使用mmdetection 中的RegNet bcakbones替换YOLOX中原始的Backbone
将mmdet/models/backbones/regnet.py中相关的代码复制到YOLOX中，并进行适配
注意通道数要适配
in_channels = [64, 160, 384]
，可以通过调试后，先运行到后后端输出结果出，打印出通道数，得到通道后，在写到这个地方。
(yolox) xuefei@f123:/mnt/d/work/study/detect/8$ python -m yolox.tools.train -f exps/kitti_car_detection/yolox_regnet.py -d 0 -b 16 --fp16
2024-02-18 22:17:51 | INFO | yolox.core.trainer:126 - args: Namespace(batch_size=16, cache=False, ckpt=None, devices=0, dist_backend='nccl', dist_url=None, exp_file='exps/kitti_car_detection/yolox_regnet.py', experiment_name='yolox_regnet', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=False, opts=[], resume=False, start_epoch=None)
2024-02-18 22:17:51 | INFO | yolox.core.trainer:127 - exp value:
╒═══════════════════╤═══════════════════════════════════════════════════════════════╕
│ keys │ values │
╞═══════════════════╪═══════════════════════════════════════════════════════════════╡
│ seed │ None │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ output_dir │ './YOLOX_outputs' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ print_interval │ 10 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ eval_interval │ 10 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ num_classes │ 7 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ depth │ 1.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ width │ 0.5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ act │ 'silu' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ data_num_workers │ 16 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ input_size │ (256, 832) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ multiscale_range │ 5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ data_dir │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/img/' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ train_ann │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/train.json' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ val_ann │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/val.json' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ test_ann │ '/mnt/d/BaiduNetdiskDownload/CV/KITTI/KITTI_YOLOX/test.json' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mosaic_prob │ 1.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mixup_prob │ 1.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ hsv_prob │ 1.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ flip_prob │ 0.5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ degrees │ 10.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ translate │ 0.1 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mosaic_scale │ (0.1, 2) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ enable_mixup │ True │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ mixup_scale │ (0.5, 1.5) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ shear │ 2.0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ warmup_epochs │ 5 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ max_epoch │ 300 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ warmup_lr │ 0 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ min_lr_ratio │ 0.05 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ basic_lr_per_img │ 0.00015625 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ scheduler │ 'yoloxwarmcos' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ no_aug_epochs │ 80 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ ema │ True │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ weight_decay │ 0.0005 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ momentum │ 0.9 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ save_history_ckpt │ True │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ exp_name │ 'yolox_regnet' │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ test_size │ (256, 832) │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ test_conf │ 0.01 │
├───────────────────┼───────────────────────────────────────────────────────────────┤
│ nmsthre │ 0.65 │
╘═══════════════════╧═══════════════════════════════════════════════════════════════╛
2024-02-18 22:17:52 | INFO | yolox.core.trainer:133 - Model Summary: Params: 8.90M, Gflops: 10.70
2024-02-18 22:17:54 | INFO | yolox.data.datasets.kitti:64 - loading annotations into memory...
2024-02-18 22:17:55 | INFO | yolox.data.datasets.kitti:64 - Done (t=0.08s)
2024-02-18 22:17:55 | INFO | pycocotools.coco:86 - creating index...
2024-02-18 22:17:55 | INFO | pycocotools.coco:86 - index created!
2024-02-18 22:17:55 | INFO | yolox.core.trainer:151 - init prefetcher, this might take one minute or less...
2024-02-18 22:18:07 | INFO | yolox.data.datasets.kitti:64 - loading annotations into memory...
2024-02-18 22:18:08 | INFO | yolox.data.datasets.kitti:64 - Done (t=0.11s)
2024-02-18 22:18:08 | INFO | pycocotools.coco:86 - creating index...
2024-02-18 22:18:08 | INFO | pycocotools.coco:86 - index created!
2024-02-18 22:18:08 | INFO | yolox.core.trainer:187 - Training start...
2024-02-18 22:18:08 | INFO | yolox.core.trainer:188 -
YOLOX(
(backbone): YOLOPAFPNRegNet(
(backbone): RegNet(
(conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(layer1): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(32, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
)
(layer2): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=4, bias=False)
(bn2): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(1): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=4, bias=False)
(bn2): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
)
(layer3): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(64, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=10, bias=False)
(bn2): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 160, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(1): Bottleneck(
(conv1): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=10, bias=False)
(bn2): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(2): Bottleneck(
(conv1): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=10, bias=False)
(bn2): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(3): Bottleneck(
(conv1): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=10, bias=False)
(bn2): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(4): Bottleneck(
(conv1): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=10, bias=False)
(bn2): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(5): Bottleneck(
(conv1): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=10, bias=False)
(bn2): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(6): Bottleneck(
(conv1): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=10, bias=False)
(bn2): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
)
(layer4): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(160, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(160, 384, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(1): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(2): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(3): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(4): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(5): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(6): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(7): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(8): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(9): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(10): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
(11): Bottleneck(
(conv1): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv2): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)
(bn2): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(conv3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
init_cfg={'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
)
)
init_cfg=[{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
(upsample): Upsample(scale_factor=2.0, mode=nearest)
(lateral_conv0): BaseConv(
(conv): Conv2d(384, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_p4): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(reduce_conv1): BaseConv(
(conv): Conv2d(160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_p3): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(bu_conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_n3): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(128, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(160, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(bu_conv1): BaseConv(
(conv): Conv2d(160, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_n4): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(320, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(320, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(head): YOLOXHeadFixed(
(cls_convs): ModuleList(
(0): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
(reg_convs): ModuleList(
(0): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
(cls_preds): ModuleList(
(0): Conv2d(128, 7, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(128, 7, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(128, 7, kernel_size=(1, 1), stride=(1, 1))
)
(reg_preds): ModuleList(
(0): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
)
(obj_preds): ModuleList(
(0): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
)
(stems): ModuleList(
