46:44.305 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.12
2025-07-30 09:46:44.305 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.26
2025-07-30 09:46:44.305 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:46:44.313 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.32 ms, Average NMS time: 0.90 ms, Average inference time: 8.23 ms

2025-07-30 09:46:44.314 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:46:44.391 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:46:44.465 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch271
2025-07-30 09:46:47.817 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.0, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.8, lr: 5.181e-05, size: 512, ETA: 0:10:50
2025-07-30 09:46:50.941 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 5.128e-05, size: 352, ETA: 0:10:46
2025-07-30 09:46:54.209 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.7, lr: 5.075e-05, size: 352, ETA: 0:10:43
2025-07-30 09:46:57.394 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 4.2, cls_loss: 0.8, lr: 5.022e-05, size: 320, ETA: 0:10:40
2025-07-30 09:47:00.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 4.970e-05, size: 352, ETA: 0:10:36
2025-07-30 09:47:03.781 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 271/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 4.918e-05, size: 416, ETA: 0:10:33
2025-07-30 09:47:05.298 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:47:12.221 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:47:13.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:47:13.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4251
2025-07-30 09:47:13.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3559
2025-07-30 09:47:13.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2206
2025-07-30 09:47:13.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3339
2025-07-30 09:47:13.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:47:13.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.334
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:47:13.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:47:13.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:47:13.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:47:14.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:47:15.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:47:16.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:47:17.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:47:17.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:47:18.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:47:19.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:47:20.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:47:20.812 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:47:20.812 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:47:20.812 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 09:47:20.812 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:47:20.827 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.89 ms, Average inference time: 8.32 ms

2025-07-30 09:47:20.833 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:47:20.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:47:21.014 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch272
2025-07-30 09:47:24.174 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.7, lr: 4.843e-05, size: 352, ETA: 0:10:28
2025-07-30 09:47:27.703 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.8, lr: 4.791e-05, size: 448, ETA: 0:10:24
2025-07-30 09:47:30.983 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.8, cls_loss: 0.7, lr: 4.740e-05, size: 416, ETA: 0:10:21
2025-07-30 09:47:34.383 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.8, lr: 4.689e-05, size: 448, ETA: 0:10:18
2025-07-30 09:47:37.668 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 4.638e-05, size: 320, ETA: 0:10:14
2025-07-30 09:47:40.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 272/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 4.588e-05, size: 384, ETA: 0:10:11
2025-07-30 09:47:42.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:47:49.343 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:47:50.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:47:51.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4283
2025-07-30 09:47:51.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3392
2025-07-30 09:47:51.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1832
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3169
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.183
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.317
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:47:51.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:47:51.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:47:51.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:47:51.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:47:51.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:47:51.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:47:51.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:47:52.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:47:53.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:47:53.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:47:54.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:47:55.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:47:55.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:47:56.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:47:57.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:47:57.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:47:57.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:47:57.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 09:47:57.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:47:57.895 | 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

2025-07-30 09:47:57.896 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:47:57.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:47:58.052 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch273
2025-07-30 09:48:01.355 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.5, lr: 4.515e-05, size: 544, ETA: 0:10:06
2025-07-30 09:48:04.842 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 9.1, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 4.3, cls_loss: 0.8, lr: 4.465e-05, size: 576, ETA: 0:10:03
2025-07-30 09:48:07.975 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 9.8, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 4.4, cls_loss: 0.9, lr: 4.416e-05, size: 256, ETA: 0:09:59
2025-07-30 09:48:11.463 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.170s, 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: 4.367e-05, size: 448, ETA: 0:09:56
2025-07-30 09:48:14.785 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 4.318e-05, size: 256, ETA: 0:09:52
2025-07-30 09:48:17.917 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 273/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 4.269e-05, size: 352, ETA: 0:09:49
2025-07-30 09:48:19.371 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:48:26.382 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:48:27.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:48:28.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4725
2025-07-30 09:48:28.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4312
2025-07-30 09:48:28.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2350
2025-07-30 09:48:28.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3796
2025-07-30 09:48:28.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:48:28.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:48:28.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-07-30 09:48:28.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-07-30 09:48:28.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.235
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.380
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:48:28.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:48:28.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:48:29.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:48:29.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:48:30.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:48:31.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:48:32.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:48:32.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:48:33.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:48:34.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:48:35.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:48:35.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:48:35.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 09:48:35.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:48:35.144 | 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

2025-07-30 09:48:35.145 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:48:35.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:48:35.346 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch274
2025-07-30 09:48:38.384 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.147s, 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: 4.199e-05, size: 416, ETA: 0:09:44
2025-07-30 09:48:41.626 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 4.2, cls_loss: 0.7, lr: 4.151e-05, size: 384, ETA: 0:09:41
2025-07-30 09:48:44.943 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 4.103e-05, size: 320, ETA: 0:09:37
2025-07-30 09:48:48.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 4.056e-05, size: 352, ETA: 0:09:34
2025-07-30 09:48:51.473 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.9, lr: 4.008e-05, size: 576, ETA: 0:09:30
2025-07-30 09:48:54.964 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 274/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 3.961e-05, size: 512, ETA: 0:09:27
2025-07-30 09:48:56.468 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:49:03.258 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:49:04.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:49:04.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4440
2025-07-30 09:49:04.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4268
2025-07-30 09:49:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2412
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3707
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.241
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.371
2025-07-30 09:49:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:49:04.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:49:05.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:49:05.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:49:06.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:49:06.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:49:07.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:49:07.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:49:08.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:49:09.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:49:09.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:49:09.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:49:09.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:49:09.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:49:09.557 | 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

