2022-05-20 16:58:21.594 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 16:58:21.596 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 16:58:21.603 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (28476), thread 'MainThread' (139739628771072): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f1738a7c110>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f17a9a21540, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪══... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤════════════════════════════╕ │ │ keys │ values │ │ ╞═══════════════════╪═... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 75, in train self.before_train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 140, in before_train model = self.exp.get_model() │ │ └ │ └ ╒═══════════════════╤════════════════════════════╕ │ │ keys │ values │ │ ╞═══════════════════╪══... └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/exp/ppyoloe_base.py", line 134, in get_model depth_mult=self.depth │ └ 0.33 └ ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪══... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/backbone.py", line 203, in __init__ *[CSPResStage(BasicBlock, channels[i], channels[i + 1], layers[i], 2, act=act) for i in range(n)] │ │ │ │ │ │ └ 4 │ │ │ │ │ └ 'swish' │ │ │ │ └ [1, 2, 2, 1] │ │ │ └ [32, 64, 128, 256, 512] │ │ └ [32, 64, 128, 256, 512] │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/backbone.py", line 203, in *[CSPResStage(BasicBlock, channels[i], channels[i + 1], layers[i], 2, act=act) for i in range(n)] │ │ │ │ │ │ │ │ │ └ 0 │ │ │ │ │ │ │ │ └ 'swish' │ │ │ │ │ │ │ └ 0 │ │ │ │ │ │ └ [1, 2, 2, 1] │ │ │ │ │ └ 0 │ │ │ │ └ [32, 64, 128, 256, 512] │ │ │ └ 0 │ │ └ [32, 64, 128, 256, 512] │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/backbone.py", line 50, in __init__ self.attn = EffectiveSELayer(ch_mid, act='sigmoid') │ │ └ 48 │ └ └ CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=F... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/backbone.py", line 18, in __init__ str, dict)) else act └ 'sigmoid' File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/network_blocks.py", line 34, in get_activation module = nn.Sigmoid(inplace=inplace) │ │ └ True │ └ └ TypeError: __init__() got an unexpected keyword argument 'inplace' 2022-05-20 16:59:21.599 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 16:59:21.600 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 16:59:21.745 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 16:59:24.331 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 16:59:24.437 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:02:05.686 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=161.25s) 2022-05-20 17:02:05.686 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:02:07.113 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:02:32.437 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:03:06.412 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:03:22.303 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=15.89s) 2022-05-20 17:03:22.303 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:03:22.333 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:03:23.381 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:03:23.383 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:03:23.384 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:03:23.384 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:03:23.384 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:03:24.406 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 17:03:24.406 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (28559), thread 'MainThread' (139759600039680): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f1bdf092110>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f1c50086030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 244.7500, 165.8750, 288.7500, 361.5000], │ │ │ [ 0.0000, 187.0000, 41.9375, 211.3750, 271.2500], │ │ │ ... │ │ └ tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 0} │ │ └ tensor([[[ 0.0000, 244.7500, 165.8750, 288.7500, 361.5000], │ │ [ 0.0000, 187.0000, 41.9375, 211.3750, 271.2500], │ │ ... │ └ [tensor([[[[ 9.5312e-01, 1.6475e+00, 1.9219e+00, ..., 1.5176e+00, │ 1.6641e+00, 1.9219e+00], │ [ 1.191... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 9.5312e-01, 1.6475e+00, 1.9219e+00, ..., 1.5176e+00, │ │ 1.6641e+00, 1.9219e+00], │ │ [ 1.19... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 218, in forward return self.forward_train(feats, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 244.7500, 165.8750, 288.7500, 361.5000], │ │ │ [ 0.0000, 187.0000, 41.9375, 211.3750, 271.2500], │ │ │ ... │ │ └ [tensor([[[[ 9.5312e-01, 1.6475e+00, 1.9219e+00, ..., 1.5176e+00, │ │ 1.6641e+00, 1.9219e+00], │ │ [ 1.191... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 144, in forward_train ], targets, extra_info) │ └ {'epoch': 0} └ tensor([[[ 0.0000, 244.7500, 165.8750, 288.7500, 361.5000], [ 0.0000, 187.0000, 41.9375, 211.3750, 271.2500], ... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 276, in get_loss one_hot_label) └ tensor([[[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., ... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[0.0016, 0.0051, 0.0029, ..., 0.0028, 0.0070, 0.0053], │ │ [0.0049, 0.0044, 0.0043, ..., 0.0032, 0.0031, 0.0... │ └ └ VarifocalLoss() File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/losses.py", line 101, in forward loss = (F.binary_cross_entropy(pred_score, gt_score, reduction='none') * weight).sum() │ │ │ │ └ tensor([[[1.9001e-06, 1.9219e-05, 6.5130e-06, ..., 6.0250e-06, │ │ │ │ 3.6310e-05, 2.1422e-05], │ │ │ │ [1.8049e-05, 1.44... │ │ │ └ tensor([[[0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ ... │ │ └ tensor([[[0.0016, 0.0051, 0.0029, ..., 0.0028, 0.0070, 0.0053], │ │ [0.0049, 0.0044, 0.0043, ..., 0.0032, 0.0031, 0.00... │ └ └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/functional.py", line 2526, in binary_cross_entropy input, target, weight, reduction_enum) │ │ │ └ 0 │ │ └ None │ └ tensor([[[0., 0., 0., ..., 0., 0., 0.], │ [0., 0., 0., ..., 0., 0., 0.], │ [0., 0., 0., ..., 0., 0., 0.], │ ... └ tensor([[[0.0016, 0.0051, 0.0029, ..., 0.0028, 0.0070, 0.0053], [0.0049, 0.0044, 0.0043, ..., 0.0032, 0.0031, 0.00... RuntimeError: torch.nn.functional.binary_cross_entropy and torch.nn.BCELoss are unsafe to autocast. Many models use a sigmoid layer right before the binary cross entropy layer. In this case, combine the two layers using torch.nn.functional.binary_cross_entropy_with_logits or torch.nn.BCEWithLogitsLoss. binary_cross_entropy_with_logits and BCEWithLogits are safe to autocast. 2022-05-20 17:17:54.113 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 17:17:54.115 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 17:17:54.265 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 17:17:56.644 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 17:17:56.749 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:18:06.989 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=10.24s) 2022-05-20 17:18:06.989 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:18:08.390 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:18:33.483 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:18:43.724 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:18:44.053 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=0.33s) 2022-05-20 17:18:44.053 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:18:44.083 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:18:45.091 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:18:45.093 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:18:45.093 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:18:45.093 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:18:45.094 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:18:45.970 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 17:18:45.971 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (3029), thread 'MainThread' (140042873988864): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f5dd37a9190>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f5e4479d030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 244.6250, 326.5000, 302.2500, 470.2500], │ │ │ [ 0.0000, 168.8750, 235.5000, 200.7500, 404.0000], │ │ │ ... │ │ └ tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 0} │ │ └ tensor([[[ 0.0000, 244.6250, 326.5000, 302.2500, 470.2500], │ │ [ 0.0000, 168.8750, 235.5000, 200.7500, 404.0000], │ │ ... │ └ [tensor([[[[ 1.0703e+00, 1.5078e+00, 1.3242e+00, ..., 1.5869e+00, │ 1.5498e+00, 1.2832e+00], │ [ 5.717... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 1.0703e+00, 1.5078e+00, 1.3242e+00, ..., 1.5869e+00, │ │ 1.5498e+00, 1.2832e+00], │ │ [ 5.71... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 218, in forward return self.forward_train(feats, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 244.6250, 326.5000, 302.2500, 470.2500], │ │ │ [ 0.0000, 168.8750, 235.5000, 200.7500, 404.0000], │ │ │ ... │ │ └ [tensor([[[[ 1.0703e+00, 1.5078e+00, 1.3242e+00, ..., 1.5869e+00, │ │ 1.5498e+00, 1.2832e+00], │ │ [ 5.717... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 144, in forward_train ], targets, extra_info) │ └ {'epoch': 0} └ tensor([[[ 0.0000, 244.6250, 326.5000, 302.2500, 470.2500], [ 0.0000, 168.8750, 235.5000, 200.7500, 404.0000], ... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 255, in get_loss pred_bboxes=pred_bboxes.detach() * stride_tensor) │ │ └ tensor([[32.], │ │ [32.], │ │ [32.], │ │ ..., │ │ [ 8.], │ │ [ 8.], │ │ [ 8.]], device='cuda:0', dtyp... │ └ └ tensor([[[ -0.9355, -2.3125, 5.6016, 5.5977], [ -0.6035, -1.2676, 5.5469, 4.9688], [ -0.8574, -1... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[-29.9375, -74.0000, 179.2500, 179.1250], │ │ │ [-19.3125, -40.5625, 177.5000, 15... │ │ └ (tensor([[-64., -64., 96., 96.], │ │ [-32., -64., 128., 96.], │ │ [ 0., -64., 160., 96.], │ │ ..., │ │ [... │ └ └ ATSSAssigner() File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context return func(*args, **kwargs) │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[-29.9375, -74.0000, 179.2500, 179.1250], │ │ [-19.3125, -40.5625, 177.5000, 15... │ └ (ATSSAssigner(), tensor([[-64., -64., 96., 96.], │ [-32., -64., 128., 96.], │ [ 0., -64., 160., 96.], │ ... └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/assigner/atss_assigner.py", line 118, in forward mask_positive) └ tensor([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ... RuntimeError: expected scalar type c10::Half but found float 2022-05-20 17:21:11.667 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 17:21:11.668 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 17:21:11.812 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 17:21:14.200 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 17:21:14.305 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:21:24.322 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=10.02s) 2022-05-20 17:21:24.323 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:21:25.716 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:21:50.146 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:22:00.235 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:22:00.565 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=0.33s) 2022-05-20 17:22:00.566 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:22:00.595 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:22:01.606 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:22:01.608 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:22:01.608 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:22:01.608 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:22:01.609 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:29:18.899 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 17:29:18.899 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (4689), thread 'MainThread' (140001245382400): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f5422387110>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f549337b030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 393.5000, 394.0000, 433.5000, 513.0000], │ │ │ [ 0.0000, 464.0000, 318.5000, 486.0000, 458.0000], │ │ │ ... │ │ └ tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 0} │ │ └ tensor([[[ 0.0000, 393.5000, 394.0000, 433.5000, 513.0000], │ │ [ 0.0000, 464.0000, 318.5000, 486.0000, 458.0000], │ │ ... │ └ [tensor([[[[ 1.1465e+00, 1.6104e+00, 1.5947e+00, ..., 1.5254e+00, │ 1.6562e+00, 1.8730e+00], │ [ 8.623... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 1.1465e+00, 1.6104e+00, 1.5947e+00, ..., 1.5254e+00, │ │ 1.6562e+00, 1.8730e+00], │ │ [ 8.62... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 218, in forward if self.training: │ └ True └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 144, in forward_train num_anchors_list, stride_tensor │ └ tensor([[32.], │ [32.], │ [32.], │ ..., │ [ 8.], │ [ 8.], │ [ 8.]], device='cuda:0', dtyp... └ [576, 2304, 9216] File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 276, in get_loss loss_cls = self.varifocal_loss(pred_scores, assigned_scores, │ │ └ tensor([[[0., 0., 0., ..., 0., 0., 0.], │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ ... │ └ tensor([[[0.0040, 0.0070, 0.0055, ..., 0.0051, 0.0114, 0.0079], │ [0.0138, 0.0064, 0.0090, ..., 0.0059, 0.0061, 0.00... └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[0.0040, 0.0070, 0.0055, ..., 0.0051, 0.0114, 0.0079], │ │ [0.0138, 0.0064, 0.0090, ..., 0.0059, 0.0061, 0.0... │ └ └ VarifocalLoss() File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/losses.py", line 103, in forward loss = (F.binary_cross_entropy(pred_score, gt_score, reduction='none') * weight).sum() │ │ │ │ └ tensor([[[1.1759e-05, 3.6590e-05, 2.2978e-05, ..., 1.9509e-05, │ │ │ │ 9.7050e-05, 4.6494e-05], │ │ │ │ [1.4381e-04, 3.06... │ │ │ └ tensor([[[0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ ... │ │ └ tensor([[[0.0040, 0.0070, 0.0055, ..., 0.0051, 0.0114, 0.0079], │ │ [0.0138, 0.0064, 0.0090, ..., 0.0059, 0.0061, 0.00... │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/losses.py", line 103, in forward loss = (F.binary_cross_entropy(pred_score, gt_score, reduction='none') * weight).sum() │ │ │ │ └ tensor([[[1.1759e-05, 3.6590e-05, 2.2978e-05, ..., 1.9509e-05, │ │ │ │ 9.7050e-05, 4.6494e-05], │ │ │ │ [1.4381e-04, 3.06... │ │ │ └ tensor([[[0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ ... │ │ └ tensor([[[0.0040, 0.0070, 0.0055, ..., 0.0051, 0.0114, 0.0079], │ │ [0.0138, 0.0064, 0.0090, ..., 0.0059, 0.0061, 0.00... │ └ └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/bdb.py", line 88, in trace_dispatch return self.dispatch_line(frame) │ │ └ │ └ └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/bdb.py", line 113, in dispatch_line if self.quitting: raise BdbQuit │ │ └ │ └ True └ bdb.BdbQuit 2022-05-20 17:29:23.868 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 17:29:23.869 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 17:29:24.028 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 17:29:26.416 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 17:29:26.525 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:29:36.891 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=10.37s) 2022-05-20 17:29:36.891 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:29:38.342 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:30:03.393 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:30:12.968 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:30:13.313 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=0.34s) 2022-05-20 17:30:13.313 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:30:13.345 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:30:14.453 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:30:14.455 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:30:14.456 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:30:14.456 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:30:14.456 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:30:15.319 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 17:30:15.320 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (8053), thread 'MainThread' (140111044060928): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f6db2bb5150>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f6e23ba9030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 497.5000, 61.3750, 663.5000, 433.5000], │ │ │ [ 0.0000, 278.7500, 0.0000, 370.5000, 262.0000], │ │ │ ... │ │ └ tensor([[[[-1.2275, -1.3301, -1.4160, ..., 2.1641, 2.2480, -2.0488], │ │ [-1.2617, -1.3135, -1.3643, ..., 2.1797, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[-1.2275, -1.3301, -1.4160, ..., 2.1641, 2.2480, -2.0488], │ │ [-1.2617, -1.3135, -1.3643, ..., 2.1797,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 0} │ │ └ tensor([[[ 0.0000, 497.5000, 61.3750, 663.5000, 433.5000], │ │ [ 0.0000, 278.7500, 0.0000, 370.5000, 262.0000], │ │ ... │ └ [tensor([[[[ 2.2402e+00, 2.3203e+00, 2.2930e+00, ..., 2.4336e+00, │ 2.9707e+00, 2.9727e+00], │ [ 1.445... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 2.2402e+00, 2.3203e+00, 2.2930e+00, ..., 2.4336e+00, │ │ 2.9707e+00, 2.9727e+00], │ │ [ 1.44... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 219, in forward return self.forward_train(feats, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 497.5000, 61.3750, 663.5000, 433.5000], │ │ │ [ 0.0000, 278.7500, 0.0000, 370.5000, 262.0000], │ │ │ ... │ │ └ [tensor([[[[ 2.2402e+00, 2.3203e+00, 2.2930e+00, ..., 2.4336e+00, │ │ 2.9707e+00, 2.9727e+00], │ │ [ 1.445... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 145, in forward_train ], targets, extra_info) │ └ {'epoch': 0} └ tensor([[[ 0.0000, 497.5000, 61.3750, 663.5000, 433.5000], [ 0.0000, 278.7500, 0.0000, 370.5000, 262.0000], ... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 256, in get_loss pred_bboxes=pred_bboxes.detach() * stride_tensor) │ │ └ tensor([[32.], │ │ [32.], │ │ [32.], │ │ ..., │ │ [ 8.], │ │ [ 8.], │ │ [ 8.]], device='cuda:0', dtyp... │ └ └ tensor([[[-1.5566e+00, -1.3242e+00, 4.9609e+00, 4.3047e+00], [-6.2695e-01, -2.3096e-01, 5.0391e+00, 2.3477e+00],... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[-4.9812e+01, -4.2375e+01, 1.5875e+02, 1.3775e+02], │ │ │ [-2.0062e+01, -7.3906... │ │ └ (tensor([[-64., -64., 96., 96.], │ │ [-32., -64., 128., 96.], │ │ [ 0., -64., 160., 96.], │ │ ..., │ │ [... │ └ └ ATSSAssigner() File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context return func(*args, **kwargs) │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[-4.9812e+01, -4.2375e+01, 1.5875e+02, 1.3775e+02], │ │ [-2.0062e+01, -7.3906... │ └ (ATSSAssigner(), tensor([[-64., -64., 96., 96.], │ [-32., -64., 128., 96.], │ [ 0., -64., 160., 96.], │ ... └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/assigner/atss_assigner.py", line 118, in forward mask_positive) └ tensor([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ... RuntimeError: expected scalar type c10::Half but found float 2022-05-20 17:31:58.646 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 17:31:58.647 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 17:31:58.793 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 17:32:01.148 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 17:32:01.252 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:32:11.268 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=10.02s) 2022-05-20 17:32:11.269 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:32:12.639 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:32:38.663 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:32:48.241 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:32:48.570 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=0.33s) 2022-05-20 17:32:48.570 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:32:48.599 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:32:49.720 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:32:49.722 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:32:49.722 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:32:49.722 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:32:49.723 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:32:50.570 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 17:32:50.570 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (9224), thread 'MainThread' (140083756197632): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f67583f9250>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f67c93ed030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 396.0000, 192.2500, 438.0000, 296.7500], │ │ │ [ 0.0000, 341.0000, 126.1250, 364.2500, 248.6250], │ │ │ ... │ │ └ tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 0} │ │ └ tensor([[[ 0.0000, 396.0000, 192.2500, 438.0000, 296.7500], │ │ [ 0.0000, 341.0000, 126.1250, 364.2500, 248.6250], │ │ ... │ └ [tensor([[[[ 1.5342e+00, 1.8896e+00, 1.7275e+00, ..., 9.9707e-01, │ 1.1484e+00, 1.6348e+00], │ [ 1.099... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 1.5342e+00, 1.8896e+00, 1.7275e+00, ..., 9.9707e-01, │ │ 1.1484e+00, 1.6348e+00], │ │ [ 1.09... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 219, in forward return self.forward_train(feats, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 396.0000, 192.2500, 438.0000, 296.7500], │ │ │ [ 0.0000, 341.0000, 126.1250, 364.2500, 248.6250], │ │ │ ... │ │ └ [tensor([[[[ 1.5342e+00, 1.8896e+00, 1.7275e+00, ..., 9.9707e-01, │ │ 1.1484e+00, 1.6348e+00], │ │ [ 1.099... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 145, in forward_train ], targets, extra_info) │ └ {'epoch': 0} └ tensor([[[ 0.0000, 396.0000, 192.2500, 438.0000, 296.7500], [ 0.0000, 341.0000, 126.1250, 364.2500, 248.6250], ... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 256, in get_loss pred_bboxes=pred_bboxes.detach() * stride_tensor) │ │ └ tensor([[32.], │ │ [32.], │ │ [32.], │ │ ..., │ │ [ 8.], │ │ [ 8.], │ │ [ 8.]], device='cuda:0', dtyp... │ └ └ tensor([[[-1.6484e+00, -2.4316e+00, 5.2344e+00, 5.5664e+00], [-7.4219e-01, -9.1211e-01, 4.9375e+00, 4.6719e+00],... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[ -52.7500, -77.8125, 167.5000, 178.1250], │ │ │ [ -23.7500, -29.1875, 158.0... │ │ └ (tensor([[-64., -64., 96., 96.], │ │ [-32., -64., 128., 96.], │ │ [ 0., -64., 160., 96.], │ │ ..., │ │ [... │ └ └ ATSSAssigner() File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context return func(*args, **kwargs) │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[ -52.7500, -77.8125, 167.5000, 178.1250], │ │ [ -23.7500, -29.1875, 158.0... │ └ (ATSSAssigner(), tensor([[-64., -64., 96., 96.], │ [-32., -64., 128., 96.], │ [ 0., -64., 160., 96.], │ ... └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/assigner/atss_assigner.py", line 118, in forward mask_positive) └ tensor([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ... RuntimeError: expected scalar type c10::Half but found float 2022-05-20 17:36:05.666 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 17:36:05.667 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 17:36:05.812 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 17:36:08.224 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 17:36:08.330 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:36:18.512 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=10.18s) 2022-05-20 17:36:18.513 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:36:19.919 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:36:45.959 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:36:55.101 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:36:55.437 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=0.34s) 2022-05-20 17:36:55.438 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:36:55.468 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:36:56.555 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:36:56.557 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:36:56.557 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:36:56.558 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:36:56.558 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:38:39.102 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 17:38:39.103 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (10720), thread 'MainThread' (140031327995648): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f5b2348b190>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f5b9447f030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 325.7500, 247.2500, 434.7500, 519.0000], │ │ │ [ 0.0000, 518.0000, 75.5000, 578.5000, 393.7500], │ │ │ ... │ │ └ tensor([[[[ 0.2111, 0.1597, 0.1255, ..., 0.4165, 0.2454, 0.2795], │ │ [ 0.1940, 0.1597, 0.1768, ..., 0.3652, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[ 0.2111, 0.1597, 0.1255, ..., 0.4165, 0.2454, 0.2795], │ │ [ 0.1940, 0.1597, 0.1768, ..., 0.3652,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 0} │ │ └ tensor([[[ 0.0000, 325.7500, 247.2500, 434.7500, 519.0000], │ │ [ 0.0000, 518.0000, 75.5000, 578.5000, 393.7500], │ │ ... │ └ [tensor([[[[ 3.2324e+00, 2.7090e+00, 2.5684e+00, ..., 1.9688e+00, │ 1.7480e+00, 2.3945e+00], │ [ 1.703... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 3.2324e+00, 2.7090e+00, 2.5684e+00, ..., 1.9688e+00, │ │ 1.7480e+00, 2.3945e+00], │ │ [ 1.70... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 219, in forward return self.forward_train(feats, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 325.7500, 247.2500, 434.7500, 519.0000], │ │ │ [ 0.0000, 518.0000, 75.5000, 578.5000, 393.7500], │ │ │ ... │ │ └ [tensor([[[[ 3.2324e+00, 2.7090e+00, 2.5684e+00, ..., 1.9688e+00, │ │ 1.7480e+00, 2.3945e+00], │ │ [ 1.703... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 145, in forward_train ], targets, extra_info) │ └ {'epoch': 0} └ tensor([[[ 0.0000, 325.7500, 247.2500, 434.7500, 519.0000], [ 0.0000, 518.0000, 75.5000, 578.5000, 393.7500], ... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 256, in get_loss pred_bboxes=pred_bboxes.detach() * stride_tensor) │ │ └ tensor([[32.], │ │ [32.], │ │ [32.], │ │ ..., │ │ [ 8.], │ │ [ 8.], │ │ [ 8.]], device='cuda:0', dtyp... │ └ └ tensor([[[-4.3438e+00, -4.4844e+00, 8.7109e+00, 6.0000e+00], [-3.0273e+00, -2.6387e+00, 9.8750e+00, 4.2930e+00],... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[-1.3900e+02, -1.4350e+02, 2.7875e+02, 1.9200e+02], │ │ │ [-9.6875e+01, -8.4438... │ │ └ (tensor([[-64., -64., 96., 96.], │ │ [-32., -64., 128., 96.], │ │ [ 0., -64., 160., 96.], │ │ ..., │ │ [... │ └ └ ATSSAssigner() File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context return func(*args, **kwargs) │ │ └ {'bg_index': 80, 'pred_bboxes': tensor([[[-1.3900e+02, -1.4350e+02, 2.7875e+02, 1.9200e+02], │ │ [-9.6875e+01, -8.4438... │ └ (ATSSAssigner(), tensor([[-64., -64., 96., 96.], │ [-32., -64., 128., 96.], │ [ 0., -64., 160., 96.], │ ... └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/assigner/atss_assigner.py", line 119, in forward mask_positive = torch.where(mask_multiple_gts, is_max_iou, │ │ │ └ tensor([[[1., 1., 1., ..., 1., 1., 1.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ ... │ │ └ tensor([[[False, False, False, ..., False, False, False], │ │ [False, False, False, ..., False, False, False], │ │ ... │ └ └ RuntimeError: expected scalar type c10::Half but found float 2022-05-20 17:39:50.714 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 17:39:50.716 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 17:39:50.863 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 17:39:53.206 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 17:39:53.310 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:40:03.244 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=9.93s) 2022-05-20 17:40:03.245 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:40:04.616 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:40:29.379 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:40:37.964 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:40:38.292 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=0.33s) 2022-05-20 17:40:38.292 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:40:38.322 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:40:39.343 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:40:39.346 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:40:39.346 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:40:39.346 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:40:39.346 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:46:58.852 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 17:46:58.854 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (12261), thread 'MainThread' (139859865110272): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f33374cb050>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f33a84bf030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 354.5000, 276.2500, 439.5000, 488.0000], │ │ │ [ 0.0000, 242.7500, 142.3750, 289.7500, 390.5000], │ │ │ ... │ │ └ tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 0} │ │ └ tensor([[[ 0.0000, 354.5000, 276.2500, 439.5000, 488.0000], │ │ [ 0.0000, 242.7500, 142.3750, 289.7500, 390.5000], │ │ ... │ └ [tensor([[[[ 1.1787e+00, 1.5879e+00, 1.3701e+00, ..., 7.9980e-01, │ 1.1367e+00, 1.3320e+00], │ [ 1.155... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 1.1787e+00, 1.5879e+00, 1.3701e+00, ..., 7.9980e-01, │ │ 1.1367e+00, 1.3320e+00], │ │ [ 1.15... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 219, in forward return self.forward_train(feats, targets, extra_info) │ │ │ │ └ {'epoch': 0} │ │ │ └ tensor([[[ 0.0000, 354.5000, 276.2500, 439.5000, 488.0000], │ │ │ [ 0.0000, 242.7500, 142.3750, 289.7500, 390.5000], │ │ │ ... │ │ └ [tensor([[[[ 1.1787e+00, 1.5879e+00, 1.3701e+00, ..., 7.9980e-01, │ │ 1.1367e+00, 1.3320e+00], │ │ [ 1.155... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 145, in forward_train ], targets, extra_info) │ └ {'epoch': 0} └ tensor([[[ 0.0000, 354.5000, 276.2500, 439.5000, 488.0000], [ 0.0000, 242.7500, 142.3750, 289.7500, 390.5000], ... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 277, in get_loss one_hot_label) └ tensor([[[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., ... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[0.0008, 0.0033, 0.0018, ..., 0.0022, 0.0052, 0.0036], │ │ [0.0040, 0.0038, 0.0041, ..., 0.0036, 0.0025, 0.0... │ └ └ VarifocalLoss() File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/losses.py", line 103, in forward loss = (F.binary_cross_entropy(pred_score, gt_score, reduction='none') * weight).sum() │ │ │ │ └ tensor([[[4.9632e-07, 8.1001e-06, 2.5146e-06, ..., 3.4902e-06, │ │ │ │ 1.9979e-05, 9.8395e-06], │ │ │ │ [1.1941e-05, 1.08... │ │ │ └ tensor([[[0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ ... │ │ └ tensor([[[0.0008, 0.0033, 0.0018, ..., 0.0022, 0.0052, 0.0036], │ │ [0.0040, 0.0038, 0.0041, ..., 0.0036, 0.0025, 0.00... │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/losses.py", line 103, in forward loss = (F.binary_cross_entropy(pred_score, gt_score, reduction='none') * weight).sum() │ │ │ │ └ tensor([[[4.9632e-07, 8.1001e-06, 2.5146e-06, ..., 3.4902e-06, │ │ │ │ 1.9979e-05, 9.8395e-06], │ │ │ │ [1.1941e-05, 1.08... │ │ │ └ tensor([[[0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ [0., 0., 0., ..., 0., 0., 0.], │ │ │ ... │ │ └ tensor([[[0.0008, 0.0033, 0.0018, ..., 0.0022, 0.0052, 0.0036], │ │ [0.0040, 0.0038, 0.0041, ..., 0.0036, 0.0025, 0.00... │ └ └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/bdb.py", line 88, in trace_dispatch return self.dispatch_line(frame) │ │ └ │ └ └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/bdb.py", line 113, in dispatch_line if self.quitting: raise BdbQuit │ │ └ │ └ True └ bdb.BdbQuit 2022-05-20 17:47:04.418 | INFO | yolox.core.trainer:before_train:135 - args: Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='exps/ppyoloe/default/ppyoloe_s.py', experiment_name='ppyoloe_s_sigmoid', fp16=True, logger='tensorboard', machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None) 2022-05-20 17:47:04.419 | INFO | yolox.core.trainer:before_train:136 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 80 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'swish' │ ├───────────────────┼────────────────────────────┤ │ sybn │ True │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 10 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (768, 768) │ ├───────────────────┼────────────────────────────┤ │ random_size │ (10, 24) │ ├───────────────────┼────────────────────────────┤ │ data_dir │ None │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ lr_max_epochs │ 360 │ ├───────────────────┼────────────────────────────┤ │ start_factor │ 0.0 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.000625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'ppyoloelr' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 300 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'ppyoloe_s' │ ├───────────────────┼────────────────────────────┤ │ atss_topk │ 9 │ ├───────────────────┼────────────────────────────┤ │ tal_topk │ 13 │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.6 │ ╘═══════════════════╧════════════════════════════╛ 2022-05-20 17:47:04.564 | INFO | yolox.core.trainer:before_train:142 - Model Summary: Params: 8.08M, Gflops: 17.82 2022-05-20 17:47:06.897 | INFO | yolox.core.trainer:resume_train:320 - loading checkpoint for fine tuning 2022-05-20 17:47:07.004 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:47:17.312 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=10.31s) 2022-05-20 17:47:17.312 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:47:18.702 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:47:43.459 | INFO | yolox.core.trainer:before_train:166 - init prefetcher, this might take one minute or less... 2022-05-20 17:47:52.656 | INFO | yolox.data.datasets.coco:__init__:64 - loading annotations into memory... 2022-05-20 17:47:52.994 | INFO | yolox.data.datasets.coco:__init__:64 - Done (t=0.34s) 2022-05-20 17:47:52.994 | INFO | pycocotools.coco:__init__:86 - creating index... 2022-05-20 17:47:53.024 | INFO | pycocotools.coco:__init__:86 - index created! 2022-05-20 17:47:54.036 | INFO | yolox.core.trainer:before_train:202 - Training start... 2022-05-20 17:47:54.038 | INFO | yolox.core.trainer:before_train:203 - PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (2): ConvBNLayer( (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (stages): Sequential( (0): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (1): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (3): CSPResStage( (conv_down): ConvBNLayer( (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv1): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (blocks): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (attn): EffectiveSELayer( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): Sigmoid() ) (conv3): ConvBNLayer( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (neck): CustomCSPPAN( (fpn_stages): ModuleList( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(512, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) (spp): SPP( (pool0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (pool1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (pool2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) (conv): ConvBNLayer( (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(448, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (2): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(224, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (fpn_routes): ModuleList( (0): ConvBNLayer( (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (pan_stages): Sequential( (0): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (1): Sequential( (0): CSPStage( (conv1): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): ConvBNLayer( (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (convs): Sequential( (0): BasicBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (conv2): RepVggBlock( (conv1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (conv2): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Identity() ) (act): Swish() ) ) ) (conv3): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) ) (pan_routes): Sequential( (0): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) (1): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (head): PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULoss() ) (stem_cls): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (stem_reg): ModuleList( (0): ESEAttn( (fc): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (1): ESEAttn( (fc): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) (2): ESEAttn( (fc): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1)) (sig): Sigmoid() (conv): ConvBNLayer( (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): Swish() ) ) ) (pred_cls): ModuleList( (0): Conv2d(384, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (pred_reg): ModuleList( (0): Conv2d(384, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(192, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(96, 68, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) (atss_assign): ATSSAssigner() (assigner): TaskAlignedAssigner() ) ) 2022-05-20 17:47:54.039 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch1 2022-05-20 17:47:54.039 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 17:47:54.039 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 17:48:02.007 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 10/7393, mem: 8935Mb, iter_time: 0.796s, data_time: 0.001s, total_loss: 6919.2, loss_cls: 6918.0, loss_iou: 0.2, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.705e-06, size: 768, ETA: 20 days, 10:36:33 2022-05-20 17:48:07.941 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 20/7393, mem: 8935Mb, iter_time: 0.593s, data_time: 0.002s, total_loss: 2044.8, loss_cls: 2043.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 5.411e-06, size: 640, ETA: 17 days, 19:55:39 2022-05-20 17:48:11.163 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 30/7393, mem: 8935Mb, iter_time: 0.322s, data_time: 0.002s, total_loss: 233.3, loss_cls: 231.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.3, lr: 8.116e-06, size: 448, ETA: 14 days, 15:19:16 2022-05-20 17:48:13.612 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 40/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.006s, total_loss: 108.6, loss_cls: 106.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 1.082e-05, size: 320, ETA: 12 days, 13:04:08 2022-05-20 17:48:18.908 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 50/7393, mem: 8935Mb, iter_time: 0.529s, data_time: 0.002s, total_loss: 126.3, loss_cls: 124.5, loss_iou: 0.4, loss_dfl: 1.7, loss_l1: 1.7, lr: 1.353e-05, size: 576, ETA: 12 days, 18:01:36 2022-05-20 17:48:26.486 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 60/7393, mem: 8935Mb, iter_time: 0.757s, data_time: 0.001s, total_loss: 68.6, loss_cls: 66.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 2.0, lr: 1.623e-05, size: 736, ETA: 13 days, 20:47:01 2022-05-20 17:48:28.895 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 70/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.002s, total_loss: 27.0, loss_cls: 25.0, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 1.894e-05, size: 352, ETA: 12 days, 18:22:13 2022-05-20 17:48:35.518 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 80/7393, mem: 8935Mb, iter_time: 0.662s, data_time: 0.002s, total_loss: 42.3, loss_cls: 40.