(0): BaseConv(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(2): BaseConv(
(conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(l1_loss): L1Loss()
(bcewithlog_loss): BCEWithLogitsLoss()
(iou_loss): IOUloss()
)
)
2024-02-18 22:18:08 | INFO | yolox.core.trainer:199 - ---> start train epoch1
2024-02-18 22:18:15 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 10/250, mem: 2790Mb, iter_time: 0.753s, data_time: 0.002s, total_loss: 15.9, iou_loss: 4.3, l1_loss: 2.3, conf_loss: 8.0, cls_loss: 1.2, lr: 1.600e-07, size: 256, ETA: 15:41:22
2024-02-18 22:18:25 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 20/250, mem: 3853Mb, iter_time: 0.915s, data_time: 0.002s, total_loss: 20.4, iou_loss: 4.3, l1_loss: 2.7, conf_loss: 12.2, cls_loss: 1.1, lr: 6.400e-07, size: 384, ETA: 17:22:16
2024-02-18 22:18:35 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 30/250, mem: 3871Mb, iter_time: 1.053s, data_time: 0.002s, total_loss: 21.8, iou_loss: 4.2, l1_loss: 2.4, conf_loss: 14.0, cls_loss: 1.1, lr: 1.440e-06, size: 416, ETA: 18:53:27
2024-02-18 22:18:39 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 40/250, mem: 3871Mb, iter_time: 0.380s, data_time: 0.001s, total_loss: 15.2, iou_loss: 4.4, l1_loss: 2.3, conf_loss: 7.3, cls_loss: 1.2, lr: 2.560e-06, size: 256, ETA: 16:08:35
2024-02-18 22:18:45 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 50/250, mem: 3871Mb, iter_time: 0.576s, data_time: 0.002s, total_loss: 14.7, iou_loss: 4.2, l1_loss: 2.0, conf_loss: 7.3, cls_loss: 1.2, lr: 4.000e-06, size: 224, ETA: 15:18:46
2024-02-18 22:18:52 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 60/250, mem: 3871Mb, iter_time: 0.701s, data_time: 0.002s, total_loss: 20.0, iou_loss: 4.4, l1_loss: 2.8, conf_loss: 11.8, cls_loss: 1.1, lr: 5.760e-06, size: 416, ETA: 15:11:29
2024-02-18 22:18:59 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 70/250, mem: 3871Mb, iter_time: 0.689s, data_time: 0.002s, total_loss: 25.5, iou_loss: 4.3, l1_loss: 2.8, conf_loss: 17.3, cls_loss: 1.1, lr: 7.840e-06, size: 416, ETA: 15:04:08
2024-02-18 22:19:04 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 80/250, mem: 3871Mb, iter_time: 0.532s, data_time: 0.001s, total_loss: 14.0, iou_loss: 4.4, l1_loss: 2.1, conf_loss: 6.4, cls_loss: 1.2, lr: 1.024e-05, size: 192, ETA: 14:34:04
2024-02-18 22:19:12 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 90/250, mem: 3871Mb, iter_time: 0.847s, data_time: 0.002s, total_loss: 19.2, iou_loss: 4.5, l1_loss: 2.5, conf_loss: 11.2, cls_loss: 1.1, lr: 1.296e-05, size: 320, ETA: 14:54:16
2024-02-18 22:19:18 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 100/250, mem: 3871Mb, iter_time: 0.524s, data_time: 0.002s, total_loss: 16.2, iou_loss: 4.1, l1_loss: 2.2, conf_loss: 8.7, cls_loss: 1.3, lr: 1.600e-05, size: 320, ETA: 14:30:13
2024-02-18 22:19:23 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 110/250, mem: 3871Mb, iter_time: 0.519s, data_time: 0.001s, total_loss: 18.0, iou_loss: 4.3, l1_loss: 2.4, conf_loss: 10.2, cls_loss: 1.2, lr: 1.936e-05, size: 320, ETA: 14:09:50
2024-02-18 22:19:32 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 120/250, mem: 3871Mb, iter_time: 0.913s, data_time: 0.002s, total_loss: 21.2, iou_loss: 4.4, l1_loss: 2.7, conf_loss: 13.0, cls_loss: 1.0, lr: 2.304e-05, size: 352, ETA: 14:33:49
2024-02-18 22:19:35 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 130/250, mem: 3871Mb, iter_time: 0.337s, data_time: 0.001s, total_loss: 13.8, iou_loss: 4.2, l1_loss: 2.1, conf_loss: 6.2, cls_loss: 1.3, lr: 2.704e-05, size: 192, ETA: 13:58:51
2024-02-18 22:19:39 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 140/250, mem: 3871Mb, iter_time: 0.395s, data_time: 0.002s, total_loss: 13.4, iou_loss: 4.1, l1_loss: 2.1, conf_loss: 5.8, cls_loss: 1.4, lr: 3.136e-05, size: 160, ETA: 13:34:01
2024-02-18 22:19:45 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 150/250, mem: 3871Mb, iter_time: 0.584s, data_time: 0.175s, total_loss: 15.0, iou_loss: 4.1, l1_loss: 2.1, conf_loss: 7.5, cls_loss: 1.3, lr: 3.600e-05, size: 256, ETA: 13:28:14
2024-02-18 22:19:51 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 160/250, mem: 3871Mb, iter_time: 0.584s, data_time: 0.210s, total_loss: 14.3, iou_loss: 4.1, l1_loss: 2.4, conf_loss: 6.5, cls_loss: 1.3, lr: 4.096e-05, size: 224, ETA: 13:23:07
2024-02-18 22:19:59 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 170/250, mem: 3871Mb, iter_time: 0.765s, data_time: 0.349s, total_loss: 14.7, iou_loss: 4.2, l1_loss: 2.3, conf_loss: 6.8, cls_loss: 1.3, lr: 4.624e-05, size: 256, ETA: 13:31:53
2024-02-18 22:20:07 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 180/250, mem: 3871Mb, iter_time: 0.790s, data_time: 0.045s, total_loss: 14.5, iou_loss: 3.7, l1_loss: 2.2, conf_loss: 7.1, cls_loss: 1.5, lr: 5.184e-05, size: 288, ETA: 13:41:26
2024-02-18 22:20:14 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 190/250, mem: 3871Mb, iter_time: 0.734s, data_time: 0.019s, total_loss: 16.4, iou_loss: 3.8, l1_loss: 2.7, conf_loss: 8.4, cls_loss: 1.5, lr: 5.776e-05, size: 416, ETA: 13:46:15
2024-02-18 22:20:21 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 200/250, mem: 3871Mb, iter_time: 0.658s, data_time: 0.348s, total_loss: 13.1, iou_loss: 3.6, l1_loss: 2.2, conf_loss: 5.7, cls_loss: 1.5, lr: 6.400e-05, size: 192, ETA: 13:45:51
2024-02-18 22:20:28 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 210/250, mem: 3871Mb, iter_time: 0.732s, data_time: 0.136s, total_loss: 15.4, iou_loss: 3.6, l1_loss: 2.6, conf_loss: 7.7, cls_loss: 1.5, lr: 7.056e-05, size: 384, ETA: 13:49:53
2024-02-18 22:20:33 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 220/250, mem: 3871Mb, iter_time: 0.535s, data_time: 0.246s, total_loss: 13.0, iou_loss: 3.8, l1_loss: 2.1, conf_loss: 5.7, cls_loss: 1.5, lr: 7.744e-05, size: 192, ETA: 13:42:23
2024-02-18 22:20:41 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 230/250, mem: 3871Mb, iter_time: 0.789s, data_time: 0.484s, total_loss: 12.2, iou_loss: 3.8, l1_loss: 2.1, conf_loss: 4.9, cls_loss: 1.4, lr: 8.464e-05, size: 160, ETA: 13:49:15
2024-02-18 22:20:46 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 240/250, mem: 3871Mb, iter_time: 0.481s, data_time: 0.085s, total_loss: 13.3, iou_loss: 3.5, l1_loss: 2.3, conf_loss: 5.9, cls_loss: 1.6, lr: 9.216e-05, size: 256, ETA: 13:39:34
2024-02-18 22:20:54 | INFO | yolox.core.trainer:257 - epoch: 1/300, iter: 250/250, mem: 3871Mb, iter_time: 0.793s, data_time: 0.375s, total_loss: 12.8, iou_loss: 3.5, l1_loss: 2.4, conf_loss: 5.5, cls_loss: 1.4, lr: 1.000e-04, size: 256, ETA: 13:46:12
2024-02-18 22:20:54 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:20:55 | INFO | yolox.core.trainer:199 - ---> start train epoch2
2024-02-18 22:21:02 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 10/250, mem: 3871Mb, iter_time: 0.654s, data_time: 0.285s, total_loss: 12.2, iou_loss: 4.0, l1_loss: 2.5, conf_loss: 4.6, cls_loss: 1.2, lr: 1.082e-04, size: 96, ETA: 13:45:39
2024-02-18 22:21:08 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 20/250, mem: 3871Mb, iter_time: 0.663s, data_time: 0.098s, total_loss: 14.2, iou_loss: 3.4, l1_loss: 2.4, conf_loss: 6.9, cls_loss: 1.5, lr: 1.166e-04, size: 320, ETA: 13:45:32
2024-02-18 22:21:17 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 30/250, mem: 3871Mb, iter_time: 0.885s, data_time: 0.272s, total_loss: 13.8, iou_loss: 3.2, l1_loss: 2.5, conf_loss: 6.8, cls_loss: 1.3, lr: 1.254e-04, size: 352, ETA: 13:55:18
2024-02-18 22:21:23 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 40/250, mem: 3871Mb, iter_time: 0.629s, data_time: 0.245s, total_loss: 12.3, iou_loss: 3.3, l1_loss: 2.3, conf_loss: 5.3, cls_loss: 1.4, lr: 1.346e-04, size: 256, ETA: 13:53:24
2024-02-18 22:21:30 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 50/250, mem: 3871Mb, iter_time: 0.627s, data_time: 0.188s, total_loss: 12.2, iou_loss: 3.1, l1_loss: 2.4, conf_loss: 5.3, cls_loss: 1.3, lr: 1.440e-04, size: 288, ETA: 13:51:31
2024-02-18 22:21:39 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 60/250, mem: 3871Mb, iter_time: 0.933s, data_time: 0.552s, total_loss: 11.7, iou_loss: 3.3, l1_loss: 2.1, conf_loss: 4.8, cls_loss: 1.5, lr: 1.538e-04, size: 192, ETA: 14:02:04
2024-02-18 22:21:44 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 70/250, mem: 3871Mb, iter_time: 0.462s, data_time: 0.111s, total_loss: 11.4, iou_loss: 3.3, l1_loss: 2.2, conf_loss: 4.7, cls_loss: 1.3, lr: 1.638e-04, size: 224, ETA: 13:53:36
2024-02-18 22:21:52 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 80/250, mem: 3871Mb, iter_time: 0.811s, data_time: 0.379s, total_loss: 12.1, iou_loss: 3.2, l1_loss: 2.2, conf_loss: 5.4, cls_loss: 1.3, lr: 1.742e-04, size: 256, ETA: 13:58:49
2024-02-18 22:22:00 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 90/250, mem: 3871Mb, iter_time: 0.797s, data_time: 0.211s, total_loss: 12.9, iou_loss: 3.2, l1_loss: 2.5, conf_loss: 5.9, cls_loss: 1.4, lr: 1.850e-04, size: 352, ETA: 14:03:13
2024-02-18 22:22:05 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 100/250, mem: 3871Mb, iter_time: 0.501s, data_time: 0.088s, total_loss: 11.2, iou_loss: 2.9, l1_loss: 2.2, conf_loss: 4.7, cls_loss: 1.4, lr: 1.960e-04, size: 256, ETA: 13:56:49