2025-07-30 09:49:09.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:49:09.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:49:09.740 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch275
2025-07-30 09:49:13.052 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.7, lr: 3.894e-05, size: 256, ETA: 0:09:22
2025-07-30 09:49:16.454 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 3.6, cls_loss: 0.8, lr: 3.848e-05, size: 256, ETA: 0:09:19
2025-07-30 09:49:19.655 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.8, lr: 3.802e-05, size: 256, ETA: 0:09:15
2025-07-30 09:49:23.078 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.004s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 3.756e-05, size: 352, ETA: 0:09:12
2025-07-30 09:49:26.602 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 3.710e-05, size: 320, ETA: 0:09:09
2025-07-30 09:49:29.869 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 275/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.9, lr: 3.665e-05, size: 416, ETA: 0:09:05
2025-07-30 09:49:31.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:49:38.310 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:49:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:49:40.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4422
2025-07-30 09:49:40.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3928
2025-07-30 09:49:40.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2073
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3474
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.207
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.347
2025-07-30 09:49:40.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:49:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:49:41.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:49:42.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:49:43.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:49:43.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:49:44.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:49:45.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:49:46.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:49:47.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:49:47.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:49:47.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:49:47.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:49:47.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:49:47.961 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.56 ms, Average NMS time: 0.88 ms, Average inference time: 8.44 ms

2025-07-30 09:49:47.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:49:48.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:49:48.113 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch276
2025-07-30 09:49:51.410 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, 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: 3.600e-05, size: 512, ETA: 0:09:00
2025-07-30 09:49:54.551 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 3.556e-05, size: 416, ETA: 0:08:57
2025-07-30 09:49:57.797 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 3.511e-05, size: 480, ETA: 0:08:53
2025-07-30 09:50:01.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 3.467e-05, size: 544, ETA: 0:08:50
2025-07-30 09:50:04.550 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.004s, total_loss: 8.2, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.7, lr: 3.424e-05, size: 544, ETA: 0:08:47
2025-07-30 09:50:07.954 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 276/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 3.380e-05, size: 576, ETA: 0:08:43
2025-07-30 09:50:09.425 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:50:16.392 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:50:17.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:50:17.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4874
2025-07-30 09:50:17.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4534
2025-07-30 09:50:17.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2162
2025-07-30 09:50:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3857
2025-07-30 09:50:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:50:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:50:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-07-30 09:50:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-07-30 09:50:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.386
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:50:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:50:18.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:50:19.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:50:19.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:50:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:50:20.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:50:21.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:50:22.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:50:22.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:50:23.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:50:23.447 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-07-30 09:50:23.447 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-07-30 09:50:23.447 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:50:23.454 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.46 ms, Average NMS time: 0.91 ms, Average inference time: 8.37 ms

2025-07-30 09:50:23.456 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:50:23.538 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:50:23.664 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch277
2025-07-30 09:50:27.078 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, 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: 3.318e-05, size: 384, ETA: 0:08:38
2025-07-30 09:50:30.367 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 3.275e-05, size: 512, ETA: 0:08:35
2025-07-30 09:50:33.785 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, 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: 3.233e-05, size: 576, ETA: 0:08:32
2025-07-30 09:50:37.217 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 3.190e-05, size: 544, ETA: 0:08:28
2025-07-30 09:50:40.532 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 3.148e-05, size: 352, ETA: 0:08:25
2025-07-30 09:50:43.702 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 277/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 2.1, cls_loss: 0.6, lr: 3.107e-05, size: 512, ETA: 0:08:21
2025-07-30 09:50:45.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:50:51.928 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:50:52.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:50:53.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4488
2025-07-30 09:50:53.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3880
2025-07-30 09:50:53.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2606
2025-07-30 09:50:53.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3658
2025-07-30 09:50:53.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:50:53.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:50:53.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-07-30 09:50:53.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-07-30 09:50:53.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.261
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.366
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:50:53.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:50:54.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:50:54.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:50:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:50:55.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:50:56.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:50:56.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:50:57.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:50:57.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:50:58.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:50:58.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:50:58.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-07-30 09:50:58.535 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:50:58.542 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.58 ms, Average NMS time: 0.91 ms, Average inference time: 8.49 ms

2025-07-30 09:50:58.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:50:58.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:50:58.695 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch278
2025-07-30 09:51:01.952 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 3.047e-05, size: 256, ETA: 0:08:17
2025-07-30 09:51:05.304 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 1.6, cls_loss: 0.6, lr: 3.006e-05, size: 576, ETA: 0:08:13
2025-07-30 09:51:08.668 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.8, lr: 2.965e-05, size: 256, ETA: 0:08:10
2025-07-30 09:51:11.914 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, 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: 2.925e-05, size: 288, ETA: 0:08:06
2025-07-30 09:51:15.089 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 2.884e-05, size: 544, ETA: 0:08:03
2025-07-30 09:51:18.541 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 278/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 4.3, cls_loss: 0.8, lr: 2.844e-05, size: 544, ETA: 0:08:00
2025-07-30 09:51:20.041 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:51:26.893 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:51:27.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:51:28.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4455
2025-07-30 09:51:28.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3869
2025-07-30 09:51:28.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2382
2025-07-30 09:51:28.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3569
2025-07-30 09:51:28.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:51:28.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:51:28.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-07-30 09:51:28.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:51:28.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:51:28.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:51:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:51:29.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:51:29.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:51:30.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:51:30.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:51:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:51:32.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:51:32.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:51:33.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:51:33.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:51:33.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:51:33.304 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:51:33.312 | 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: 8.28 ms