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 2.164e-05, size: 672, ETA: 13 days, 7:02:04 2022-05-20 17:48:37.377 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 90/7393, mem: 8935Mb, iter_time: 0.185s, data_time: 0.002s, total_loss: 17.2, loss_cls: 15.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.6, lr: 2.435e-05, size: 352, ETA: 12 days, 8:15:24 2022-05-20 17:48:42.314 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 100/7393, mem: 8935Mb, iter_time: 0.493s, data_time: 0.002s, total_loss: 21.6, loss_cls: 20.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 2.705e-05, size: 672, ETA: 12 days, 9:00:00 2022-05-20 17:48:47.841 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 110/7393, mem: 8935Mb, iter_time: 0.552s, data_time: 0.001s, total_loss: 24.4, loss_cls: 22.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.8, lr: 2.976e-05, size: 736, ETA: 12 days, 12:55:22 2022-05-20 17:48:49.775 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 120/7393, mem: 8935Mb, iter_time: 0.193s, data_time: 0.002s, total_loss: 16.2, loss_cls: 14.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.246e-05, size: 352, ETA: 11 days, 21:43:59 2022-05-20 17:48:53.700 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 130/7393, mem: 8935Mb, iter_time: 0.392s, data_time: 0.005s, total_loss: 15.5, loss_cls: 14.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 3.517e-05, size: 576, ETA: 11 days, 18:18:46 2022-05-20 17:48:56.396 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 140/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 13.1, loss_cls: 11.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 3.787e-05, size: 384, ETA: 11 days, 9:58:07 2022-05-20 17:49:01.308 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 150/7393, mem: 8935Mb, iter_time: 0.490s, data_time: 0.004s, total_loss: 17.5, loss_cls: 15.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 4.058e-05, size: 672, ETA: 11 days, 11:50:38 2022-05-20 17:49:06.089 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 160/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.002s, total_loss: 16.8, loss_cls: 15.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 4.328e-05, size: 672, ETA: 11 days, 12:59:30 2022-05-20 17:49:12.002 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 170/7393, mem: 8935Mb, iter_time: 0.591s, data_time: 0.001s, total_loss: 21.4, loss_cls: 20.3, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.7, lr: 4.599e-05, size: 768, ETA: 11 days, 18:06:28 2022-05-20 17:49:14.554 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 180/7393, mem: 8935Mb, iter_time: 0.254s, data_time: 0.002s, total_loss: 11.6, loss_cls: 10.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 4.869e-05, size: 448, ETA: 11 days, 11:08:32 2022-05-20 17:49:20.536 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 190/7393, mem: 8935Mb, iter_time: 0.598s, data_time: 0.001s, total_loss: 17.8, loss_cls: 16.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 5.140e-05, size: 768, ETA: 11 days, 16:02:17 2022-05-20 17:49:23.520 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 200/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.002s, total_loss: 11.4, loss_cls: 10.3, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.5, lr: 5.411e-05, size: 416, ETA: 11 days, 11:12:11 2022-05-20 17:49:27.961 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 210/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 12.0, loss_cls: 10.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 5.681e-05, size: 640, ETA: 11 days, 11:06:38 2022-05-20 17:49:31.719 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 220/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 12.7, loss_cls: 11.4, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.952e-05, size: 576, ETA: 11 days, 9:06:35 2022-05-20 17:49:36.556 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 230/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 14.9, loss_cls: 13.8, loss_iou: 0.2, loss_dfl: 1.1, loss_l1: 1.1, lr: 6.222e-05, size: 672, ETA: 11 days, 10:10:23 2022-05-20 17:49:40.569 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 240/7393, mem: 8935Mb, iter_time: 0.401s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 6.493e-05, size: 512, ETA: 11 days, 9:02:02 2022-05-20 17:49:43.042 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 250/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 6.763e-05, size: 416, ETA: 11 days, 4:11:00 2022-05-20 17:49:45.860 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 260/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.004s, total_loss: 11.4, loss_cls: 10.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.034e-05, size: 448, ETA: 11 days, 0:30:06 2022-05-20 17:49:53.043 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 270/7393, mem: 8935Mb, iter_time: 0.718s, data_time: 0.003s, total_loss: 10.7, loss_cls: 9.8, loss_iou: 0.2, loss_dfl: 0.9, loss_l1: 0.7, lr: 7.304e-05, size: 704, ETA: 11 days, 7:04:40 2022-05-20 17:49:54.967 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 280/7393, mem: 8935Mb, iter_time: 0.192s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 7.575e-05, size: 352, ETA: 11 days, 1:36:37 2022-05-20 17:49:58.093 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 290/7393, mem: 8935Mb, iter_time: 0.311s, data_time: 0.007s, total_loss: 9.1, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.291s, data_time: 0.006s, total_loss: 7.5, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 8.927e-05, size: 416, ETA: 10 days, 14:48:14 2022-05-20 17:50:14.843 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 340/7393, mem: 8935Mb, iter_time: 0.403s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 9.198e-05, size: 480, ETA: 10 days, 14:36:58 2022-05-20 17:50:17.561 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 350/7393, mem: 8935Mb, iter_time: 0.271s, data_time: 0.008s, total_loss: 9.2, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 9.468e-05, size: 352, ETA: 10 days, 12:06:25 2022-05-20 17:50:20.107 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 360/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.004s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 9.739e-05, size: 384, ETA: 10 days, 9:26:25 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 550/7393, mem: 8935Mb, iter_time: 0.598s, data_time: 0.001s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.488e-04, size: 768, ETA: 10 days, 8:45:06 2022-05-20 17:51:41.809 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 560/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.001s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.3, lr: 1.515e-04, size: 704, ETA: 10 days, 9:58:28 2022-05-20 17:51:45.564 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 570/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 6.6, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.542e-04, size: 576, ETA: 10 days, 9:38:17 2022-05-20 17:51:48.690 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 580/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.0, 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iter: 730/7393, mem: 8935Mb, iter_time: 0.573s, data_time: 0.002s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.975e-04, size: 608, ETA: 10 days, 8:25:39 2022-05-20 17:52:51.043 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 740/7393, mem: 8935Mb, iter_time: 0.192s, data_time: 0.002s, total_loss: 7.0, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.9, lr: 2.002e-04, size: 352, ETA: 10 days, 6:39:55 2022-05-20 17:52:55.621 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 750/7393, mem: 8935Mb, iter_time: 0.457s, data_time: 0.003s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.029e-04, size: 640, ETA: 10 days, 7:07:44 2022-05-20 17:53:01.558 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 760/7393, mem: 8935Mb, iter_time: 0.593s, data_time: 0.001s, total_loss: 8.3, loss_cls: 7.2, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.4, lr: 2.056e-04, size: 768, ETA: 10 days, 8:41:01 2022-05-20 17:53:03.507 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 770/7393, mem: 8935Mb, iter_time: 0.194s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.083e-04, size: 352, ETA: 10 days, 7:00:17 2022-05-20 17:53:08.515 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 780/7393, mem: 8935Mb, iter_time: 0.500s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.2, lr: 2.110e-04, size: 672, ETA: 10 days, 7:47:11 2022-05-20 17:53:10.479 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 790/7393, mem: 8935Mb, iter_time: 0.196s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.137e-04, size: 352, ETA: 10 days, 6:10:24 2022-05-20 17:53:16.644 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 800/7393, mem: 8935Mb, iter_time: 0.616s, data_time: 0.003s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.8, lr: 2.164e-04, size: 768, ETA: 10 days, 7:50:05 2022-05-20 17:53:19.701 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 810/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.002s, total_loss: 6.6, loss_cls: 5.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.191e-04, size: 512, ETA: 10 days, 7:05:37 2022-05-20 17:53:22.853 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 820/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.002s, total_loss: 6.1, loss_cls: 5.1, loss_iou: 0.2, loss_dfl: 0.8, loss_l1: 0.4, lr: 2.218e-04, size: 512, ETA: 10 days, 6:26:27 2022-05-20 17:53:28.595 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 830/7393, mem: 8935Mb, iter_time: 0.574s, data_time: 0.003s, total_loss: 6.7, loss_cls: 5.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.245e-04, size: 736, ETA: 10 days, 7:43:37 2022-05-20 17:53:30.636 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 840/7393, mem: 8935Mb, iter_time: 0.203s, data_time: 0.002s, total_loss: 7.1, loss_cls: 6.1, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 1.1, lr: 2.272e-04, size: 384, ETA: 10 days, 6:16:06 2022-05-20 17:53:33.263 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 850/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.004s, total_loss: 7.3, loss_cls: 6.2, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.299e-04, size: 416, ETA: 10 days, 5:15:53 2022-05-20 17:53:36.521 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 860/7393, mem: 8935Mb, iter_time: 0.322s, data_time: 0.003s, total_loss: 7.5, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.327e-04, size: 480, ETA: 10 days, 4:43:13 2022-05-20 17:53:42.227 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 870/7393, mem: 8935Mb, iter_time: 0.570s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.354e-04, 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 950/7393, mem: 8935Mb, iter_time: 0.364s, data_time: 0.004s, total_loss: 7.4, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.570e-04, size: 544, ETA: 10 days, 5:50:14 2022-05-20 17:54:19.672 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 960/7393, mem: 8935Mb, iter_time: 0.558s, data_time: 0.001s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.5, lr: 2.597e-04, size: 736, ETA: 10 days, 6:51:11 2022-05-20 17:54:21.562 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 970/7393, mem: 8935Mb, iter_time: 0.188s, data_time: 0.003s, total_loss: 7.0, loss_cls: 5.9, loss_iou: 0.2, loss_dfl: 0.9, loss_l1: 0.4, lr: 2.624e-04, size: 352, ETA: 10 days, 5:30:09 2022-05-20 17:54:24.218 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 980/7393, mem: 8935Mb, iter_time: 0.265s, data_time: 0.005s, total_loss: 6.0, loss_cls: 4.8, loss_iou: 0.3, loss_dfl: 1.0, 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iter_time: 0.308s, data_time: 0.002s, total_loss: 6.8, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 2.759e-04, size: 512, ETA: 10 days, 4:50:15 2022-05-20 17:54:45.064 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1030/7393, mem: 8935Mb, iter_time: 0.462s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 2.786e-04, size: 640, ETA: 10 days, 5:13:13 2022-05-20 17:54:48.864 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1040/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 6.8, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 2.813e-04, size: 576, ETA: 10 days, 5:06:27 2022-05-20 17:54:53.660 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1050/7393, mem: 8935Mb, iter_time: 0.479s, data_time: 0.001s, total_loss: 8.4, loss_cls: 7.3, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 1.2, lr: 2.841e-04, size: 672, ETA: 10 days, 5:34:56 2022-05-20 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total_loss: 6.1, loss_cls: 5.