2024-02-18 22:22:14 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 110/250, mem: 3871Mb, iter_time: 0.917s, data_time: 0.264s, total_loss: 13.5, iou_loss: 2.9, l1_loss: 2.5, conf_loss: 6.5, cls_loss: 1.5, lr: 2.074e-04, size: 384, ETA: 14:05:08
2024-02-18 22:22:22 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 120/250, mem: 3871Mb, iter_time: 0.765s, data_time: 0.001s, total_loss: 12.5, iou_loss: 2.7, l1_loss: 2.4, conf_loss: 5.9, cls_loss: 1.4, lr: 2.190e-04, size: 416, ETA: 14:07:54
2024-02-18 22:22:27 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 130/250, mem: 3871Mb, iter_time: 0.517s, data_time: 0.114s, total_loss: 11.2, iou_loss: 3.3, l1_loss: 1.9, conf_loss: 4.6, cls_loss: 1.3, lr: 2.310e-04, size: 128, ETA: 14:02:24
2024-02-18 22:22:36 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 140/250, mem: 3871Mb, iter_time: 0.876s, data_time: 0.542s, total_loss: 10.8, iou_loss: 3.1, l1_loss: 1.9, conf_loss: 4.4, cls_loss: 1.3, lr: 2.434e-04, size: 160, ETA: 14:08:36
2024-02-18 22:22:41 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 150/250, mem: 3871Mb, iter_time: 0.518s, data_time: 0.126s, total_loss: 10.9, iou_loss: 3.0, l1_loss: 2.2, conf_loss: 4.4, cls_loss: 1.3, lr: 2.560e-04, size: 224, ETA: 14:03:23
2024-02-18 22:22:50 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 160/250, mem: 3871Mb, iter_time: 0.977s, data_time: 0.239s, total_loss: 12.5, iou_loss: 2.9, l1_loss: 2.4, conf_loss: 5.9, cls_loss: 1.3, lr: 2.690e-04, size: 416, ETA: 14:12:20
2024-02-18 22:22:57 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 170/250, mem: 3871Mb, iter_time: 0.608s, data_time: 0.145s, total_loss: 11.6, iou_loss: 2.8, l1_loss: 2.3, conf_loss: 5.2, cls_loss: 1.4, lr: 2.822e-04, size: 288, ETA: 14:09:56
2024-02-18 22:23:04 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 180/250, mem: 3871Mb, iter_time: 0.713s, data_time: 0.133s, total_loss: 11.8, iou_loss: 2.8, l1_loss: 2.5, conf_loss: 5.3, cls_loss: 1.3, lr: 2.958e-04, size: 352, ETA: 14:10:39
2024-02-18 22:23:11 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 190/250, mem: 3871Mb, iter_time: 0.691s, data_time: 0.401s, total_loss: 10.7, iou_loss: 3.0, l1_loss: 1.9, conf_loss: 4.4, cls_loss: 1.3, lr: 3.098e-04, size: 160, ETA: 14:10:43
2024-02-18 22:23:17 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 200/250, mem: 3871Mb, iter_time: 0.610s, data_time: 0.001s, total_loss: 11.8, iou_loss: 2.8, l1_loss: 2.4, conf_loss: 5.3, cls_loss: 1.3, lr: 3.240e-04, size: 384, ETA: 14:08:32
2024-02-18 22:23:23 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 210/250, mem: 3871Mb, iter_time: 0.654s, data_time: 0.248s, total_loss: 10.7, iou_loss: 2.8, l1_loss: 2.1, conf_loss: 4.5, cls_loss: 1.3, lr: 3.386e-04, size: 256, ETA: 14:07:39
2024-02-18 22:23:32 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 220/250, mem: 3871Mb, iter_time: 0.848s, data_time: 0.508s, total_loss: 10.6, iou_loss: 3.2, l1_loss: 2.0, conf_loss: 4.2, cls_loss: 1.3, lr: 3.534e-04, size: 128, ETA: 14:11:55
2024-02-18 22:23:39 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 230/250, mem: 3871Mb, iter_time: 0.738s, data_time: 0.008s, total_loss: 11.9, iou_loss: 2.7, l1_loss: 2.5, conf_loss: 5.5, cls_loss: 1.3, lr: 3.686e-04, size: 416, ETA: 14:13:09
2024-02-18 22:23:46 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 240/250, mem: 3871Mb, iter_time: 0.724s, data_time: 0.009s, total_loss: 11.9, iou_loss: 2.6, l1_loss: 2.3, conf_loss: 5.7, cls_loss: 1.4, lr: 3.842e-04, size: 416, ETA: 14:13:58
2024-02-18 22:23:51 | INFO | yolox.core.trainer:257 - epoch: 2/300, iter: 250/250, mem: 3871Mb, iter_time: 0.462s, data_time: 0.179s, total_loss: 11.3, iou_loss: 3.4, l1_loss: 2.0, conf_loss: 4.5, cls_loss: 1.4, lr: 4.000e-04, size: 128, ETA: 14:08:15
2024-02-18 22:23:51 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:23:52 | INFO | yolox.core.trainer:199 - ---> start train epoch3
2024-02-18 22:23:56 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 10/250, mem: 3871Mb, iter_time: 0.373s, data_time: 0.087s, total_loss: 10.3, iou_loss: 3.2, l1_loss: 2.0, conf_loss: 3.9, cls_loss: 1.2, lr: 4.162e-04, size: 128, ETA: 14:00:35
2024-02-18 22:24:06 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 20/250, mem: 3871Mb, iter_time: 0.981s, data_time: 0.269s, total_loss: 12.2, iou_loss: 2.7, l1_loss: 2.4, conf_loss: 5.9, cls_loss: 1.2, lr: 4.326e-04, size: 416, ETA: 14:07:44
2024-02-18 22:24:12 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 30/250, mem: 3871Mb, iter_time: 0.623s, data_time: 0.001s, total_loss: 11.3, iou_loss: 2.6, l1_loss: 2.1, conf_loss: 5.2, cls_loss: 1.3, lr: 4.494e-04, size: 384, ETA: 14:06:13
2024-02-18 22:24:18 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 40/250, mem: 3871Mb, iter_time: 0.627s, data_time: 0.059s, total_loss: 11.5, iou_loss: 2.8, l1_loss: 2.2, conf_loss: 5.2, cls_loss: 1.2, lr: 4.666e-04, size: 352, ETA: 14:04:50
2024-02-18 22:24:25 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 50/250, mem: 3871Mb, iter_time: 0.676s, data_time: 0.324s, total_loss: 10.7, iou_loss: 2.8, l1_loss: 2.1, conf_loss: 4.6, cls_loss: 1.1, lr: 4.840e-04, size: 224, ETA: 14:04:36
2024-02-18 22:24:30 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 60/250, mem: 3871Mb, iter_time: 0.489s, data_time: 0.147s, total_loss: 9.8, iou_loss: 2.8, l1_loss: 2.0, conf_loss: 4.0, cls_loss: 1.1, lr: 5.018e-04, size: 224, ETA: 14:00:14
2024-02-18 22:24:36 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 70/250, mem: 3871Mb, iter_time: 0.643s, data_time: 0.373s, total_loss: 10.4, iou_loss: 3.3, l1_loss: 1.9, conf_loss: 3.9, cls_loss: 1.3, lr: 5.198e-04, size: 96, ETA: 13:59:23
2024-02-18 22:24:44 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 80/250, mem: 3871Mb, iter_time: 0.745s, data_time: 0.293s, total_loss: 10.7, iou_loss: 2.8, l1_loss: 2.0, conf_loss: 4.6, cls_loss: 1.3, lr: 5.382e-04, size: 288, ETA: 14:00:43
2024-02-18 22:24:51 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 90/250, mem: 3871Mb, iter_time: 0.773s, data_time: 0.054s, total_loss: 11.2, iou_loss: 2.5, l1_loss: 2.3, conf_loss: 5.2, cls_loss: 1.2, lr: 5.570e-04, size: 416, ETA: 14:02:37
2024-02-18 22:24:57 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 100/250, mem: 3871Mb, iter_time: 0.546s, data_time: 0.200s, total_loss: 10.1, iou_loss: 2.8, l1_loss: 2.1, conf_loss: 4.2, cls_loss: 1.1, lr: 5.760e-04, size: 224, ETA: 13:59:45
2024-02-18 22:25:02 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 110/250, mem: 3871Mb, iter_time: 0.494s, data_time: 0.240s, total_loss: 10.0, iou_loss: 3.2, l1_loss: 2.0, conf_loss: 3.8, cls_loss: 1.0, lr: 5.954e-04, size: 96, ETA: 13:55:55
2024-02-18 22:25:08 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 120/250, mem: 3871Mb, iter_time: 0.578s, data_time: 0.302s, total_loss: 9.2, iou_loss: 3.1, l1_loss: 1.8, conf_loss: 3.4, cls_loss: 1.0, lr: 6.150e-04, size: 96, ETA: 13:53:52
2024-02-18 22:25:16 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 130/250, mem: 3871Mb, iter_time: 0.869s, data_time: 0.417s, total_loss: 10.5, iou_loss: 2.7, l1_loss: 2.1, conf_loss: 4.5, cls_loss: 1.2, lr: 6.350e-04, size: 288, ETA: 13:57:37
2024-02-18 22:25:24 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 140/250, mem: 3871Mb, iter_time: 0.725s, data_time: 0.030s, total_loss: 11.2, iou_loss: 2.5, l1_loss: 2.2, conf_loss: 5.3, cls_loss: 1.1, lr: 6.554e-04, size: 416, ETA: 13:58:27
2024-02-18 22:25:30 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 150/250, mem: 3871Mb, iter_time: 0.627s, data_time: 0.031s, total_loss: 10.8, iou_loss: 2.5, l1_loss: 2.1, conf_loss: 4.9, cls_loss: 1.2, lr: 6.760e-04, size: 384, ETA: 13:57:24
2024-02-18 22:25:36 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 160/250, mem: 3871Mb, iter_time: 0.610s, data_time: 0.278s, total_loss: 10.0, iou_loss: 2.6, l1_loss: 2.0, conf_loss: 4.3, cls_loss: 1.1, lr: 6.970e-04, size: 224, ETA: 13:56:03
2024-02-18 22:25:40 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 170/250, mem: 3871Mb, iter_time: 0.397s, data_time: 0.133s, total_loss: 10.2, iou_loss: 3.1, l1_loss: 1.9, conf_loss: 3.9, cls_loss: 1.2, lr: 7.182e-04, size: 128, ETA: 13:50:48
2024-02-18 22:25:49 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 180/250, mem: 3871Mb, iter_time: 0.884s, data_time: 0.443s, total_loss: 10.3, iou_loss: 2.5, l1_loss: 1.9, conf_loss: 4.6, cls_loss: 1.3, lr: 7.398e-04, size: 288, ETA: 13:54:34
2024-02-18 22:25:54 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 190/250, mem: 3871Mb, iter_time: 0.544s, data_time: 0.292s, total_loss: 9.7, iou_loss: 2.9, l1_loss: 1.8, conf_loss: 3.9, cls_loss: 1.1, lr: 7.618e-04, size: 128, ETA: 13:52:07
2024-02-18 22:25:59 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 200/250, mem: 3871Mb, iter_time: 0.527s, data_time: 0.281s, total_loss: 10.1, iou_loss: 3.3, l1_loss: 1.9, conf_loss: 3.9, cls_loss: 1.0, lr: 7.840e-04, size: 96, ETA: 13:49:27
2024-02-18 22:26:07 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 210/250, mem: 3871Mb, iter_time: 0.780s, data_time: 0.450s, total_loss: 10.0, iou_loss: 2.7, l1_loss: 1.9, conf_loss: 4.2, cls_loss: 1.2, lr: 8.066e-04, size: 224, ETA: 13:51:15
2024-02-18 22:26:13 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 220/250, mem: 3871Mb, iter_time: 0.607s, data_time: 0.029s, total_loss: 10.7, iou_loss: 2.6, l1_loss: 2.0, conf_loss: 4.8, cls_loss: 1.3, lr: 8.294e-04, size: 352, ETA: 13:50:02