2025-07-30 09:51:33.313 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:51:33.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:51:33.542 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch279
2025-07-30 09:51:36.759 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 2.787e-05, size: 448, ETA: 0:07:55
2025-07-30 09:51:40.092 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 2.748e-05, size: 448, ETA: 0:07:51
2025-07-30 09:51:43.493 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.9, lr: 2.709e-05, size: 576, ETA: 0:07:48
2025-07-30 09:51:46.949 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 2.670e-05, size: 544, ETA: 0:07:45
2025-07-30 09:51:50.327 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 2.632e-05, size: 448, ETA: 0:07:41
2025-07-30 09:51:53.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 279/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 2.594e-05, size: 448, ETA: 0:07:38
2025-07-30 09:51:55.243 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:52:02.133 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:52:02.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:52:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4574
2025-07-30 09:52:03.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4018
2025-07-30 09:52:03.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1571
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3388
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.157
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-07-30 09:52:03.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:52:03.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:52:04.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:52:04.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:52:05.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:52:06.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:52:06.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:52:07.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:52:07.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:52:08.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:52:08.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:52:08.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:52:08.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 09:52:08.923 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:52:08.930 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.36 ms, Average NMS time: 0.87 ms, Average inference time: 8.23 ms

2025-07-30 09:52:08.932 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:52:09.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:52:09.093 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch280
2025-07-30 09:52:12.309 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.8, lr: 2.539e-05, size: 256, ETA: 0:07:33
2025-07-30 09:52:15.491 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.1, cls_loss: 0.7, lr: 2.501e-05, size: 256, ETA: 0:07:30
2025-07-30 09:52:18.748 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 2.464e-05, size: 320, ETA: 0:07:26
2025-07-30 09:52:22.036 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 2.427e-05, size: 256, ETA: 0:07:23
2025-07-30 09:52:25.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 2.391e-05, size: 480, ETA: 0:07:19
2025-07-30 09:52:28.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 280/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.9, lr: 2.354e-05, size: 576, ETA: 0:07:16
2025-07-30 09:52:30.136 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:52:37.044 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:52:37.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:52:37.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3335
2025-07-30 09:52:37.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3403
2025-07-30 09:52:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1360
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2700
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.136
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.270
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:52:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:52:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:52:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:52:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:52:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:52:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:52:37.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:52:38.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:52:38.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:52:38.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:52:39.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:52:39.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:52:39.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:52:40.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:52:40.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:52:41.006 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:52:41.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.13
2025-07-30 09:52:41.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.27
2025-07-30 09:52:41.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:52:41.013 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.84 ms, Average inference time: 8.26 ms

2025-07-30 09:52:41.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:52:41.086 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:52:41.160 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch281
2025-07-30 09:52:44.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.8, lr: 2.302e-05, size: 256, ETA: 0:07:11
2025-07-30 09:52:48.126 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.178s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 2.266e-05, size: 416, ETA: 0:07:08
2025-07-30 09:52:51.499 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.231e-05, size: 448, ETA: 0:07:04
2025-07-30 09:52:54.804 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 1.0, lr: 2.196e-05, size: 512, ETA: 0:07:01
2025-07-30 09:52:57.986 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.9, lr: 2.161e-05, size: 256, ETA: 0:06:58
2025-07-30 09:53:01.246 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 281/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.9, lr: 2.126e-05, size: 480, ETA: 0:06:54
2025-07-30 09:53:02.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:53:09.573 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:53:10.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:53:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4597
2025-07-30 09:53:11.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3718
2025-07-30 09:53:11.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2145
2025-07-30 09:53:11.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3487
2025-07-30 09:53:11.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.215
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.349
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:53:11.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:53:11.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:53:11.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:53:11.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:53:11.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:53:11.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:53:12.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:53:12.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:53:13.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:53:14.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:53:14.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:53:15.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:53:16.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:53:16.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:53:16.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:53:16.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:53:16.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:53:16.816 | 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