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 3.246e-04, size: 576, ETA: 10 days, 2:38:42 2022-05-20 17:55:51.880 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1210/7393, mem: 8935Mb, iter_time: 0.397s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.2, lr: 3.273e-04, size: 576, ETA: 10 days, 2:39:23 2022-05-20 17:55:55.607 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1220/7393, mem: 8935Mb, iter_time: 0.372s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.300e-04, size: 576, ETA: 10 days, 2:32:41 2022-05-20 17:56:00.806 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1230/7393, mem: 8935Mb, iter_time: 0.519s, data_time: 0.001s, total_loss: 7.0, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 3.327e-04, size: 704, ETA: 10 days, 3:10:18 2022-05-20 17:56:04.508 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1240/7393, mem: 8935Mb, iter_time: 0.369s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.2, lr: 3.355e-04, size: 576, ETA: 10 days, 3:02:37 2022-05-20 17:56:07.231 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1250/7393, mem: 8935Mb, iter_time: 0.270s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 3.382e-04, size: 448, ETA: 10 days, 2:25:42 2022-05-20 17:56:10.680 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1260/7393, mem: 8935Mb, iter_time: 0.344s, data_time: 0.003s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 3.409e-04, size: 512, ETA: 10 days, 2:11:03 2022-05-20 17:56:14.290 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1270/7393, mem: 8935Mb, iter_time: 0.360s, data_time: 0.004s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 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2022-05-20 17:56:45.352 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1350/7393, mem: 8935Mb, iter_time: 0.595s, data_time: 0.001s, total_loss: 8.5, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 3.652e-04, size: 768, ETA: 10 days, 1:48:14 2022-05-20 17:56:49.431 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1360/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.001s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.679e-04, size: 608, ETA: 10 days, 1:52:09 2022-05-20 17:56:52.884 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1370/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.3, lr: 3.706e-04, size: 544, ETA: 10 days, 1:39:06 2022-05-20 17:56:55.985 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1380/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.003s, total_loss: 7.9, 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epoch: 1/300, iter: 1420/7393, mem: 8935Mb, iter_time: 0.376s, data_time: 0.002s, total_loss: 7.3, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 3.841e-04, size: 576, ETA: 10 days, 1:20:22 2022-05-20 17:57:16.772 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1430/7393, mem: 8935Mb, iter_time: 0.493s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 3.869e-04, size: 672, ETA: 10 days, 1:46:31 2022-05-20 17:57:21.923 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1440/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.002s, total_loss: 5.8, loss_cls: 4.7, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 1.1, lr: 3.896e-04, size: 704, ETA: 10 days, 2:17:44 2022-05-20 17:57:24.833 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1450/7393, mem: 8935Mb, iter_time: 0.290s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.7, loss_iou: 0.2, loss_dfl: 0.9, loss_l1: 0.4, lr: 3.923e-04, size: 480, ETA: 10 days, 1:51:17 2022-05-20 17:57:28.657 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1460/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 3.950e-04, size: 576, ETA: 10 days, 1:48:26 2022-05-20 17:57:31.792 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1470/7393, mem: 8935Mb, iter_time: 0.313s, data_time: 0.003s, total_loss: 9.0, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.977e-04, size: 512, ETA: 10 days, 1:28:14 2022-05-20 17:57:35.296 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1480/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 4.004e-04, size: 544, ETA: 10 days, 1:17:33 2022-05-20 17:57:38.968 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1490/7393, mem: 8935Mb, iter_time: 0.366s, data_time: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1530/7393, mem: 8935Mb, iter_time: 0.558s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 4.139e-04, size: 736, ETA: 10 days, 1:40:49 2022-05-20 17:57:59.319 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1540/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 7.3, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 4.166e-04, size: 544, ETA: 10 days, 1:28:04 2022-05-20 17:58:02.202 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1550/7393, mem: 8935Mb, iter_time: 0.288s, data_time: 0.003s, total_loss: 8.1, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 4.193e-04, size: 480, ETA: 10 days, 1:03:04 2022-05-20 17:58:07.591 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1560/7393, mem: 8935Mb, iter_time: 0.538s, data_time: 0.002s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 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loss_cls: 5.3, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 4.518e-04, size: 448, ETA: 9 days, 23:28:15 2022-05-20 17:58:47.676 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1680/7393, mem: 8935Mb, iter_time: 0.258s, data_time: 0.005s, total_loss: 6.5, loss_cls: 5.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 4.545e-04, size: 384, ETA: 9 days, 22:59:21 2022-05-20 17:58:52.849 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1690/7393, mem: 8935Mb, iter_time: 0.516s, data_time: 0.003s, total_loss: 7.5, loss_cls: 6.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.7, lr: 4.572e-04, size: 672, ETA: 9 days, 23:27:19 2022-05-20 17:58:55.919 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1700/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 4.599e-04, size: 512, ETA: 9 days, 23:09:17 2022-05-20 17:59:00.020 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1710/7393, mem: 8935Mb, iter_time: 0.409s, data_time: 0.003s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 4.626e-04, size: 608, ETA: 9 days, 23:13:46 2022-05-20 17:59:05.246 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1720/7393, mem: 8935Mb, iter_time: 0.522s, data_time: 0.001s, total_loss: 8.3, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 4.653e-04, size: 704, ETA: 9 days, 23:42:21 2022-05-20 17:59:10.876 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1730/7393, mem: 8935Mb, iter_time: 0.563s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 4.680e-04, size: 736, ETA: 10 days, 0:19:15 2022-05-20 17:59:16.029 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1740/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.001s, total_loss: 8.3, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 4.707e-04, size: 704, ETA: 10 days, 0:45:37 2022-05-20 17:59:18.855 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1750/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.9, loss_iou: 0.2, loss_dfl: 0.9, loss_l1: 0.5, lr: 4.734e-04, size: 480, ETA: 10 days, 0:22:31 2022-05-20 17:59:21.431 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1760/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.003s, total_loss: 5.9, loss_cls: 4.7, loss_iou: 0.3, loss_dfl: 0.9, loss_l1: 0.5, lr: 4.761e-04, size: 416, ETA: 9 days, 23:54:23 2022-05-20 17:59:26.558 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1770/7393, mem: 8935Mb, iter_time: 0.512s, data_time: 0.003s, total_loss: 8.6, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.8, lr: 4.788e-04, size: 672, ETA: 10 days, 0:19:47 2022-05-20 17:59:29.146 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1780/7393, mem: 8935Mb, iter_time: 0.258s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 4.815e-04, size: 448, ETA: 9 days, 23:52:13 2022-05-20 17:59:33.606 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1790/7393, mem: 8935Mb, iter_time: 0.445s, data_time: 0.003s, total_loss: 7.2, loss_cls: 6.2, loss_iou: 0.2, loss_dfl: 0.9, loss_l1: 0.4, lr: 4.842e-04, size: 640, ETA: 10 days, 0:03:40 2022-05-20 17:59:35.370 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1800/7393, mem: 8935Mb, iter_time: 0.176s, data_time: 0.003s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 4.869e-04, size: 320, ETA: 9 days, 23:19:37 2022-05-20 17:59:37.721 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1810/7393, mem: 8935Mb, iter_time: 0.234s, data_time: 0.006s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 4.897e-04, size: 352, ETA: 9 days, 22:47:54 2022-05-20 17:59:41.228 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1820/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.004s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 4.924e-04, size: 480, ETA: 9 days, 22:40:03 2022-05-20 17:59:46.608 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1830/7393, mem: 8935Mb, iter_time: 0.537s, data_time: 0.004s, total_loss: 8.5, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 4.951e-04, size: 704, ETA: 9 days, 23:10:12 2022-05-20 17:59:50.997 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1840/7393, mem: 8935Mb, iter_time: 0.438s, data_time: 0.001s, total_loss: 7.9, loss_cls: 6.9, loss_iou: 0.2, loss_dfl: 0.9, loss_l1: 0.8, lr: 4.978e-04, size: 640, ETA: 9 days, 23:20:06 2022-05-20 17:59:55.069 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1850/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.411s, data_time: 0.003s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 5.113e-04, size: 608, ETA: 9 days, 22:58:53 2022-05-20 18:00:12.234 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1900/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 5.140e-04, size: 480, ETA: 9 days, 22:37:58 2022-05-20 18:00:17.476 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1910/7393, mem: 8935Mb, iter_time: 0.524s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 5.167e-04, size: 704, ETA: 9 days, 23:04:12 2022-05-20 18:00:22.657 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 1920/7393, mem: 8935Mb, iter_time: 0.518s, data_time: 0.001s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.7, lr: 5.194e-04, size: 704, ETA: 9 days, 23:28:59 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epoch: 1/300, iter: 2000/7393, mem: 8935Mb, iter_time: 0.170s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 5.411e-04, size: 320, ETA: 9 days, 23:12:30 2022-05-20 18:00:56.387 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2010/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.006s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 5.438e-04, size: 512, ETA: 9 days, 23:03:35 2022-05-20 18:00:58.691 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2020/7393, mem: 8935Mb, iter_time: 0.230s, data_time: 0.004s, total_loss: 7.4, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 5.465e-04, size: 384, ETA: 9 days, 22:34:28 2022-05-20 18:01:02.733 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2030/7393, mem: 8935Mb, iter_time: 0.403s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 5.492e-04, size: 576, ETA: 9 days, 22:37:16 2022-05-20 18:01:07.638 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2040/7393, mem: 8935Mb, iter_time: 0.490s, data_time: 0.002s, total_loss: 7.3, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 5.519e-04, size: 672, ETA: 9 days, 22:55:42 2022-05-20 18:01:11.667 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2050/7393, mem: 8935Mb, iter_time: 0.402s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 5.546e-04, size: 608, ETA: 9 days, 22:58:12 2022-05-20 18:01:14.756 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2060/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 5.573e-04, size: 512, ETA: 9 days, 22:43:45 2022-05-20 18:01:18.528 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2070/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 7.3, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 5.600e-04, size: 576, ETA: 9 days, 22:41:27 2022-05-20 18:01:20.989 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2080/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 5.627e-04, size: 416, ETA: 9 days, 22:16:05 2022-05-20 18:01:23.778 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2090/7393, mem: 8935Mb, iter_time: 0.278s, data_time: 0.004s, total_loss: 7.2, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 5.654e-04, size: 416, ETA: 9 days, 21:56:43 2022-05-20 18:01:28.392 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2100/7393, mem: 8935Mb, iter_time: 0.461s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 5.681e-04, size: 640, ETA: 9 days, 22:09:41 2022-05-20 18:01:32.752 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2110/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.002s, total_loss: 6.9, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 5.708e-04, size: 640, ETA: 9 days, 22:18:07 2022-05-20 18:01:38.404 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2120/7393, mem: 8935Mb, iter_time: 0.565s, data_time: 0.002s, total_loss: 8.2, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 5.735e-04, size: 736, ETA: 9 days, 22:48:58 2022-05-20 18:01:42.527 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2130/7393, mem: 8935Mb, iter_time: 0.412s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 5.762e-04, size: 608, ETA: 9 days, 22:53:02 2022-05-20 18:01:45.234 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2140/7393, mem: 8935Mb, iter_time: 0.270s, data_time: 0.003s, total_loss: 7.3, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.554s, data_time: 0.001s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 5.897e-04, size: 736, ETA: 10 days, 0:16:05 2022-05-20 18:02:08.755 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2190/7393, mem: 8935Mb, iter_time: 0.186s, data_time: 0.003s, total_loss: 6.8, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 5.925e-04, size: 320, ETA: 9 days, 23:41:38 2022-05-20 18:02:14.609 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2200/7393, mem: 8935Mb, iter_time: 0.585s, data_time: 0.004s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.3, lr: 5.952e-04, size: 736, ETA: 10 days, 0:14:20 2022-05-20 18:02:19.763 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2210/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.001s, total_loss: 8.4, loss_cls: 7.3, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.5, lr: 5.979e-04, size: 704, ETA: 10 days, 0:35:06 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loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.087e-04, size: 480, ETA: 10 days, 0:20:29 2022-05-20 18:02:38.955 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2260/7393, mem: 8935Mb, iter_time: 0.440s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 6.114e-04, size: 640, ETA: 10 days, 0:28:29 2022-05-20 18:02:43.194 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2270/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 6.141e-04, size: 608, ETA: 10 days, 0:33:42 2022-05-20 18:02:46.998 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2280/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.002s, total_loss: 9.4, loss_cls: 8.