2024-02-18 22:26:21 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 230/250, mem: 3871Mb, iter_time: 0.735s, data_time: 0.108s, total_loss: 10.8, iou_loss: 2.5, l1_loss: 2.1, conf_loss: 4.9, cls_loss: 1.5, lr: 8.526e-04, size: 384, ETA: 13:51:01
2024-02-18 22:26:27 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 240/250, mem: 3871Mb, iter_time: 0.632s, data_time: 0.236s, total_loss: 9.6, iou_loss: 2.6, l1_loss: 2.0, conf_loss: 4.0, cls_loss: 1.1, lr: 8.762e-04, size: 256, ETA: 13:50:14
2024-02-18 22:26:32 | INFO | yolox.core.trainer:257 - epoch: 3/300, iter: 250/250, mem: 3871Mb, iter_time: 0.467s, data_time: 0.077s, total_loss: 10.1, iou_loss: 2.6, l1_loss: 1.9, conf_loss: 4.4, cls_loss: 1.2, lr: 9.000e-04, size: 256, ETA: 13:46:45
2024-02-18 22:26:32 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:26:33 | INFO | yolox.core.trainer:199 - ---> start train epoch4
2024-02-18 22:26:40 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 10/250, mem: 3871Mb, iter_time: 0.750s, data_time: 0.157s, total_loss: 10.2, iou_loss: 2.4, l1_loss: 2.0, conf_loss: 4.5, cls_loss: 1.3, lr: 9.242e-04, size: 352, ETA: 13:47:59
2024-02-18 22:26:46 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 20/250, mem: 3871Mb, iter_time: 0.600s, data_time: 0.152s, total_loss: 9.8, iou_loss: 2.4, l1_loss: 2.0, conf_loss: 4.1, cls_loss: 1.2, lr: 9.486e-04, size: 288, ETA: 13:46:45
2024-02-18 22:26:51 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 30/250, mem: 3871Mb, iter_time: 0.513s, data_time: 0.209s, total_loss: 10.0, iou_loss: 2.9, l1_loss: 1.9, conf_loss: 3.8, cls_loss: 1.4, lr: 9.734e-04, size: 160, ETA: 13:44:11
2024-02-18 22:26:59 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 40/250, mem: 3871Mb, iter_time: 0.780s, data_time: 0.433s, total_loss: 9.4, iou_loss: 2.6, l1_loss: 1.8, conf_loss: 3.9, cls_loss: 1.1, lr: 9.986e-04, size: 224, ETA: 13:45:51
2024-02-18 22:27:04 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 50/250, mem: 3871Mb, iter_time: 0.516s, data_time: 0.191s, total_loss: 10.0, iou_loss: 2.8, l1_loss: 2.0, conf_loss: 4.0, cls_loss: 1.2, lr: 1.024e-03, size: 192, ETA: 13:43:24
2024-02-18 22:27:11 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 60/250, mem: 3871Mb, iter_time: 0.675s, data_time: 0.282s, total_loss: 9.8, iou_loss: 2.5, l1_loss: 1.9, conf_loss: 4.2, cls_loss: 1.2, lr: 1.050e-03, size: 256, ETA: 13:43:25
2024-02-18 22:27:18 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 70/250, mem: 3871Mb, iter_time: 0.731s, data_time: 0.216s, total_loss: 10.9, iou_loss: 2.6, l1_loss: 2.0, conf_loss: 4.9, cls_loss: 1.3, lr: 1.076e-03, size: 320, ETA: 13:44:17
2024-02-18 22:27:24 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 80/250, mem: 3871Mb, iter_time: 0.610s, data_time: 0.028s, total_loss: 9.9, iou_loss: 2.4, l1_loss: 1.8, conf_loss: 4.4, cls_loss: 1.3, lr: 1.102e-03, size: 352, ETA: 13:43:20
2024-02-18 22:27:31 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 90/250, mem: 3871Mb, iter_time: 0.649s, data_time: 0.302s, total_loss: 9.4, iou_loss: 2.5, l1_loss: 1.7, conf_loss: 3.8, cls_loss: 1.3, lr: 1.129e-03, size: 224, ETA: 13:42:58
2024-02-18 22:27:36 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 100/250, mem: 3871Mb, iter_time: 0.544s, data_time: 0.230s, total_loss: 8.9, iou_loss: 2.6, l1_loss: 1.7, conf_loss: 3.4, cls_loss: 1.1, lr: 1.156e-03, size: 192, ETA: 13:41:06
2024-02-18 22:27:43 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 110/250, mem: 3871Mb, iter_time: 0.660s, data_time: 0.203s, total_loss: 9.8, iou_loss: 2.5, l1_loss: 1.9, conf_loss: 4.1, cls_loss: 1.2, lr: 1.183e-03, size: 288, ETA: 13:40:55
2024-02-18 22:27:50 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 120/250, mem: 3871Mb, iter_time: 0.674s, data_time: 0.357s, total_loss: 9.5, iou_loss: 2.8, l1_loss: 1.8, conf_loss: 3.7, cls_loss: 1.2, lr: 1.211e-03, size: 192, ETA: 13:40:57
2024-02-18 22:27:56 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 130/250, mem: 3871Mb, iter_time: 0.620s, data_time: 0.265s, total_loss: 8.9, iou_loss: 2.7, l1_loss: 1.7, conf_loss: 3.4, cls_loss: 1.0, lr: 1.239e-03, size: 160, ETA: 13:40:13
2024-02-18 22:28:04 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 140/250, mem: 3871Mb, iter_time: 0.814s, data_time: 0.255s, total_loss: 9.4, iou_loss: 2.4, l1_loss: 1.7, conf_loss: 4.2, cls_loss: 1.1, lr: 1.267e-03, size: 320, ETA: 13:42:11
2024-02-18 22:28:11 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 150/250, mem: 3871Mb, iter_time: 0.730s, data_time: 0.406s, total_loss: 9.4, iou_loss: 3.0, l1_loss: 1.6, conf_loss: 3.7, cls_loss: 1.1, lr: 1.296e-03, size: 96, ETA: 13:42:57
2024-02-18 22:28:18 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 160/250, mem: 3871Mb, iter_time: 0.672s, data_time: 0.158s, total_loss: 10.2, iou_loss: 2.6, l1_loss: 2.0, conf_loss: 4.5, cls_loss: 1.2, lr: 1.325e-03, size: 288, ETA: 13:42:55
2024-02-18 22:28:27 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 170/250, mem: 3871Mb, iter_time: 0.850s, data_time: 0.508s, total_loss: 8.7, iou_loss: 2.6, l1_loss: 1.7, conf_loss: 3.4, cls_loss: 1.0, lr: 1.354e-03, size: 160, ETA: 13:45:15
2024-02-18 22:28:34 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 180/250, mem: 3871Mb, iter_time: 0.772s, data_time: 0.002s, total_loss: 10.1, iou_loss: 2.4, l1_loss: 1.9, conf_loss: 4.7, cls_loss: 1.2, lr: 1.384e-03, size: 416, ETA: 13:46:31
2024-02-18 22:28:42 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 190/250, mem: 3871Mb, iter_time: 0.788s, data_time: 0.119s, total_loss: 11.0, iou_loss: 2.5, l1_loss: 2.0, conf_loss: 5.4, cls_loss: 1.2, lr: 1.414e-03, size: 384, ETA: 13:47:58
2024-02-18 22:28:49 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 200/250, mem: 3871Mb, iter_time: 0.714s, data_time: 0.160s, total_loss: 9.8, iou_loss: 2.5, l1_loss: 1.8, conf_loss: 4.2, cls_loss: 1.3, lr: 1.444e-03, size: 320, ETA: 13:48:25
2024-02-18 22:28:56 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 210/250, mem: 3871Mb, iter_time: 0.671s, data_time: 0.121s, total_loss: 9.3, iou_loss: 2.3, l1_loss: 1.8, conf_loss: 4.1, cls_loss: 1.1, lr: 1.475e-03, size: 320, ETA: 13:48:18
2024-02-18 22:29:03 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 220/250, mem: 3871Mb, iter_time: 0.722s, data_time: 0.362s, total_loss: 8.8, iou_loss: 2.7, l1_loss: 1.7, conf_loss: 3.3, cls_loss: 1.1, lr: 1.505e-03, size: 160, ETA: 13:48:50
2024-02-18 22:29:11 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 230/250, mem: 3871Mb, iter_time: 0.756s, data_time: 0.362s, total_loss: 9.4, iou_loss: 2.4, l1_loss: 1.7, conf_loss: 3.9, cls_loss: 1.3, lr: 1.537e-03, size: 224, ETA: 13:49:47
2024-02-18 22:29:16 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 240/250, mem: 3871Mb, iter_time: 0.528s, data_time: 0.180s, total_loss: 10.5, iou_loss: 2.8, l1_loss: 2.0, conf_loss: 4.5, cls_loss: 1.3, lr: 1.568e-03, size: 192, ETA: 13:47:52
2024-02-18 22:29:25 | INFO | yolox.core.trainer:257 - epoch: 4/300, iter: 250/250, mem: 3871Mb, iter_time: 0.873s, data_time: 0.450s, total_loss: 9.1, iou_loss: 2.4, l1_loss: 1.8, conf_loss: 3.8, cls_loss: 1.2, lr: 1.600e-03, size: 256, ETA: 13:50:15
2024-02-18 22:29:25 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:29:26 | INFO | yolox.core.trainer:199 - ---> start train epoch5
2024-02-18 22:29:32 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 10/250, mem: 3871Mb, iter_time: 0.572s, data_time: 0.010s, total_loss: 9.3, iou_loss: 2.3, l1_loss: 1.8, conf_loss: 4.1, cls_loss: 1.1, lr: 1.632e-03, size: 352, ETA: 13:48:53
2024-02-18 22:29:37 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 20/250, mem: 3871Mb, iter_time: 0.533s, data_time: 0.236s, total_loss: 9.4, iou_loss: 2.7, l1_loss: 1.8, conf_loss: 3.8, cls_loss: 1.1, lr: 1.665e-03, size: 192, ETA: 13:47:05
2024-02-18 22:29:45 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 30/250, mem: 3871Mb, iter_time: 0.762s, data_time: 0.362s, total_loss: 9.3, iou_loss: 2.5, l1_loss: 1.8, conf_loss: 3.8, cls_loss: 1.2, lr: 1.697e-03, size: 256, ETA: 13:48:04
2024-02-18 22:29:51 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 40/250, mem: 3871Mb, iter_time: 0.600s, data_time: 0.270s, total_loss: 8.6, iou_loss: 2.4, l1_loss: 1.6, conf_loss: 3.4, cls_loss: 1.2, lr: 1.731e-03, size: 192, ETA: 13:47:06
2024-02-18 22:29:58 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 50/250, mem: 3871Mb, iter_time: 0.748s, data_time: 0.214s, total_loss: 9.1, iou_loss: 2.3, l1_loss: 1.7, conf_loss: 4.1, cls_loss: 1.1, lr: 1.764e-03, size: 320, ETA: 13:47:54
2024-02-18 22:30:05 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 60/250, mem: 3871Mb, iter_time: 0.659s, data_time: 0.131s, total_loss: 9.2, iou_loss: 2.4, l1_loss: 1.8, conf_loss: 3.9, cls_loss: 1.1, lr: 1.798e-03, size: 320, ETA: 13:47:38
2024-02-18 22:30:13 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 70/250, mem: 3871Mb, iter_time: 0.779s, data_time: 0.144s, total_loss: 9.2, iou_loss: 2.2, l1_loss: 1.8, conf_loss: 4.1, cls_loss: 1.2, lr: 1.832e-03, size: 384, ETA: 13:48:46
2024-02-18 22:30:20 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 80/250, mem: 3871Mb, iter_time: 0.715s, data_time: 0.300s, total_loss: 8.8, iou_loss: 2.4, l1_loss: 1.6, conf_loss: 3.8, cls_loss: 1.1, lr: 1.866e-03, size: 256, ETA: 13:49:09