2025-07-30 09:53:16.821 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:53:16.900 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:53:16.977 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch282
2025-07-30 09:53:20.023 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.6, lr: 2.077e-05, size: 320, ETA: 0:06:49
2025-07-30 09:53:23.252 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 9.1, iou_loss: 2.7, l1_loss: 1.9, conf_loss: 3.8, cls_loss: 0.8, lr: 2.043e-05, size: 544, ETA: 0:06:46
2025-07-30 09:53:26.643 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.6, lr: 2.009e-05, size: 320, ETA: 0:06:43
2025-07-30 09:53:30.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.976e-05, size: 576, ETA: 0:06:39
2025-07-30 09:53:33.487 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.170s, data_time: 0.005s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.943e-05, size: 544, ETA: 0:06:36
2025-07-30 09:53:36.887 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 282/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, 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: 1.910e-05, size: 416, ETA: 0:06:32
2025-07-30 09:53:38.309 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:53:44.997 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:53:45.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:53:46.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4896
2025-07-30 09:53:46.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4390
2025-07-30 09:53:46.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2181
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3822
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.218
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-07-30 09:53:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:53:46.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:53:46.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:53:47.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:53:47.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:53:48.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:53:49.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:53:49.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:53:50.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:53:50.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:53:51.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:53:51.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:53:51.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 09:53:51.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:53:51.242 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.53 ms, Average NMS time: 0.83 ms, Average inference time: 8.36 ms

2025-07-30 09:53:51.243 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:53:51.323 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:53:51.399 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch283
2025-07-30 09:53:54.601 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.6, cls_loss: 0.7, lr: 1.863e-05, size: 544, ETA: 0:06:27
2025-07-30 09:53:58.085 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 1.831e-05, size: 448, ETA: 0:06:24
2025-07-30 09:54:01.303 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 0.7, lr: 1.799e-05, size: 320, ETA: 0:06:21
2025-07-30 09:54:04.647 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.767e-05, size: 352, ETA: 0:06:17
2025-07-30 09:54:08.033 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.736e-05, size: 512, ETA: 0:06:14
2025-07-30 09:54:11.289 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 283/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 1.705e-05, size: 544, ETA: 0:06:11
2025-07-30 09:54:12.872 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:54:19.654 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:54:20.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:54:20.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3779
2025-07-30 09:54:20.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3764
2025-07-30 09:54:20.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1384
2025-07-30 09:54:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2976
2025-07-30 09:54:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:54:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:54:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-07-30 09:54:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-07-30 09:54:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.138
2025-07-30 09:54:20.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.298
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:54:20.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:54:21.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:54:21.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:54:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:54:22.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:54:23.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:54:23.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:54:23.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:54:24.224 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:54:24.224 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:54:24.224 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.30
2025-07-30 09:54:24.224 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:54:24.231 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.54 ms, Average NMS time: 0.80 ms, Average inference time: 8.34 ms

2025-07-30 09:54:24.232 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:54:24.346 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:54:24.429 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch284
2025-07-30 09:54:27.823 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 1.660e-05, size: 480, ETA: 0:06:06
2025-07-30 09:54:30.990 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.630e-05, size: 320, ETA: 0:06:02
2025-07-30 09:54:34.385 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 1.4, conf_loss: 2.2, cls_loss: 0.7, lr: 1.600e-05, size: 256, ETA: 0:05:59
2025-07-30 09:54:37.511 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.570e-05, size: 384, ETA: 0:05:56
2025-07-30 09:54:40.773 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.541e-05, size: 384, ETA: 0:05:52
2025-07-30 09:54:44.008 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 284/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.511e-05, size: 416, ETA: 0:05:49
2025-07-30 09:54:45.595 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:54:52.604 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:54:53.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:54:54.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4603
2025-07-30 09:54:54.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4497
2025-07-30 09:54:54.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1839
2025-07-30 09:54:54.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3647
2025-07-30 09:54:54.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:54:54.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:54:54.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.450
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.184
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.365
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:54:54.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:54:54.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:54:54.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:54:55.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:54:56.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:54:56.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:54:57.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:54:58.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:54:59.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:54:59.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:55:00.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:55:00.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:55:00.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:55:00.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:55:00.442 | 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

2025-07-30 09:55:00.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:55:00.527 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:55:00.604 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch285
2025-07-30 09:55:04.015 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.170s, 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: 1.470e-05, size: 576, ETA: 0:05:44
2025-07-30 09:55:07.395 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 1.441e-05, size: 512, ETA: 0:05:41
2025-07-30 09:55:10.782 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.413e-05, size: 544, ETA: 0:05:37
2025-07-30 09:55:14.280 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 1.385e-05, size: 512, ETA: 0:05:34
2025-07-30 09:55:17.660 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.357e-05, size: 576, ETA: 0:05:30
2025-07-30 09:55:21.304 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 285/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.176s, data_time: 0.001s, total_loss: 8.9, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 4.1, cls_loss: 0.8, lr: 1.330e-05, size: 576, ETA: 0:05:27
2025-07-30 09:55:22.813 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:55:29.769 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:55:30.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:55:30.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4713
2025-07-30 09:55:30.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4518
2025-07-30 09:55:31.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2378
2025-07-30 09:55:31.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3870
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:55:31.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:55:31.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:55:31.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:55:31.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:55:31.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:55:32.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:55:32.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:55:33.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:55:33.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:55:34.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:55:35.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:55:35.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:55:36.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:55:36.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 09:55:36.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-07-30 09:55:36.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:55:36.114 | 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: 8.27 ms