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 6.168e-04, size: 576, ETA: 10 days, 0:31:50 2022-05-20 18:02:49.003 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2290/7393, mem: 8935Mb, iter_time: 0.200s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 6.195e-04, size: 352, ETA: 10 days, 0:00:56 2022-05-20 18:02:55.135 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2300/7393, mem: 8935Mb, iter_time: 0.613s, data_time: 0.003s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 6.222e-04, size: 768, ETA: 10 days, 0:36:37 2022-05-20 18:03:00.317 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2310/7393, mem: 8935Mb, iter_time: 0.518s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 6.249e-04, size: 704, ETA: 10 days, 0:56:48 2022-05-20 18:03:02.404 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2320/7393, mem: 8935Mb, iter_time: 0.208s, data_time: 0.002s, total_loss: 6.5, loss_cls: 5.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 6.276e-04, size: 320, ETA: 10 days, 0:27:30 2022-05-20 18:03:07.501 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2330/7393, mem: 8935Mb, iter_time: 0.509s, data_time: 0.003s, total_loss: 8.9, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 6.303e-04, size: 672, ETA: 10 days, 0:46:11 2022-05-20 18:03:09.478 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2340/7393, mem: 8935Mb, iter_time: 0.197s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 6.330e-04, size: 352, ETA: 10 days, 0:15:28 2022-05-20 18:03:11.583 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2350/7393, mem: 8935Mb, iter_time: 0.209s, data_time: 0.004s, total_loss: 7.2, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.357e-04, size: 320, ETA: 9 days, 23:46:58 2022-05-20 18:03:14.530 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2360/7393, mem: 8935Mb, iter_time: 0.294s, data_time: 0.005s, total_loss: 8.2, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 6.384e-04, size: 416, ETA: 9 days, 23:31:54 2022-05-20 18:03:19.323 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2370/7393, mem: 8935Mb, iter_time: 0.479s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.411e-04, size: 640, ETA: 9 days, 23:45:46 2022-05-20 18:03:21.951 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2380/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.002s, total_loss: 8.2, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 6.439e-04, size: 448, ETA: 9 days, 23:25:53 2022-05-20 18:03:24.785 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2390/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.005s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 6.466e-04, size: 448, ETA: 9 days, 23:09:20 2022-05-20 18:03:29.387 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2400/7393, mem: 8935Mb, iter_time: 0.459s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 6.493e-04, size: 640, ETA: 9 days, 23:20:10 2022-05-20 18:03:32.816 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2410/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 6.520e-04, size: 544, ETA: 9 days, 23:12:58 2022-05-20 18:03:35.269 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2420/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.003s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 6.547e-04, size: 416, ETA: 9 days, 22:50:53 2022-05-20 18:03:38.848 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2430/7393, mem: 8935Mb, iter_time: 0.357s, data_time: 0.003s, total_loss: 6.8, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2690/7393, mem: 8935Mb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.3, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 7.277e-04, size: 320, ETA: 9 days, 23:42:53 2022-05-20 18:05:30.286 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2700/7393, mem: 8935Mb, iter_time: 0.608s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.304e-04, size: 768, ETA: 10 days, 0:12:39 2022-05-20 18:05:35.183 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2710/7393, mem: 8935Mb, iter_time: 0.489s, data_time: 0.002s, total_loss: 8.4, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.331e-04, size: 672, ETA: 10 days, 0:26:02 2022-05-20 18:05:41.141 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2720/7393, mem: 8935Mb, iter_time: 0.595s, data_time: 0.001s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.585s, data_time: 0.004s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.467e-04, size: 736, ETA: 10 days, 0:55:32 2022-05-20 18:06:02.561 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2770/7393, mem: 8935Mb, iter_time: 0.558s, data_time: 0.001s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 7.494e-04, size: 736, ETA: 10 days, 1:17:37 2022-05-20 18:06:06.073 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2780/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.3, lr: 7.521e-04, size: 544, ETA: 10 days, 1:12:00 2022-05-20 18:06:08.157 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2790/7393, mem: 8935Mb, iter_time: 0.208s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 7.548e-04, size: 352, ETA: 10 days, 0:47:32 2022-05-20 18:06:13.556 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2800/7393, mem: 8935Mb, iter_time: 0.539s, data_time: 0.002s, total_loss: 8.2, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 7.575e-04, size: 704, ETA: 10 days, 1:06:57 2022-05-20 18:06:19.097 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2810/7393, mem: 8935Mb, iter_time: 0.554s, data_time: 0.001s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 7.602e-04, size: 736, ETA: 10 days, 1:28:08 2022-05-20 18:06:21.122 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2820/7393, mem: 8935Mb, iter_time: 0.202s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 7.629e-04, size: 384, ETA: 10 days, 1:03:07 2022-05-20 18:06:27.184 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2830/7393, mem: 8935Mb, iter_time: 0.606s, data_time: 0.001s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 7.656e-04, size: 768, ETA: 10 days, 1:30:58 2022-05-20 18:06:29.699 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2840/7393, mem: 8935Mb, iter_time: 0.251s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.683e-04, size: 416, ETA: 10 days, 1:12:28 2022-05-20 18:06:32.093 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2850/7393, mem: 8935Mb, iter_time: 0.238s, data_time: 0.003s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 7.710e-04, size: 320, ETA: 10 days, 0:52:27 2022-05-20 18:06:35.473 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2860/7393, mem: 8935Mb, iter_time: 0.337s, data_time: 0.004s, total_loss: 9.1, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 7.737e-04, size: 512, ETA: 10 days, 0:45:22 2022-05-20 18:06:38.745 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2870/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.004s, total_loss: 6.3, loss_cls: 5.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 7.764e-04, size: 512, ETA: 10 days, 0:36:54 2022-05-20 18:06:40.806 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2880/7393, mem: 8935Mb, iter_time: 0.205s, data_time: 0.003s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 7.791e-04, size: 352, ETA: 10 days, 0:12:57 2022-05-20 18:06:46.778 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2890/7393, mem: 8935Mb, iter_time: 0.596s, data_time: 0.005s, total_loss: 9.5, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.6, lr: 7.818e-04, size: 736, ETA: 10 days, 0:39:12 2022-05-20 18:06:50.254 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2900/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 7.845e-04, size: 544, ETA: 10 days, 0:33:30 2022-05-20 18:06:56.237 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2910/7393, mem: 8935Mb, iter_time: 0.598s, data_time: 0.001s, total_loss: 8.4, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 7.872e-04, size: 768, ETA: 10 days, 0:59:41 2022-05-20 18:07:00.281 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2920/7393, mem: 8935Mb, iter_time: 0.404s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.899e-04, size: 608, ETA: 10 days, 1:01:10 2022-05-20 18:07:04.570 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2930/7393, mem: 8935Mb, iter_time: 0.428s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.926e-04, size: 608, ETA: 10 days, 1:05:43 2022-05-20 18:07:07.574 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2940/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.7, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.6, lr: 7.953e-04, size: 480, ETA: 10 days, 0:54:04 2022-05-20 18:07:12.014 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2950/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 7.981e-04, size: 640, ETA: 10 days, 1:00:29 2022-05-20 18:07:15.532 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2960/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 8.008e-04, size: 544, ETA: 10 days, 0:55:21 2022-05-20 18:07:19.941 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2970/7393, mem: 8935Mb, iter_time: 0.440s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.3, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.5, lr: 8.035e-04, size: 640, ETA: 10 days, 1:01:21 2022-05-20 18:07:22.169 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2980/7393, mem: 8935Mb, iter_time: 0.222s, data_time: 0.005s, total_loss: 9.8, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 8.062e-04, size: 384, ETA: 10 days, 0:40:15 2022-05-20 18:07:24.944 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 2990/7393, mem: 8935Mb, iter_time: 0.276s, data_time: 0.003s, total_loss: 6.4, loss_cls: 4.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 8.089e-04, size: 416, ETA: 10 days, 0:26:00 2022-05-20 18:07:27.761 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3000/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.006s, total_loss: 7.5, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 8.116e-04, size: 448, ETA: 10 days, 0:12:21 2022-05-20 18:07:30.908 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3010/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.003s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.224s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 8.251e-04, size: 384, ETA: 9 days, 23:18:07 2022-05-20 18:07:48.962 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3060/7393, mem: 8935Mb, iter_time: 0.608s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 0.9, loss_l1: 0.6, lr: 8.278e-04, size: 768, ETA: 9 days, 23:44:28 2022-05-20 18:07:51.015 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3070/7393, mem: 8935Mb, iter_time: 0.204s, data_time: 0.002s, total_loss: 9.4, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 8.305e-04, size: 384, ETA: 9 days, 23:22:07 2022-05-20 18:07:53.543 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3080/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.004s, total_loss: 8.0, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 8.332e-04, size: 352, ETA: 9 days, 23:05:35 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loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 8.440e-04, size: 352, ETA: 9 days, 22:12:38 2022-05-20 18:08:07.537 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3130/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.005s, total_loss: 7.5, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 8.467e-04, size: 416, ETA: 9 days, 22:00:31 2022-05-20 18:08:10.164 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3140/7393, mem: 8935Mb, iter_time: 0.258s, data_time: 0.003s, total_loss: 7.5, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 8.495e-04, size: 320, ETA: 9 days, 21:45:18 2022-05-20 18:08:14.243 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3150/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 