2024-02-18 22:30:26 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 90/250, mem: 3871Mb, iter_time: 0.601s, data_time: 0.307s, total_loss: 9.2, iou_loss: 3.0, l1_loss: 1.7, conf_loss: 3.4, cls_loss: 1.0, lr: 1.901e-03, size: 96, ETA: 13:48:13
2024-02-18 22:30:34 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 100/250, mem: 3871Mb, iter_time: 0.812s, data_time: 0.268s, total_loss: 9.7, iou_loss: 2.5, l1_loss: 1.7, conf_loss: 4.2, cls_loss: 1.2, lr: 1.936e-03, size: 288, ETA: 13:49:40
2024-02-18 22:30:40 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 110/250, mem: 3871Mb, iter_time: 0.636s, data_time: 0.106s, total_loss: 8.7, iou_loss: 2.2, l1_loss: 1.6, conf_loss: 3.9, cls_loss: 1.1, lr: 1.971e-03, size: 320, ETA: 13:49:08
2024-02-18 22:30:47 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 120/250, mem: 3871Mb, iter_time: 0.703s, data_time: 0.380s, total_loss: 8.6, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 3.3, cls_loss: 1.1, lr: 2.007e-03, size: 128, ETA: 13:49:21
2024-02-18 22:30:55 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 130/250, mem: 3871Mb, iter_time: 0.801s, data_time: 0.358s, total_loss: 8.9, iou_loss: 2.3, l1_loss: 1.8, conf_loss: 3.8, cls_loss: 1.0, lr: 2.043e-03, size: 256, ETA: 13:50:37
2024-02-18 22:31:02 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 140/250, mem: 3871Mb, iter_time: 0.640s, data_time: 0.049s, total_loss: 9.4, iou_loss: 2.2, l1_loss: 1.7, conf_loss: 4.5, cls_loss: 1.1, lr: 2.079e-03, size: 352, ETA: 13:50:08
2024-02-18 22:31:09 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 150/250, mem: 3871Mb, iter_time: 0.699s, data_time: 0.213s, total_loss: 9.1, iou_loss: 2.4, l1_loss: 1.6, conf_loss: 3.9, cls_loss: 1.3, lr: 2.116e-03, size: 288, ETA: 13:50:17
2024-02-18 22:31:17 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 160/250, mem: 3871Mb, iter_time: 0.813s, data_time: 0.327s, total_loss: 8.6, iou_loss: 2.4, l1_loss: 1.6, conf_loss: 3.6, cls_loss: 1.0, lr: 2.153e-03, size: 288, ETA: 13:51:39
2024-02-18 22:31:23 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 170/250, mem: 3871Mb, iter_time: 0.604s, data_time: 0.129s, total_loss: 9.2, iou_loss: 2.4, l1_loss: 1.7, conf_loss: 3.9, cls_loss: 1.3, lr: 2.190e-03, size: 288, ETA: 13:50:47
2024-02-18 22:31:30 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 180/250, mem: 3871Mb, iter_time: 0.739s, data_time: 0.327s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 1.1, lr: 2.228e-03, size: 192, ETA: 13:51:20
2024-02-18 22:31:37 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 190/250, mem: 3871Mb, iter_time: 0.708s, data_time: 0.343s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 1.1, lr: 2.266e-03, size: 160, ETA: 13:51:33
2024-02-18 22:31:48 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 200/250, mem: 3871Mb, iter_time: 1.073s, data_time: 0.322s, total_loss: 9.3, iou_loss: 2.3, l1_loss: 1.6, conf_loss: 4.4, cls_loss: 1.0, lr: 2.304e-03, size: 416, ETA: 13:55:30
2024-02-18 22:31:54 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 210/250, mem: 3871Mb, iter_time: 0.635s, data_time: 0.049s, total_loss: 8.8, iou_loss: 2.2, l1_loss: 1.7, conf_loss: 3.8, cls_loss: 1.2, lr: 2.343e-03, size: 352, ETA: 13:54:57
2024-02-18 22:32:00 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 220/250, mem: 3871Mb, iter_time: 0.591s, data_time: 0.002s, total_loss: 9.2, iou_loss: 2.4, l1_loss: 1.6, conf_loss: 4.0, cls_loss: 1.2, lr: 2.381e-03, size: 352, ETA: 13:53:57
2024-02-18 22:32:08 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 230/250, mem: 3871Mb, iter_time: 0.763s, data_time: 0.132s, total_loss: 9.1, iou_loss: 2.3, l1_loss: 1.7, conf_loss: 3.9, cls_loss: 1.2, lr: 2.421e-03, size: 384, ETA: 13:54:41
2024-02-18 22:32:17 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 240/250, mem: 3871Mb, iter_time: 0.863s, data_time: 0.220s, total_loss: 9.2, iou_loss: 2.2, l1_loss: 1.6, conf_loss: 4.2, cls_loss: 1.2, lr: 2.460e-03, size: 352, ETA: 13:56:23
2024-02-18 22:32:22 | INFO | yolox.core.trainer:257 - epoch: 5/300, iter: 250/250, mem: 3871Mb, iter_time: 0.538s, data_time: 0.220s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.3, cls_loss: 1.1, lr: 2.500e-03, size: 128, ETA: 13:54:53
2024-02-18 22:32:22 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:32:23 | INFO | yolox.core.trainer:199 - ---> start train epoch6
2024-02-18 22:32:29 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 10/250, mem: 3871Mb, iter_time: 0.594s, data_time: 0.185s, total_loss: 8.9, iou_loss: 2.6, l1_loss: 1.6, conf_loss: 3.6, cls_loss: 1.2, lr: 2.500e-03, size: 224, ETA: 13:53:56
2024-02-18 22:32:35 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 20/250, mem: 3871Mb, iter_time: 0.615s, data_time: 0.195s, total_loss: 8.9, iou_loss: 2.4, l1_loss: 1.7, conf_loss: 3.7, cls_loss: 1.1, lr: 2.500e-03, size: 256, ETA: 13:53:12
2024-02-18 22:32:43 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 30/250, mem: 3871Mb, iter_time: 0.797s, data_time: 0.404s, total_loss: 8.4, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 3.6, cls_loss: 1.0, lr: 2.500e-03, size: 224, ETA: 13:54:14
2024-02-18 22:32:52 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 40/250, mem: 3871Mb, iter_time: 0.857s, data_time: 0.243s, total_loss: 8.5, iou_loss: 2.1, l1_loss: 1.6, conf_loss: 3.7, cls_loss: 1.1, lr: 2.500e-03, size: 352, ETA: 13:55:49
2024-02-18 22:32:57 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 50/250, mem: 3871Mb, iter_time: 0.518s, data_time: 0.100s, total_loss: 8.9, iou_loss: 2.4, l1_loss: 1.6, conf_loss: 3.7, cls_loss: 1.2, lr: 2.500e-03, size: 256, ETA: 13:54:09
2024-02-18 22:33:03 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 60/250, mem: 3871Mb, iter_time: 0.620s, data_time: 0.303s, total_loss: 8.1, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.0, lr: 2.500e-03, size: 128, ETA: 13:53:30
2024-02-18 22:33:13 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 70/250, mem: 3871Mb, iter_time: 0.993s, data_time: 0.257s, total_loss: 9.1, iou_loss: 2.2, l1_loss: 1.6, conf_loss: 4.2, cls_loss: 1.2, lr: 2.500e-03, size: 416, ETA: 13:56:18
2024-02-18 22:33:18 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 80/250, mem: 3871Mb, iter_time: 0.520s, data_time: 0.117s, total_loss: 8.3, iou_loss: 2.3, l1_loss: 1.5, conf_loss: 3.4, cls_loss: 1.2, lr: 2.500e-03, size: 256, ETA: 13:54:42
2024-02-18 22:33:25 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 90/250, mem: 3871Mb, iter_time: 0.690s, data_time: 0.213s, total_loss: 9.1, iou_loss: 2.3, l1_loss: 1.6, conf_loss: 4.1, cls_loss: 1.1, lr: 2.500e-03, size: 288, ETA: 13:54:41
2024-02-18 22:33:30 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 100/250, mem: 3871Mb, iter_time: 0.458s, data_time: 0.173s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.6, conf_loss: 3.0, cls_loss: 1.0, lr: 2.500e-03, size: 128, ETA: 13:52:33
2024-02-18 22:33:38 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 110/250, mem: 3871Mb, iter_time: 0.818s, data_time: 0.352s, total_loss: 8.7, iou_loss: 2.3, l1_loss: 1.5, conf_loss: 3.8, cls_loss: 1.1, lr: 2.500e-03, size: 288, ETA: 13:53:42
2024-02-18 22:33:45 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 120/250, mem: 3871Mb, iter_time: 0.668s, data_time: 0.372s, total_loss: 8.1, iou_loss: 2.5, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 1.0, lr: 2.500e-03, size: 128, ETA: 13:53:29
2024-02-18 22:33:51 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 130/250, mem: 3871Mb, iter_time: 0.656s, data_time: 0.152s, total_loss: 9.3, iou_loss: 2.4, l1_loss: 1.6, conf_loss: 4.1, cls_loss: 1.2, lr: 2.500e-03, size: 320, ETA: 13:53:10
2024-02-18 22:33:58 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 140/250, mem: 3871Mb, iter_time: 0.673s, data_time: 0.165s, total_loss: 8.3, iou_loss: 2.2, l1_loss: 1.5, conf_loss: 3.5, cls_loss: 1.1, lr: 2.500e-03, size: 320, ETA: 13:53:00
2024-02-18 22:34:06 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 150/250, mem: 3871Mb, iter_time: 0.802s, data_time: 0.184s, total_loss: 8.5, iou_loss: 2.1, l1_loss: 1.5, conf_loss: 3.8, cls_loss: 1.2, lr: 2.500e-03, size: 384, ETA: 13:53:57
2024-02-18 22:34:11 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 160/250, mem: 3871Mb, iter_time: 0.551s, data_time: 0.164s, total_loss: 8.6, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 3.6, cls_loss: 1.1, lr: 2.500e-03, size: 256, ETA: 13:52:43
2024-02-18 22:34:19 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 170/250, mem: 3871Mb, iter_time: 0.740s, data_time: 0.141s, total_loss: 8.0, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 1.1, lr: 2.500e-03, size: 384, ETA: 13:53:08
2024-02-18 22:34:25 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 180/250, mem: 3871Mb, iter_time: 0.574s, data_time: 0.117s, total_loss: 7.7, iou_loss: 2.1, l1_loss: 1.4, conf_loss: 3.2, cls_loss: 1.0, lr: 2.500e-03, size: 288, ETA: 13:52:08
2024-02-18 22:34:31 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 190/250, mem: 3871Mb, iter_time: 0.636s, data_time: 0.344s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.2, cls_loss: 1.2, lr: 2.500e-03, size: 128, ETA: 13:51:39
2024-02-18 22:34:39 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 200/250, mem: 3871Mb, iter_time: 0.808s, data_time: 0.440s, total_loss: 8.3, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 3.3, cls_loss: 1.0, lr: 2.500e-03, size: 224, ETA: 13:52:38