2025-07-30 09:55:36.119 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:55:36.218 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:55:36.334 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch286
2025-07-30 09:55:39.763 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 1.290e-05, size: 576, ETA: 0:05:22
2025-07-30 09:55:43.275 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.6, lr: 1.264e-05, size: 512, ETA: 0:05:19
2025-07-30 09:55:46.480 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.237e-05, size: 512, ETA: 0:05:15
2025-07-30 09:55:49.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.211e-05, size: 320, ETA: 0:05:12
2025-07-30 09:55:53.244 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.005s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 1.185e-05, size: 256, ETA: 0:05:09
2025-07-30 09:55:56.437 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 286/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.9, cls_loss: 0.7, lr: 1.159e-05, size: 544, ETA: 0:05:05
2025-07-30 09:55:57.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:56:04.654 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:56:05.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:56:05.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3879
2025-07-30 09:56:05.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3774
2025-07-30 09:56:05.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1772
2025-07-30 09:56:05.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3142
2025-07-30 09:56:05.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.177
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.314
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:56:05.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:56:05.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:56:05.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:56:06.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:56:06.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:56:07.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:56:08.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:56:08.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:56:09.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:56:09.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:56:10.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:56:10.943 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:56:10.943 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 09:56:10.943 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 09:56:10.944 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:56:10.951 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.33 ms, Average NMS time: 0.81 ms, Average inference time: 8.13 ms

2025-07-30 09:56:10.955 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:56:11.049 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:56:11.138 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch287
2025-07-30 09:56:14.513 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.123e-05, size: 576, ETA: 0:05:00
2025-07-30 09:56:17.884 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.9, lr: 1.098e-05, size: 320, ETA: 0:04:57
2025-07-30 09:56:20.997 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 1.0, lr: 1.073e-05, size: 320, ETA: 0:04:54
2025-07-30 09:56:24.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.049e-05, size: 448, ETA: 0:04:50
2025-07-30 09:56:27.596 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.005s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.024e-05, size: 288, ETA: 0:04:47
2025-07-30 09:56:31.133 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 287/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.171s, 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: 1.001e-05, size: 320, ETA: 0:04:43
2025-07-30 09:56:32.688 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:56:39.482 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:56:40.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:56:40.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4093
2025-07-30 09:56:40.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3659
2025-07-30 09:56:40.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2139
2025-07-30 09:56:40.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3297
2025-07-30 09:56:40.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:56:40.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.330
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:56:40.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:56:40.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:56:40.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:56:40.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:56:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:56:41.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:56:42.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:56:42.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:56:42.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:56:43.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:56:43.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:56:44.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:56:44.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:56:44.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:56:44.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 09:56:44.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:56:44.790 | 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

2025-07-30 09:56:44.791 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:56:44.863 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:56:44.984 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch288
2025-07-30 09:56:48.206 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.6, lr: 9.666e-06, size: 512, ETA: 0:04:39
2025-07-30 09:56:51.580 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 1.0, lr: 9.434e-06, size: 256, ETA: 0:04:35
2025-07-30 09:56:54.889 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 9.205e-06, size: 480, ETA: 0:04:32
2025-07-30 09:56:58.331 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 8.980e-06, size: 352, ETA: 0:04:28
2025-07-30 09:57:01.588 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 8.756e-06, size: 256, ETA: 0:04:25
2025-07-30 09:57:04.824 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 288/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 8.536e-06, size: 256, ETA: 0:04:22
2025-07-30 09:57:06.189 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:57:12.946 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:57:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:57:14.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4428
2025-07-30 09:57:14.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4017
2025-07-30 09:57:14.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2144
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3530
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.214
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-07-30 09:57:14.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:57:14.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:57:15.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:57:16.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:57:17.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:57:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:57:19.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:57:20.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:57:21.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:57:21.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:57:22.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:57:22.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:57:22.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:57:22.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:57:22.870 | 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: 8.29 ms

2025-07-30 09:57:22.877 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:57:22.950 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:57:23.028 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch289
2025-07-30 09:57:26.106 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 8.221e-06, size: 544, ETA: 0:04:17
2025-07-30 09:57:29.359 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.9, lr: 8.008e-06, size: 288, ETA: 0:04:13
2025-07-30 09:57:32.598 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 7.797e-06, size: 288, ETA: 0:04:10
2025-07-30 09:57:36.037 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 1.9, cls_loss: 0.5, lr: 7.589e-06, size: 256, ETA: 0:04:07
2025-07-30 09:57:39.258 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.5, lr: 7.384e-06, size: 448, ETA: 0:04:03
2025-07-30 09:57:42.671 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 289/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, 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: 7.182e-06, size: 544, ETA: 0:04:00
2025-07-30 09:57:44.193 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:57:51.090 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:57:51.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:57:52.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3668
2025-07-30 09:57:52.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3423
2025-07-30 09:57:52.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1412
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2834
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.141
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.283
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:57:52.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:57:52.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:57:52.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:57:52.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:57:52.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:57:52.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:57:52.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:57:53.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:57:53.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:57:54.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:57:54.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:57:55.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:57:56.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:57:56.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:57:57.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:57:58.028 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:57:58.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 09:57:58.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-07-30 09:57:58.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:57:58.036 | 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