8.522e-04, size: 576, ETA: 9 days, 21:47:39 2022-05-20 18:08:18.367 | INFO | yolox.core.trainer:after_iter:273 - 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size: 640, ETA: 9 days, 21:43:33 2022-05-20 18:08:34.574 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3200/7393, mem: 8935Mb, iter_time: 0.517s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.3, lr: 8.657e-04, size: 704, ETA: 9 days, 21:58:34 2022-05-20 18:08:40.503 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3210/7393, mem: 8935Mb, iter_time: 0.592s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.2, lr: 8.684e-04, size: 768, ETA: 9 days, 22:22:08 2022-05-20 18:08:43.514 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3220/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 8.711e-04, size: 480, ETA: 9 days, 22:12:05 2022-05-20 18:08:46.076 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3230/7393, mem: 8935Mb, iter_time: 0.254s, data_time: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3270/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.003s, total_loss: 7.0, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 8.846e-04, size: 352, ETA: 9 days, 21:29:10 2022-05-20 18:09:02.403 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3280/7393, mem: 8935Mb, iter_time: 0.323s, data_time: 0.003s, total_loss: 8.2, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 8.873e-04, size: 480, ETA: 9 days, 21:22:02 2022-05-20 18:09:04.530 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3290/7393, mem: 8935Mb, iter_time: 0.212s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 8.900e-04, size: 320, ETA: 9 days, 21:02:25 2022-05-20 18:09:07.390 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3300/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.003s, total_loss: 8.8, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3560/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 9.631e-04, size: 416, ETA: 9 days, 20:40:23 2022-05-20 18:10:49.358 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3570/7393, mem: 8935Mb, iter_time: 0.247s, data_time: 0.007s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 9.658e-04, size: 320, ETA: 9 days, 20:26:08 2022-05-20 18:10:53.927 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3580/7393, mem: 8935Mb, iter_time: 0.454s, data_time: 0.003s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 9.685e-04, size: 640, ETA: 9 days, 20:33:15 2022-05-20 18:10:57.484 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3590/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 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loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.001e-03, size: 352, ETA: 9 days, 20:17:02 2022-05-20 18:11:41.256 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3710/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.004s, total_loss: 7.0, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.004e-03, size: 320, ETA: 9 days, 20:04:49 2022-05-20 18:11:46.581 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3720/7393, mem: 8935Mb, iter_time: 0.531s, data_time: 0.006s, total_loss: 8.0, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.6, lr: 1.006e-03, size: 672, ETA: 9 days, 20:19:23 2022-05-20 18:11:49.277 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3730/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.009e-03, size: 448, ETA: 9 days, 20:07:54 2022-05-20 18:11:55.378 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3740/7393, mem: 8935Mb, iter_time: 0.609s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.2, lr: 1.012e-03, size: 768, ETA: 9 days, 20:30:05 2022-05-20 18:11:58.452 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3750/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.014e-03, size: 512, ETA: 9 days, 20:22:22 2022-05-20 18:12:02.310 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3760/7393, mem: 8935Mb, iter_time: 0.383s, data_time: 0.005s, total_loss: 7.0, loss_cls: 5.9, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.017e-03, size: 576, ETA: 9 days, 20:22:12 2022-05-20 18:12:05.088 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3770/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.002s, total_loss: 8.2, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.020e-03, size: 448, ETA: 9 days, 20:11:37 2022-05-20 18:12:11.097 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3780/7393, mem: 8935Mb, iter_time: 0.600s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.023e-03, size: 768, ETA: 9 days, 20:32:40 2022-05-20 18:12:15.926 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3790/7393, mem: 8935Mb, iter_time: 0.482s, data_time: 0.001s, total_loss: 7.8, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.025e-03, size: 672, ETA: 9 days, 20:42:08 2022-05-20 18:12:21.864 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3800/7393, mem: 8935Mb, iter_time: 0.593s, data_time: 0.001s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.028e-03, size: 768, ETA: 9 days, 21:02:18 2022-05-20 18:12:24.921 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3810/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 1.031e-03, size: 512, ETA: 9 days, 20:54:28 2022-05-20 18:12:28.768 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3820/7393, mem: 8935Mb, iter_time: 0.384s, data_time: 0.002s, total_loss: 6.2, loss_cls: 4.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.033e-03, size: 576, ETA: 9 days, 20:54:17 2022-05-20 18:12:32.230 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3830/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.036e-03, size: 544, ETA: 9 days, 20:50:24 2022-05-20 18:12:38.356 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3840/7393, mem: 8935Mb, iter_time: 0.612s, data_time: 0.002s, total_loss: 9.2, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.039e-03, size: 768, ETA: 9 days, 21:12:07 2022-05-20 18:12:42.712 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3850/7393, mem: 8935Mb, iter_time: 0.435s, data_time: 0.001s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.042e-03, size: 640, ETA: 9 days, 21:16:48 2022-05-20 18:12:45.886 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3860/7393, mem: 8935Mb, iter_time: 0.317s, data_time: 0.002s, total_loss: 6.9, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.044e-03, size: 512, ETA: 9 days, 21:10:07 2022-05-20 18:12:51.228 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3870/7393, mem: 8935Mb, iter_time: 0.534s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.047e-03, size: 704, ETA: 9 days, 21:24:09 2022-05-20 18:12:56.808 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 3880/7393, mem: 8935Mb, iter_time: 0.558s, data_time: 0.001s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4140/7393, mem: 8935Mb, iter_time: 0.406s, data_time: 0.003s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.120e-03, size: 576, ETA: 9 days, 21:02:14 2022-05-20 18:14:38.684 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4150/7393, mem: 8935Mb, iter_time: 0.529s, data_time: 0.002s, total_loss: 11.8, loss_cls: 10.2, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 1.123e-03, size: 704, ETA: 9 days, 21:14:55 2022-05-20 18:14:43.907 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4160/7393, mem: 8935Mb, iter_time: 0.522s, data_time: 0.003s, total_loss: 8.4, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 1.125e-03, size: 704, ETA: 9 days, 21:26:55 2022-05-20 18:14:47.345 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4170/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 0.003s, total_loss: 7.6, loss_cls: 6.1, loss_iou: 0.4, 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4210/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.139e-03, size: 544, ETA: 9 days, 21:33:40 2022-05-20 18:15:09.432 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4220/7393, mem: 8935Mb, iter_time: 0.536s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.142e-03, size: 704, ETA: 9 days, 21:46:40 2022-05-20 18:15:15.382 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4230/7393, mem: 8935Mb, iter_time: 0.595s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.144e-03, size: 768, ETA: 9 days, 22:04:44 2022-05-20 18:15:18.416 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4240/7393, mem: 8935Mb, iter_time: 0.303s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.147e-03, size: 512, ETA: 9 days, 21:57:20 2022-05-20 18:15:23.339 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4250/7393, mem: 8935Mb, iter_time: 0.492s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.150e-03, size: 672, ETA: 9 days, 22:06:20 2022-05-20 18:15:28.124 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4260/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.002s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.152e-03, size: 672, ETA: 9 days, 22:14:08 2022-05-20 18:15:31.914 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4270/7393, mem: 8935Mb, iter_time: 0.378s, data_time: 0.003s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.155e-03, size: 576, ETA: 9 days, 22:13:18 2022-05-20 18:15:37.493 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4280/7393, mem: 8935Mb, iter_time: 0.557s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.158e-03, size: 736, ETA: 9 days, 22:27:53 2022-05-20 18:15:42.915 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4290/7393, mem: 8935Mb, iter_time: 0.541s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.161e-03, size: 672, ETA: 9 days, 22:41:02 2022-05-20 18:15:46.188 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4300/7393, mem: 8935Mb, iter_time: 0.327s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.163e-03, size: 512, ETA: 9 days, 22:35:40 2022-05-20 18:15:49.894 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4310/7393, mem: 8935Mb, iter_time: 0.370s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.166e-03, size: 544, ETA: 9 days, 22:34:02 2022-05-20 18:15:53.636 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4320/7393, mem: 8935Mb, iter_time: 0.374s, data_time: 0.004s, total_loss: 7.8, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 1.169e-03, size: 544, ETA: 9 days, 22:32:44 2022-05-20 18:15:57.159 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4330/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.171e-03, size: 544, ETA: 9 days, 22:29:34 2022-05-20 18:16:01.360 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4340/7393, mem: 8935Mb, iter_time: 0.419s, data_time: 0.003s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.174e-03, size: 576, ETA: 9 days, 22:32:09 2022-05-20 18:16:05.799 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4350/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.002s, total_loss: 7.0, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 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loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.207e-03, size: 704, ETA: 9 days, 22:44:13 2022-05-20 18:16:51.654 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4470/7393, mem: 8935Mb, iter_time: 0.206s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.209e-03, size: 384, ETA: 9 days, 22:29:04 2022-05-20 18:16:56.919 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4480/7393, mem: 8935Mb, iter_time: 0.525s, data_time: 0.007s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.212e-03, size: 448, ETA: 9 days, 22:40:17 2022-05-20 18:17:00.086 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4490/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.005s, total_loss: 8.7, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.215e-03, size: 352, ETA: 9 days, 22:34:16 2022-05-20 18:17:05.788 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4500/7393, mem: 8935Mb, iter_time: 0.569s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.3, lr: 1.217e-03, size: 704, ETA: 9 days, 22:49:04 2022-05-20 18:17:08.656 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4510/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.220e-03, size: 480, ETA: 9 days, 22:40:38 2022-05-20 18:17:15.974 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4520/7393, mem: 8935Mb, iter_time: 0.727s, data_time: 0.025s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.223e-03, size: 576, ETA: 9 days, 23:08:13 2022-05-20 18:17:17.974 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4530/7393, mem: 8935Mb, iter_time: 0.199s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 1.225e-03, size: 352, ETA: 9 days, 22:52:42 2022-05-20 18:17:21.239 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4540/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.002s, total_loss: 7.0, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.228e-03, size: 416, ETA: 9 days, 22:47:31 2022-05-20 18:17:23.685 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4550/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.005s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.231e-03, size: 320, ETA: 9 days, 22:35:43 2022-05-20 18:17:27.647 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4560/7393, mem: 8935Mb, iter_time: 0.395s, data_time: 0.005s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.234e-03, size: 384, ETA: 9 days, 22:36:12 2022-05-20 