2024-02-18 22:34:46 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 210/250, mem: 3871Mb, iter_time: 0.660s, data_time: 0.028s, total_loss: 8.6, iou_loss: 2.0, l1_loss: 1.5, conf_loss: 4.0, cls_loss: 1.1, lr: 2.500e-03, size: 384, ETA: 13:52:22
2024-02-18 22:34:55 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 220/250, mem: 3871Mb, iter_time: 0.886s, data_time: 0.140s, total_loss: 7.8, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.4, cls_loss: 1.0, lr: 2.500e-03, size: 416, ETA: 13:53:58
2024-02-18 22:35:01 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 230/250, mem: 3871Mb, iter_time: 0.624s, data_time: 0.313s, total_loss: 9.3, iou_loss: 3.0, l1_loss: 1.7, conf_loss: 3.6, cls_loss: 1.0, lr: 2.500e-03, size: 96, ETA: 13:53:23
2024-02-18 22:35:07 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 240/250, mem: 3871Mb, iter_time: 0.617s, data_time: 0.222s, total_loss: 8.3, iou_loss: 2.3, l1_loss: 1.5, conf_loss: 3.4, cls_loss: 1.1, lr: 2.500e-03, size: 256, ETA: 13:52:45
2024-02-18 22:35:13 | INFO | yolox.core.trainer:257 - epoch: 6/300, iter: 250/250, mem: 3871Mb, iter_time: 0.559s, data_time: 0.225s, total_loss: 7.9, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 1.0, lr: 2.500e-03, size: 192, ETA: 13:51:39
2024-02-18 22:35:13 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:35:14 | INFO | yolox.core.trainer:199 - ---> start train epoch7
2024-02-18 22:35:19 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 10/250, mem: 3871Mb, iter_time: 0.591s, data_time: 0.184s, total_loss: 7.9, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.2, lr: 2.500e-03, size: 256, ETA: 13:50:50
2024-02-18 22:35:26 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 20/250, mem: 3871Mb, iter_time: 0.699s, data_time: 0.358s, total_loss: 8.0, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 1.1, lr: 2.500e-03, size: 192, ETA: 13:50:53
2024-02-18 22:35:35 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 30/250, mem: 3871Mb, iter_time: 0.846s, data_time: 0.124s, total_loss: 8.7, iou_loss: 2.1, l1_loss: 1.6, conf_loss: 3.9, cls_loss: 1.1, lr: 2.500e-03, size: 416, ETA: 13:52:07
2024-02-18 22:35:40 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 40/250, mem: 3871Mb, iter_time: 0.524s, data_time: 0.172s, total_loss: 7.9, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 1.2, lr: 2.500e-03, size: 224, ETA: 13:50:45
2024-02-18 22:35:48 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 50/250, mem: 3871Mb, iter_time: 0.757s, data_time: 0.138s, total_loss: 8.6, iou_loss: 2.2, l1_loss: 1.6, conf_loss: 3.7, cls_loss: 1.1, lr: 2.500e-03, size: 384, ETA: 13:51:16
2024-02-18 22:35:54 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 60/250, mem: 3871Mb, iter_time: 0.585s, data_time: 0.291s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 1.0, lr: 2.500e-03, size: 128, ETA: 13:50:25
2024-02-18 22:36:00 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 70/250, mem: 3871Mb, iter_time: 0.610s, data_time: 0.291s, total_loss: 8.3, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 1.1, lr: 2.500e-03, size: 192, ETA: 13:49:46
2024-02-18 22:36:04 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 80/250, mem: 3871Mb, iter_time: 0.472s, data_time: 0.197s, total_loss: 7.6, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 1.0, lr: 2.500e-03, size: 128, ETA: 13:48:03
2024-02-18 22:36:12 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 90/250, mem: 3871Mb, iter_time: 0.743s, data_time: 0.408s, total_loss: 7.6, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 1.0, lr: 2.500e-03, size: 160, ETA: 13:48:27
2024-02-18 22:36:19 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 100/250, mem: 3871Mb, iter_time: 0.748s, data_time: 0.402s, total_loss: 8.4, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 3.2, cls_loss: 1.1, lr: 2.500e-03, size: 160, ETA: 13:48:53
2024-02-18 22:36:26 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 110/250, mem: 3871Mb, iter_time: 0.654s, data_time: 0.296s, total_loss: 7.9, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.1, lr: 2.500e-03, size: 224, ETA: 13:48:35
2024-02-18 22:36:33 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 120/250, mem: 3871Mb, iter_time: 0.726s, data_time: 0.216s, total_loss: inf, iou_loss: 2.3, l1_loss: inf, conf_loss: 3.6, cls_loss: 1.1, lr: 2.500e-03, size: 320, ETA: 13:48:51
2024-02-18 22:36:37 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 130/250, mem: 3871Mb, iter_time: 0.394s, data_time: 0.103s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.0, lr: 2.500e-03, size: 160, ETA: 13:46:36
2024-02-18 22:36:46 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 140/250, mem: 3871Mb, iter_time: 0.915s, data_time: 0.333s, total_loss: 8.2, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 1.1, lr: 2.500e-03, size: 352, ETA: 13:48:16
2024-02-18 22:36:53 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 150/250, mem: 3871Mb, iter_time: 0.640s, data_time: 0.253s, total_loss: 7.8, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.1, lr: 2.500e-03, size: 256, ETA: 13:47:53
2024-02-18 22:36:58 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 160/250, mem: 3871Mb, iter_time: 0.583s, data_time: 0.113s, total_loss: 7.3, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.1, lr: 2.500e-03, size: 288, ETA: 13:47:04
2024-02-18 22:37:06 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 170/250, mem: 3871Mb, iter_time: 0.766s, data_time: 0.157s, total_loss: 7.4, iou_loss: 1.9, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.0, lr: 2.500e-03, size: 384, ETA: 13:47:37
2024-02-18 22:37:11 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 180/250, mem: 3871Mb, iter_time: 0.515s, data_time: 0.252s, total_loss: 8.8, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 3.5, cls_loss: 1.0, lr: 2.500e-03, size: 96, ETA: 13:46:19
2024-02-18 22:37:18 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 190/250, mem: 3871Mb, iter_time: 0.657s, data_time: 0.359s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 1.1, lr: 2.500e-03, size: 160, ETA: 13:46:04
2024-02-18 22:37:25 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 200/250, mem: 3871Mb, iter_time: 0.739s, data_time: 0.232s, total_loss: 8.4, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.7, cls_loss: 1.1, lr: 2.500e-03, size: 320, ETA: 13:46:24
2024-02-18 22:37:31 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 210/250, mem: 3871Mb, iter_time: 0.551s, data_time: 0.102s, total_loss: 7.5, iou_loss: 2.1, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 1.1, lr: 2.500e-03, size: 288, ETA: 13:45:24
2024-02-18 22:37:39 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 220/250, mem: 3871Mb, iter_time: 0.832s, data_time: 0.218s, total_loss: 8.2, iou_loss: 2.1, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 1.1, lr: 2.500e-03, size: 384, ETA: 13:46:24
2024-02-18 22:37:45 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 230/250, mem: 3871Mb, iter_time: 0.627s, data_time: 0.172s, total_loss: 8.0, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 1.1, lr: 2.500e-03, size: 288, ETA: 13:45:56
2024-02-18 22:37:50 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 240/250, mem: 3871Mb, iter_time: 0.497s, data_time: 0.196s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 1.1, lr: 2.500e-03, size: 128, ETA: 13:44:34
2024-02-18 22:37:58 | INFO | yolox.core.trainer:257 - epoch: 7/300, iter: 250/250, mem: 3871Mb, iter_time: 0.725s, data_time: 0.311s, total_loss: 7.5, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 1.0, lr: 2.499e-03, size: 256, ETA: 13:44:48
2024-02-18 22:37:58 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:37:59 | INFO | yolox.core.trainer:199 - ---> start train epoch8
2024-02-18 22:38:04 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 10/250, mem: 3871Mb, iter_time: 0.563s, data_time: 0.228s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 1.0, lr: 2.499e-03, size: 192, ETA: 13:43:54
2024-02-18 22:38:12 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 20/250, mem: 3871Mb, iter_time: 0.814s, data_time: 0.092s, total_loss: 8.4, iou_loss: 2.2, l1_loss: 1.5, conf_loss: 3.7, cls_loss: 1.0, lr: 2.499e-03, size: 416, ETA: 13:44:45
2024-02-18 22:38:18 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 30/250, mem: 3871Mb, iter_time: 0.568s, data_time: 0.102s, total_loss: 8.2, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 1.1, lr: 2.499e-03, size: 288, ETA: 13:43:54
2024-02-18 22:38:23 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 40/250, mem: 3871Mb, iter_time: 0.476s, data_time: 0.151s, total_loss: 8.7, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 3.4, cls_loss: 1.1, lr: 2.499e-03, size: 192, ETA: 13:42:26
2024-02-18 22:38:32 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 50/250, mem: 3871Mb, iter_time: 0.875s, data_time: 0.350s, total_loss: 7.5, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 1.1, lr: 2.499e-03, size: 320, ETA: 13:43:41
2024-02-18 22:38:38 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 60/250, mem: 3871Mb, iter_time: 0.653s, data_time: 0.075s, total_loss: 7.3, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 1.0, lr: 2.499e-03, size: 352, ETA: 13:43:25