2025-07-30 09:57:58.037 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:57:58.109 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:57:58.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch290
2025-07-30 09:58:01.432 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 6.893e-06, size: 512, ETA: 0:03:55
2025-07-30 09:58:04.810 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, 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: 6.698e-06, size: 576, ETA: 0:03:52
2025-07-30 09:58:08.419 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.175s, 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: 6.505e-06, size: 448, ETA: 0:03:48
2025-07-30 09:58:11.725 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 6.315e-06, size: 448, ETA: 0:03:45
2025-07-30 09:58:15.007 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 6.128e-06, size: 512, ETA: 0:03:42
2025-07-30 09:58:18.279 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 290/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 5.944e-06, size: 480, ETA: 0:03:38
2025-07-30 09:58:19.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:58:26.764 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:58:27.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:58:28.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3718
2025-07-30 09:58:28.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3186
2025-07-30 09:58:28.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1928
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2944
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.193
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.294
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:58:28.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:58:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:58:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:58:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:58:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:58:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:58:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:58:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:58:29.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:58:29.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:58:30.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:58:31.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:58:31.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:58:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:58:33.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:58:33.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:58:34.650 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:58:34.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-07-30 09:58:34.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 09:58:34.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:58:34.659 | 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

2025-07-30 09:58:34.665 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:58:34.742 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:58:34.842 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch291
2025-07-30 09:58:37.987 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 5.682e-06, size: 512, ETA: 0:03:33
2025-07-30 09:58:41.561 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 5.504e-06, size: 576, ETA: 0:03:30
2025-07-30 09:58:44.893 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 5.330e-06, size: 448, ETA: 0:03:27
2025-07-30 09:58:48.346 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.7, lr: 5.158e-06, size: 512, ETA: 0:03:23
2025-07-30 09:58:51.670 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 4.989e-06, size: 384, ETA: 0:03:20
2025-07-30 09:58:54.892 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 291/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, 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: 4.823e-06, size: 320, ETA: 0:03:16
2025-07-30 09:58:56.362 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:59:03.152 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:59:04.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:59:04.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4576
2025-07-30 09:59:05.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3853
2025-07-30 09:59:05.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2149
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3526
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.215
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.353
2025-07-30 09:59:05.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:59:05.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:59:05.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:59:05.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:59:05.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:59:05.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:59:05.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:59:05.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:59:05.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:59:06.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:59:07.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:59:07.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:59:08.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:59:09.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:59:10.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:59:11.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:59:12.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:59:13.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:59:13.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 09:59:13.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-07-30 09:59:13.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:59:13.229 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.43 ms, Average NMS time: 0.89 ms, Average inference time: 8.32 ms

2025-07-30 09:59:13.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:59:13.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:59:13.428 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch292
2025-07-30 09:59:16.788 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 4.587e-06, size: 544, ETA: 0:03:12
2025-07-30 09:59:20.004 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 4.428e-06, size: 288, ETA: 0:03:08
2025-07-30 09:59:23.527 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.172s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 4.271e-06, size: 416, ETA: 0:03:05
2025-07-30 09:59:26.868 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.7, lr: 4.118e-06, size: 352, ETA: 0:03:01
2025-07-30 09:59:30.336 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.170s, 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: 3.967e-06, size: 576, ETA: 0:02:58
2025-07-30 09:59:33.665 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 292/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 9.3, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 4.1, cls_loss: 0.8, lr: 3.819e-06, size: 352, ETA: 0:02:55
2025-07-30 09:59:35.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:59:41.963 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 09:59:42.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 09:59:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4566
2025-07-30 09:59:43.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3937
2025-07-30 09:59:43.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2213
2025-07-30 09:59:43.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3572
2025-07-30 09:59:43.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.221
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.357
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 09:59:43.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 09:59:43.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 09:59:43.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 09:59:43.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 09:59:43.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 09:59:44.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 09:59:44.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 09:59:45.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 09:59:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 09:59:46.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 09:59:47.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 09:59:47.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 09:59:48.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 09:59:49.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 09:59:49.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 09:59:49.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 09:59:49.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 09:59:49.121 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:367 - 
Average forward time: 7.38 ms, Average NMS time: 0.89 ms, Average inference time: 8.27 ms

2025-07-30 09:59:49.123 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:59:49.237 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 09:59:49.320 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch293
2025-07-30 09:59:52.568 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.1, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.8, lr: 3.609e-06, size: 416, ETA: 0:02:50
2025-07-30 09:59:55.926 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.7, lr: 3.468e-06, size: 544, ETA: 0:02:46
2025-07-30 09:59:59.214 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 3.330e-06, size: 288, ETA: 0:02:43
2025-07-30 10:00:02.647 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 3.194e-06, size: 576, ETA: 0:02:40
2025-07-30 10:00:06.055 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.6, lr: 3.061e-06, size: 544, ETA: 0:02:36
2025-07-30 10:00:09.312 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 293/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 1.0, lr: 2.931e-06, size: 256, ETA: 0:02:33
2025-07-30 10:00:10.819 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:00:17.710 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:00:18.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:00:19.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4257
2025-07-30 10:00:19.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3396
2025-07-30 10:00:19.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2013
2025-07-30 10:00:19.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3222
2025-07-30 10:00:19.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:00:19.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:00:19.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-07-30 10:00:19.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-07-30 10:00:19.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.201
2025-07-30 10:00:19.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.322
2025-07-30 10:00:19.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:00:19.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:00:19.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:00:19.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:00:19.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:00:19.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:00:19.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:00:19.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:00:19.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:00:20.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:00:21.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:00:22.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:00:22.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:00:23.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:00:24.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:00:25.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:00:26.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:00:27.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:00:27.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 10:00:27.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.32
2025-07-30 10:00:27.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:00:27.033 | 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

2025-07-30 10:00:27.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:00:27.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:00:27.182 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch294
2025-07-30 10:00:30.528 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.9, lr: 2.748e-06, size: 320, ETA: 0:02:28
2025-07-30 10:00:33.741 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 16.6, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 16.6, cls_loss: 0.0, lr: 2.625e-06, size: 512, ETA: 0:02:25
2025-07-30 10:00:37.236 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.8, lr: 2.505e-06, size: 288, ETA: 0:02:21
2025-07-30 10:00:40.524 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 2.387e-06, size: 544, ETA: 0:02:18
2025-07-30 10:00:43.769 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 2.273e-06, size: 320, ETA: 0:02:15
2025-07-30 10:00:47.022 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 294/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.9, lr: 2.161e-06, size: 288, ETA: 0:02:11
2025-07-30 10:00:48.461 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:00:55.279 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:00:56.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:00:57.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4968
2025-07-30 10:00:57.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4333
2025-07-30 10:00:57.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2632
2025-07-30 10:00:57.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3978
2025-07-30 10:00:57.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:00:57.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:00:57.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-07-30 10:00:57.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-07-30 10:00:57.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-07-30 10:00:57.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-07-30 10:00:57.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:00:57.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:00:57.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:00:57.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:00:57.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:00:57.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:00:57.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:00:57.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:00:57.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:00:58.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:00:59.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:00:59.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:01:00.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:01:01.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:01:02.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:01:03.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:01:04.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:01:05.125 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:01:05.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 10:01:05.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-07-30 10:01:05.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:01:05.134 | 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

2025-07-30 10:01:05.137 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:01:05.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:01:05.291 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch295
2025-07-30 10:01:08.657 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 2.004e-06, size: 288, ETA: 0:02:06
2025-07-30 10:01:11.940 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.899e-06, size: 352, ETA: 0:02:03
2025-07-30 10:01:15.241 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.797e-06, size: 544, ETA: 0:02:00
2025-07-30 10:01:18.542 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.698e-06, size: 448, ETA: 0:01:56
2025-07-30 10:01:21.927 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.8, lr: 1.601e-06, size: 512, ETA: 0:01:53
2025-07-30 10:01:25.196 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 295/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.508e-06, size: 288, ETA: 0:01:50
2025-07-30 10:01:26.594 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:01:33.352 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:01:34.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:01:34.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4632
2025-07-30 10:01:34.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4255
2025-07-30 10:01:34.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2565
2025-07-30 10:01:34.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3818
2025-07-30 10:01:34.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:01:34.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:01:34.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:01:34.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:01:34.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:01:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:01:35.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:01:36.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:01:36.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:01:37.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:01:37.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:01:38.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:01:38.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:01:39.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:01:39.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-07-30 10:01:39.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-07-30 10:01:39.335 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:01:39.342 | 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

2025-07-30 10:01:39.344 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:01:39.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:01:39.491 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch296
2025-07-30 10:01:42.700 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.5, lr: 1.377e-06, size: 576, ETA: 0:01:45
2025-07-30 10:01:46.027 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.290e-06, size: 384, ETA: 0:01:41
2025-07-30 10:01:49.417 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 5.7, iou_loss: 1.5, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.5, lr: 1.206e-06, size: 576, ETA: 0:01:38
2025-07-30 10:01:52.784 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.125e-06, size: 512, ETA: 0:01:35
2025-07-30 10:01:56.130 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.7, lr: 1.047e-06, size: 448, ETA: 0:01:31
2025-07-30 10:01:59.458 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 296/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.6, lr: 9.717e-07, size: 576, ETA: 0:01:28
2025-07-30 10:02:00.973 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:02:08.071 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:02:08.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:02:09.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4258
2025-07-30 10:02:09.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3944
2025-07-30 10:02:09.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1955
2025-07-30 10:02:09.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3386
2025-07-30 10:02:09.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:02:09.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.196
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.339
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:02:09.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:02:09.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:02:09.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:02:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:02:10.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:02:11.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:02:11.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:02:12.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:02:12.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:02:13.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:02:14.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:02:14.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:02:14.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-07-30 10:02:14.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.34
2025-07-30 10:02:14.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:02:14.675 | 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

2025-07-30 10:02:14.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:02:14.792 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:02:14.876 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch297
2025-07-30 10:02:17.921 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.8, lr: 8.674e-07, size: 416, ETA: 0:01:23
2025-07-30 10:02:21.265 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.9, lr: 7.988e-07, size: 320, ETA: 0:01:20
2025-07-30 10:02:24.687 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 7.331e-07, size: 256, ETA: 0:01:16
2025-07-30 10:02:27.984 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 6.702e-07, size: 480, ETA: 0:01:13
2025-07-30 10:02:31.272 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 6.102e-07, size: 544, ETA: 0:01:09
2025-07-30 10:02:34.511 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 297/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 5.529e-07, size: 416, ETA: 0:01:06
2025-07-30 10:02:35.950 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:02:42.543 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:02:43.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:02:43.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4604
2025-07-30 10:02:44.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4055
2025-07-30 10:02:44.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2171
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3610
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.217
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.361
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:02:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:02:44.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:02:44.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:02:44.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:02:44.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:02:44.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:02:44.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:02:44.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:02:45.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:02:45.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:02:46.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:02:46.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:02:47.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:02:48.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:02:48.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:02:49.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:02:49.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-07-30 10:02:49.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-07-30 10:02:49.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:02:49.109 | 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: 8.14 ms