2024-02-18 22:38:43 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 70/250, mem: 3871Mb, iter_time: 0.486s, data_time: 0.200s, total_loss: 8.7, iou_loss: 2.8, l1_loss: 1.6, conf_loss: 3.3, cls_loss: 1.0, lr: 2.499e-03, size: 128, ETA: 13:42:02
2024-02-18 22:38:51 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 80/250, mem: 3871Mb, iter_time: 0.829s, data_time: 0.243s, total_loss: 7.7, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.2, cls_loss: 1.0, lr: 2.499e-03, size: 352, ETA: 13:42:57
2024-02-18 22:38:57 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 90/250, mem: 3871Mb, iter_time: 0.572s, data_time: 0.289s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.0, lr: 2.499e-03, size: 96, ETA: 13:42:10
2024-02-18 22:39:04 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 100/250, mem: 3871Mb, iter_time: 0.701s, data_time: 0.245s, total_loss: 7.7, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 1.1, lr: 2.499e-03, size: 288, ETA: 13:42:14
2024-02-18 22:39:12 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 110/250, mem: 3871Mb, iter_time: 0.756s, data_time: 0.126s, total_loss: 7.3, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.9, lr: 2.499e-03, size: 384, ETA: 13:42:39
2024-02-18 22:39:16 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 120/250, mem: 3871Mb, iter_time: 0.390s, data_time: 0.115s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 2.9, cls_loss: 1.0, lr: 2.499e-03, size: 96, ETA: 13:40:41
2024-02-18 22:39:25 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 130/250, mem: 3871Mb, iter_time: 0.915s, data_time: 0.393s, total_loss: 8.1, iou_loss: 2.1, l1_loss: 1.6, conf_loss: 3.3, cls_loss: 1.1, lr: 2.499e-03, size: 320, ETA: 13:42:08
2024-02-18 22:39:30 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 140/250, mem: 3871Mb, iter_time: 0.553s, data_time: 0.290s, total_loss: 8.9, iou_loss: 3.2, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 1.1, lr: 2.499e-03, size: 96, ETA: 13:41:14
2024-02-18 22:39:36 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 150/250, mem: 3871Mb, iter_time: 0.620s, data_time: 0.153s, total_loss: 7.9, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 3.2, cls_loss: 1.1, lr: 2.499e-03, size: 288, ETA: 13:40:47
2024-02-18 22:39:42 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 160/250, mem: 3871Mb, iter_time: 0.574s, data_time: 0.255s, total_loss: 7.4, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 1.0, lr: 2.499e-03, size: 192, ETA: 13:40:02
2024-02-18 22:39:51 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 170/250, mem: 3871Mb, iter_time: 0.917s, data_time: 0.191s, total_loss: 8.1, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.7, cls_loss: 1.0, lr: 2.499e-03, size: 416, ETA: 13:41:28
2024-02-18 22:39:59 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 180/250, mem: 3871Mb, iter_time: 0.727s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 1.0, lr: 2.499e-03, size: 416, ETA: 13:41:41
2024-02-18 22:40:03 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 190/250, mem: 3871Mb, iter_time: 0.444s, data_time: 0.160s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 1.1, lr: 2.499e-03, size: 128, ETA: 13:40:07
2024-02-18 22:40:10 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 200/250, mem: 3871Mb, iter_time: 0.645s, data_time: 0.082s, total_loss: 8.2, iou_loss: 2.2, l1_loss: 1.5, conf_loss: 3.4, cls_loss: 1.1, lr: 2.499e-03, size: 352, ETA: 13:39:50
2024-02-18 22:40:16 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 210/250, mem: 3871Mb, iter_time: 0.677s, data_time: 0.316s, total_loss: 8.3, iou_loss: 2.3, l1_loss: 1.5, conf_loss: 3.4, cls_loss: 1.2, lr: 2.499e-03, size: 224, ETA: 13:39:45
2024-02-18 22:40:24 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 220/250, mem: 3871Mb, iter_time: 0.744s, data_time: 0.237s, total_loss: 7.2, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 1.1, lr: 2.499e-03, size: 320, ETA: 13:40:04
2024-02-18 22:40:30 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 230/250, mem: 3871Mb, iter_time: 0.590s, data_time: 0.030s, total_loss: 7.4, iou_loss: 1.9, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 1.1, lr: 2.499e-03, size: 352, ETA: 13:39:27
2024-02-18 22:40:35 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 240/250, mem: 3871Mb, iter_time: 0.557s, data_time: 0.262s, total_loss: 8.1, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 1.0, lr: 2.499e-03, size: 160, ETA: 13:38:37
2024-02-18 22:40:43 | INFO | yolox.core.trainer:257 - epoch: 8/300, iter: 250/250, mem: 3871Mb, iter_time: 0.738s, data_time: 0.403s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 1.0, lr: 2.499e-03, size: 160, ETA: 13:38:54
2024-02-18 22:40:43 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:40:44 | INFO | yolox.core.trainer:199 - ---> start train epoch9
2024-02-18 22:40:49 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 10/250, mem: 3871Mb, iter_time: 0.486s, data_time: 0.201s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 1.0, lr: 2.499e-03, size: 96, ETA: 13:37:40
2024-02-18 22:40:56 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 20/250, mem: 3871Mb, iter_time: 0.725s, data_time: 0.410s, total_loss: 8.2, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 1.1, lr: 2.499e-03, size: 128, ETA: 13:37:52
2024-02-18 22:41:00 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 30/250, mem: 3871Mb, iter_time: 0.393s, data_time: 0.115s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 1.0, lr: 2.499e-03, size: 96, ETA: 13:36:05
2024-02-18 22:41:09 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 40/250, mem: 3871Mb, iter_time: 0.885s, data_time: 0.535s, total_loss: 8.1, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 1.2, lr: 2.499e-03, size: 128, ETA: 13:37:15
2024-02-18 22:41:16 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 50/250, mem: 3871Mb, iter_time: 0.771s, data_time: 0.492s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.5, cls_loss: 1.1, lr: 2.499e-03, size: 96, ETA: 13:37:43
2024-02-18 22:41:21 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 60/250, mem: 3871Mb, iter_time: 0.510s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.1, lr: 2.499e-03, size: 320, ETA: 13:36:39
2024-02-18 22:41:30 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 70/250, mem: 3871Mb, iter_time: 0.890s, data_time: 0.186s, total_loss: 8.7, iou_loss: 2.2, l1_loss: 1.5, conf_loss: 3.9, cls_loss: 1.2, lr: 2.499e-03, size: 416, ETA: 13:37:49
2024-02-18 22:41:36 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 80/250, mem: 3871Mb, iter_time: 0.566s, data_time: 0.221s, total_loss: 7.4, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 1.1, lr: 2.499e-03, size: 224, ETA: 13:37:05
2024-02-18 22:41:41 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 90/250, mem: 3871Mb, iter_time: 0.515s, data_time: 0.171s, total_loss: 7.1, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 1.0, lr: 2.499e-03, size: 224, ETA: 13:36:03
2024-02-18 22:41:50 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 100/250, mem: 3871Mb, iter_time: 0.842s, data_time: 0.428s, total_loss: 7.6, iou_loss: 2.1, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.0, lr: 2.499e-03, size: 256, ETA: 13:36:56
2024-02-18 22:41:55 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 110/250, mem: 3871Mb, iter_time: 0.552s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 1.1, lr: 2.499e-03, size: 320, ETA: 13:36:07
2024-02-18 22:42:02 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 120/250, mem: 3871Mb, iter_time: 0.696s, data_time: 0.325s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 1.0, lr: 2.498e-03, size: 224, ETA: 13:36:09
2024-02-18 22:42:10 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 130/250, mem: 3871Mb, iter_time: 0.800s, data_time: 0.338s, total_loss: 7.9, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.2, lr: 2.498e-03, size: 288, ETA: 13:36:46
2024-02-18 22:42:16 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 140/250, mem: 3871Mb, iter_time: 0.623s, data_time: 0.001s, total_loss: 7.1, iou_loss: 1.9, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 1.0, lr: 2.498e-03, size: 384, ETA: 13:36:23
2024-02-18 22:42:21 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 150/250, mem: 3871Mb, iter_time: 0.496s, data_time: 0.234s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 1.1, lr: 2.498e-03, size: 96, ETA: 13:35:16
2024-02-18 22:42:29 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 160/250, mem: 3871Mb, iter_time: 0.726s, data_time: 0.353s, total_loss: 8.1, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 1.1, lr: 2.498e-03, size: 224, ETA: 13:35:28
2024-02-18 22:42:34 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 170/250, mem: 3871Mb, iter_time: 0.541s, data_time: 0.205s, total_loss: 7.4, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.1, lr: 2.498e-03, size: 192, ETA: 13:34:37
2024-02-18 22:42:42 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 180/250, mem: 3871Mb, iter_time: 0.790s, data_time: 0.432s, total_loss: 7.4, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.1, lr: 2.498e-03, size: 192, ETA: 13:35:10
2024-02-18 22:42:46 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 190/250, mem: 3871Mb, iter_time: 0.450s, data_time: 0.098s, total_loss: 7.1, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 1.0, lr: 2.498e-03, size: 224, ETA: 13:33:50