2025-07-30 10:02:49.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:02:49.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:02:49.318 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch298
2025-07-30 10:02:52.511 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.8Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.4, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 2.8, cls_loss: 0.7, lr: 4.749e-07, size: 256, ETA: 0:01:01
2025-07-30 10:02:55.694 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.9, lr: 4.245e-07, size: 544, ETA: 0:00:58
2025-07-30 10:02:59.064 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 4.8, cls_loss: 0.7, lr: 3.770e-07, size: 288, ETA: 0:00:54
2025-07-30 10:03:02.510 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.8Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 3.323e-07, size: 384, ETA: 0:00:51
2025-07-30 10:03:05.838 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 2.904e-07, size: 384, ETA: 0:00:48
2025-07-30 10:03:09.115 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 298/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 2.514e-07, size: 288, ETA: 0:00:44
2025-07-30 10:03:10.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:03:17.325 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:03:18.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:03:18.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4268
2025-07-30 10:03:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3709
2025-07-30 10:03:18.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1938
2025-07-30 10:03:18.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3305
2025-07-30 10:03:18.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:03:18.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:03:18.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-07-30 10:03:18.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.194
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.331
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:03:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:03:19.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:03:19.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:03:20.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:03:20.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:03:21.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:03:22.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:03:22.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:03:23.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:03:23.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:03:23.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 10:03:23.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.33
2025-07-30 10:03:23.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:03:23.916 | 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

2025-07-30 10:03:23.918 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:03:23.989 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:03:24.065 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch299
2025-07-30 10:03:27.276 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.8, cls_loss: 0.9, lr: 1.997e-07, size: 416, ETA: 0:00:40
2025-07-30 10:03:30.517 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 1.676e-07, size: 320, ETA: 0:00:36
2025-07-30 10:03:33.736 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.158s, 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: 1.382e-07, size: 448, ETA: 0:00:33
2025-07-30 10:03:37.160 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.117e-07, size: 544, ETA: 0:00:29
2025-07-30 10:03:40.496 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.7, lr: 8.802e-08, size: 544, ETA: 0:00:26
2025-07-30 10:03:43.787 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 299/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.8, lr: 6.715e-08, size: 320, ETA: 0:00:23
2025-07-30 10:03:45.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:03:52.065 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:03:52.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:03:53.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3884
2025-07-30 10:03:53.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3174
2025-07-30 10:03:53.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1656
2025-07-30 10:03:53.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2905
2025-07-30 10:03:53.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:03:53.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:03:53.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.317
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.166
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.290
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:03:53.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:03:53.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:03:53.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:03:53.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:03:54.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:03:54.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:03:55.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:03:55.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:03:56.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:03:57.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:03:57.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:03:58.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:03:58.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:03:58.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-07-30 10:03:58.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.29
2025-07-30 10:03:58.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:03:58.768 | 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

2025-07-30 10:03:58.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:03:58.843 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:03:58.919 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch300
2025-07-30 10:04:02.087 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/300, iter: 20/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.7, lr: 4.189e-08, size: 352, ETA: 0:00:18
2025-07-30 10:04:05.550 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/300, iter: 40/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 2.793e-08, size: 480, ETA: 0:00:14
2025-07-30 10:04:08.898 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/300, iter: 60/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.8, lr: 1.679e-08, size: 256, ETA: 0:00:11
2025-07-30 10:04:12.183 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/300, iter: 80/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.163s, 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: 8.466e-09, size: 544, ETA: 0:00:08
2025-07-30 10:04:15.426 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/300, iter: 100/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.8, lr: 2.965e-09, size: 512, ETA: 0:00:04
2025-07-30 10:04:18.779 | INFO     | yolox_microbt.core.trainer:after_iter:257 - epoch: 300/300, iter: 120/129, gpu mem: 1856Mb, mem: 76.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 3.0, cls_loss: 0.5, lr: 2.856e-10, size: 256, ETA: 0:00:01
2025-07-30 10:04:20.113 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:04:26.876 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-07-30 10:04:27.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-07-30 10:04:27.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.3777
2025-07-30 10:04:27.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3843
2025-07-30 10:04:27.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1774
2025-07-30 10:04:27.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3131
2025-07-30 10:04:27.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-07-30 10:04:27.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-07-30 10:04:27.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-07-30 10:04:27.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-07-30 10:04:27.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.177
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.313
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-07-30 10:04:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-07-30 10:04:28.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-07-30 10:04:28.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-07-30 10:04:29.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-07-30 10:04:29.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-07-30 10:04:30.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-07-30 10:04:30.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-07-30 10:04:30.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-07-30 10:04:31.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-07-30 10:04:31.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-07-30 10:04:31.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-07-30 10:04:31.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.31
2025-07-30 10:04:31.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-07-30 10:04:31.821 | 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

2025-07-30 10:04:31.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:04:31.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:388 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_300e_trainset
2025-07-30 10:04:31.971 | INFO     | yolox_microbt.core.trainer:after_train:172 - Training of experiment is done and the best AP is 27.38