2024-02-18 22:42:55 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 200/250, mem: 3871Mb, iter_time: 0.851s, data_time: 0.363s, total_loss: 7.4, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 1.1, lr: 2.498e-03, size: 288, ETA: 13:34:43
2024-02-18 22:43:02 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 210/250, mem: 3871Mb, iter_time: 0.733s, data_time: 0.423s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.9, lr: 2.498e-03, size: 128, ETA: 13:34:56
2024-02-18 22:43:07 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 220/250, mem: 3871Mb, iter_time: 0.508s, data_time: 0.059s, total_loss: 8.1, iou_loss: 2.2, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 1.1, lr: 2.498e-03, size: 288, ETA: 13:33:56
2024-02-18 22:43:14 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 230/250, mem: 3871Mb, iter_time: 0.682s, data_time: 0.345s, total_loss: 7.1, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 1.0, lr: 2.498e-03, size: 192, ETA: 13:33:53
2024-02-18 22:43:21 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 240/250, mem: 3871Mb, iter_time: 0.721s, data_time: 0.264s, total_loss: 7.6, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 1.0, lr: 2.498e-03, size: 288, ETA: 13:34:03
2024-02-18 22:43:30 | INFO | yolox.core.trainer:257 - epoch: 9/300, iter: 250/250, mem: 3871Mb, iter_time: 0.818s, data_time: 0.111s, total_loss: 7.8, iou_loss: 2.0, l1_loss: 1.5, conf_loss: 3.2, cls_loss: 1.1, lr: 2.498e-03, size: 416, ETA: 13:34:43
2024-02-18 22:43:30 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
2024-02-18 22:43:31 | INFO | yolox.core.trainer:199 - ---> start train epoch10
2024-02-18 22:43:36 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 10/250, mem: 3871Mb, iter_time: 0.502s, data_time: 0.001s, total_loss: 6.8, iou_loss: 1.9, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.9, lr: 2.498e-03, size: 320, ETA: 13:33:42
2024-02-18 22:43:39 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 20/250, mem: 3871Mb, iter_time: 0.358s, data_time: 0.056s, total_loss: 7.6, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 1.1, lr: 2.498e-03, size: 160, ETA: 13:31:55
2024-02-18 22:43:48 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 30/250, mem: 3871Mb, iter_time: 0.900s, data_time: 0.364s, total_loss: 7.2, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 1.1, lr: 2.498e-03, size: 320, ETA: 13:33:02
2024-02-18 22:43:55 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 40/250, mem: 3871Mb, iter_time: 0.668s, data_time: 0.338s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 2.8, cls_loss: 1.0, lr: 2.498e-03, size: 192, ETA: 13:32:54
2024-02-18 22:43:59 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 50/250, mem: 3871Mb, iter_time: 0.426s, data_time: 0.126s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 1.1, lr: 2.498e-03, size: 128, ETA: 13:31:30
2024-02-18 22:44:08 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 60/250, mem: 3871Mb, iter_time: 0.886s, data_time: 0.346s, total_loss: 7.1, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 1.1, lr: 2.498e-03, size: 320, ETA: 13:32:31
2024-02-18 22:44:14 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 70/250, mem: 3871Mb, iter_time: 0.617s, data_time: 0.303s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 1.0, lr: 2.498e-03, size: 160, ETA: 13:32:08
2024-02-18 22:44:21 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 80/250, mem: 3871Mb, iter_time: 0.692s, data_time: 0.081s, total_loss: 7.6, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.1, lr: 2.498e-03, size: 384, ETA: 13:32:08
2024-02-18 22:44:27 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 90/250, mem: 3871Mb, iter_time: 0.577s, data_time: 0.309s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.2, cls_loss: 1.2, lr: 2.498e-03, size: 96, ETA: 13:31:32
2024-02-18 22:44:31 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 100/250, mem: 3871Mb, iter_time: 0.440s, data_time: 0.125s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 1.0, lr: 2.498e-03, size: 192, ETA: 13:30:14
2024-02-18 22:44:40 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 110/250, mem: 3871Mb, iter_time: 0.867s, data_time: 0.388s, total_loss: 6.8, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 1.0, lr: 2.498e-03, size: 288, ETA: 13:31:08
2024-02-18 22:44:47 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 120/250, mem: 3871Mb, iter_time: 0.713s, data_time: 0.314s, total_loss: 6.9, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.9, lr: 2.497e-03, size: 256, ETA: 13:31:15
2024-02-18 22:44:52 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 130/250, mem: 3871Mb, iter_time: 0.519s, data_time: 0.064s, total_loss: 7.6, iou_loss: 2.1, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.1, lr: 2.497e-03, size: 288, ETA: 13:30:22
2024-02-18 22:45:00 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 140/250, mem: 3871Mb, iter_time: 0.784s, data_time: 0.267s, total_loss: 7.3, iou_loss: 1.9, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 1.0, lr: 2.497e-03, size: 320, ETA: 13:30:50
2024-02-18 22:45:06 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 150/250, mem: 3871Mb, iter_time: 0.629s, data_time: 0.122s, total_loss: 6.8, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 1.0, lr: 2.497e-03, size: 320, ETA: 13:30:31
2024-02-18 22:45:12 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 160/250, mem: 3871Mb, iter_time: 0.587s, data_time: 0.254s, total_loss: 6.7, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.9, lr: 2.497e-03, size: 224, ETA: 13:29:59
2024-02-18 22:45:19 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 170/250, mem: 3871Mb, iter_time: 0.678s, data_time: 0.390s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 1.0, lr: 2.497e-03, size: 160, ETA: 13:29:55
2024-02-18 22:45:24 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 180/250, mem: 3871Mb, iter_time: 0.475s, data_time: 0.179s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 1.0, lr: 2.497e-03, size: 192, ETA: 13:28:50
2024-02-18 22:45:33 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 190/250, mem: 3871Mb, iter_time: 0.935s, data_time: 0.313s, total_loss: 7.6, iou_loss: 2.0, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 1.0, lr: 2.497e-03, size: 384, ETA: 13:30:03
2024-02-18 22:45:39 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 200/250, mem: 3871Mb, iter_time: 0.626s, data_time: 0.269s, total_loss: 7.3, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.1, lr: 2.497e-03, size: 224, ETA: 13:29:43
2024-02-18 22:45:46 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 210/250, mem: 3871Mb, iter_time: 0.661s, data_time: 0.135s, total_loss: 6.8, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 1.0, lr: 2.497e-03, size: 320, ETA: 13:29:34
2024-02-18 22:45:52 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 220/250, mem: 3871Mb, iter_time: 0.592s, data_time: 0.303s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 1.1, lr: 2.497e-03, size: 160, ETA: 13:29:04
2024-02-18 22:45:58 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 230/250, mem: 3871Mb, iter_time: 0.621s, data_time: 0.330s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 1.1, lr: 2.497e-03, size: 96, ETA: 13:28:43
2024-02-18 22:46:06 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 240/250, mem: 3871Mb, iter_time: 0.755s, data_time: 0.129s, total_loss: 7.9, iou_loss: 2.1, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 1.1, lr: 2.497e-03, size: 384, ETA: 13:29:02
2024-02-18 22:46:11 | INFO | yolox.core.trainer:257 - epoch: 10/300, iter: 250/250, mem: 3871Mb, iter_time: 0.571s, data_time: 0.284s, total_loss: 8.0, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 1.0, lr: 2.497e-03, size: 128, ETA: 13:28:26
2024-02-18 22:46:11 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet
100%|##########| 125/125 [00:32<00:00, 3.86it/s]
2024-02-18 22:46:45 | INFO | yolox.evaluators.coco_evaluator:235 - Evaluate in main process...
2024-02-18 22:46:52 | INFO | yolox.evaluators.coco_evaluator:268 - Loading and preparing results...
2024-02-18 22:46:54 | INFO | yolox.evaluators.coco_evaluator:268 - DONE (t=2.22s)
2024-02-18 22:46:54 | INFO | pycocotools.coco:366 - creating index...
2024-02-18 22:46:54 | INFO | pycocotools.coco:366 - index created!
Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 1.51 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 0.26 seconds.
2024-02-18 22:46:56 | INFO | yolox.core.trainer:349 -
Average forward time: 4.75 ms, Average NMS time: 1.02 ms, Average inference time: 5.77 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.040
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.101
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.019
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.045
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.041
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.041
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.084
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.107
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.080
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.113
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.116
2024-02-18 22:46:56 | INFO | yolox.core.trainer:359 - Save weights to ./YOLOX_outputs/yolox_regnet