18:17:31.956 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4570/7393, mem: 8935Mb, iter_time: 0.430s, data_time: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4610/7393, mem: 8935Mb, iter_time: 0.337s, data_time: 0.003s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 1.247e-03, size: 480, ETA: 9 days, 22:21:44 2022-05-20 18:17:51.099 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4620/7393, mem: 8935Mb, iter_time: 0.575s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.250e-03, size: 736, ETA: 9 days, 22:36:38 2022-05-20 18:17:56.674 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4630/7393, mem: 8935Mb, iter_time: 0.557s, data_time: 0.001s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.1, lr: 1.253e-03, size: 736, ETA: 9 days, 22:50:02 2022-05-20 18:17:58.949 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4640/7393, mem: 8935Mb, iter_time: 0.227s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 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2022-05-20 18:18:27.414 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4720/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.277e-03, size: 448, ETA: 9 days, 22:15:42 2022-05-20 18:18:32.686 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4730/7393, mem: 8935Mb, iter_time: 0.527s, data_time: 0.004s, total_loss: 6.9, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.280e-03, size: 704, ETA: 9 days, 22:26:29 2022-05-20 18:18:35.337 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4740/7393, mem: 8935Mb, iter_time: 0.264s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 1.282e-03, size: 416, ETA: 9 days, 22:16:46 2022-05-20 18:18:38.186 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4750/7393, mem: 8935Mb, iter_time: 0.283s, data_time: 0.005s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.285e-03, size: 416, ETA: 9 days, 22:08:36 2022-05-20 18:18:40.643 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4760/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.005s, total_loss: 7.3, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 1.288e-03, size: 352, ETA: 9 days, 21:57:13 2022-05-20 18:18:46.005 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4770/7393, mem: 8935Mb, iter_time: 0.536s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.290e-03, size: 704, ETA: 9 days, 22:08:38 2022-05-20 18:18:50.839 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4780/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.293e-03, size: 672, ETA: 9 days, 22:15:56 2022-05-20 18:18:54.270 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4790/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.296e-03, size: 544, ETA: 9 days, 22:12:24 2022-05-20 18:18:56.411 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4800/7393, mem: 8935Mb, iter_time: 0.213s, data_time: 0.004s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.299e-03, size: 384, ETA: 9 days, 21:58:56 2022-05-20 18:19:02.390 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4810/7393, mem: 8935Mb, iter_time: 0.597s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.301e-03, size: 640, ETA: 9 days, 22:14:57 2022-05-20 18:19:04.959 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4820/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.304e-03, size: 416, ETA: 9 days, 22:04:50 2022-05-20 18:19:11.010 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4830/7393, mem: 8935Mb, iter_time: 0.605s, data_time: 0.001s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.2, lr: 1.307e-03, size: 768, ETA: 9 days, 22:21:22 2022-05-20 18:19:14.752 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4840/7393, mem: 8935Mb, iter_time: 0.374s, data_time: 0.002s, total_loss: 7.0, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.309e-03, size: 576, ETA: 9 days, 22:20:13 2022-05-20 18:19:18.513 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4850/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.5, lr: 1.312e-03, size: 576, ETA: 9 days, 22:19:13 2022-05-20 18:19:24.474 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4860/7393, mem: 8935Mb, iter_time: 0.596s, data_time: 0.001s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.5, lr: 1.315e-03, size: 768, ETA: 9 days, 22:34:55 2022-05-20 18:19:27.568 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4870/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.317e-03, size: 512, ETA: 9 days, 22:28:51 2022-05-20 18:19:31.161 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4880/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.5, lr: 1.320e-03, size: 544, ETA: 9 days, 22:26:34 2022-05-20 18:19:35.281 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4890/7393, mem: 8935Mb, iter_time: 0.411s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.323e-03, size: 608, ETA: 9 days, 22:28:16 2022-05-20 18:19:37.259 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4900/7393, mem: 8935Mb, iter_time: 0.197s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 1.326e-03, size: 352, ETA: 9 days, 22:13:50 2022-05-20 18:19:40.880 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4910/7393, mem: 8935Mb, iter_time: 0.361s, data_time: 0.007s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.328e-03, size: 512, ETA: 9 days, 22:11:46 2022-05-20 18:19:47.015 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4920/7393, mem: 8935Mb, iter_time: 0.611s, data_time: 0.006s, total_loss: 10.0, loss_cls: 8.9, loss_iou: 0.2, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.331e-03, size: 608, ETA: 9 days, 22:28:27 2022-05-20 18:19:48.789 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4930/7393, mem: 8935Mb, iter_time: 0.177s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.339s, data_time: 0.003s, total_loss: 8.1, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.345e-03, size: 448, ETA: 9 days, 21:40:50 2022-05-20 18:20:05.340 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4980/7393, mem: 8935Mb, iter_time: 0.518s, data_time: 0.005s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.347e-03, size: 512, ETA: 9 days, 21:50:31 2022-05-20 18:20:08.538 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 4990/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 7.0, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.350e-03, size: 480, ETA: 9 days, 21:45:26 2022-05-20 18:20:10.653 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5000/7393, mem: 8935Mb, iter_time: 0.211s, data_time: 0.003s, total_loss: 7.3, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 1.353e-03, size: 352, ETA: 9 days, 21:32:23 2022-05-20 18:20:16.013 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5010/7393, mem: 8935Mb, iter_time: 0.535s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.355e-03, size: 704, ETA: 9 days, 21:43:17 2022-05-20 18:20:19.772 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5020/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.358e-03, size: 576, ETA: 9 days, 21:42:23 2022-05-20 18:20:24.217 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5030/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 1.361e-03, size: 640, ETA: 9 days, 21:46:31 2022-05-20 18:20:29.806 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5040/7393, mem: 8935Mb, iter_time: 0.559s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.363e-03, size: 736, ETA: 9 days, 21:59:01 2022-05-20 18:20:32.728 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5050/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.366e-03, size: 480, ETA: 9 days, 21:51:57 2022-05-20 18:20:35.313 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5060/7393, mem: 8935Mb, iter_time: 0.258s, data_time: 0.004s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.369e-03, size: 416, ETA: 9 days, 21:42:27 2022-05-20 18:20:40.277 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5070/7393, mem: 8935Mb, iter_time: 0.495s, data_time: 0.004s, total_loss: 9.3, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.372e-03, size: 672, ETA: 9 days, 21:50:18 2022-05-20 18:20:42.894 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5080/7393, mem: 8935Mb, iter_time: 0.261s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.374e-03, size: 448, ETA: 9 days, 21:41:04 2022-05-20 18:20:47.450 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5090/7393, mem: 8935Mb, iter_time: 0.455s, data_time: 0.002s, total_loss: 6.7, loss_cls: 5.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.377e-03, size: 640, ETA: 9 days, 21:45:57 2022-05-20 18:20:52.286 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5100/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 1.380e-03, size: 672, ETA: 9 days, 21:52:51 2022-05-20 18:20:56.654 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5110/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.382e-03, 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5190/7393, mem: 8935Mb, iter_time: 0.214s, data_time: 0.005s, total_loss: 7.8, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.404e-03, size: 384, ETA: 9 days, 21:56:22 2022-05-20 18:21:30.568 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5200/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.008s, total_loss: 8.2, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 1.407e-03, size: 352, ETA: 9 days, 21:48:36 2022-05-20 18:21:33.566 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5210/7393, mem: 8935Mb, iter_time: 0.299s, data_time: 0.004s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.409e-03, size: 416, ETA: 9 days, 21:42:18 2022-05-20 18:21:38.506 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5220/7393, mem: 8935Mb, iter_time: 0.493s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5480/7393, mem: 8935Mb, iter_time: 0.263s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.482e-03, size: 448, ETA: 9 days, 20:23:22 2022-05-20 18:23:12.074 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5490/7393, mem: 8935Mb, iter_time: 0.523s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.485e-03, size: 704, ETA: 9 days, 20:32:38 2022-05-20 18:23:17.657 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5500/7393, mem: 8935Mb, iter_time: 0.558s, data_time: 0.001s, total_loss: 8.2, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 1.488e-03, size: 736, ETA: 9 days, 20:44:09 2022-05-20 18:23:19.984 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5510/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5770/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.005s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.561e-03, size: 384, ETA: 9 days, 19:40:19 2022-05-20 18:24:55.934 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5780/7393, mem: 8935Mb, iter_time: 0.372s, data_time: 0.005s, total_loss: 8.0, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 1.0, lr: 1.564e-03, size: 544, ETA: 9 days, 19:39:32 2022-05-20 18:25:00.376 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5790/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 1.566e-03, size: 640, ETA: 9 days, 19:43:18 2022-05-20 18:25:02.768 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5800/7393, mem: 8935Mb, iter_time: 0.239s, data_time: 0.002s, total_loss: 8.2, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 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epoch: 1/300, iter: 5950/7393, mem: 8935Mb, iter_time: 0.175s, data_time: 0.001s, total_loss: 7.2, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 1.610e-03, size: 352, ETA: 9 days, 19:52:46 2022-05-20 18:26:07.439 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5960/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.004s, total_loss: 7.6, loss_cls: 6.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 1.612e-03, size: 544, ETA: 9 days, 19:52:51 2022-05-20 18:26:11.027 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5970/7393, mem: 8935Mb, iter_time: 0.356s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.615e-03, size: 544, ETA: 9 days, 19:51:06 2022-05-20 18:26:13.920 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 5980/7393, mem: 8935Mb, iter_time: 0.288s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 1.618e-03, 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6060/7393, mem: 8935Mb, iter_time: 0.229s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.639e-03, size: 416, ETA: 9 days, 20:12:15 2022-05-20 18:26:55.205 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6070/7393, mem: 8935Mb, iter_time: 0.600s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.642e-03, size: 768, ETA: 9 days, 20:25:15 2022-05-20 18:26:58.273 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6080/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.645e-03, size: 512, ETA: 9 days, 20:20:25 2022-05-20 18:27:03.865 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6090/7393, mem: 8935Mb, iter_time: 0.559s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 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epoch: 1/300, iter: 6240/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.001s, total_loss: 6.6, loss_cls: 5.2, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 1.688e-03, size: 576, ETA: 9 days, 20:18:41 2022-05-20 18:28:02.134 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6250/7393, mem: 8935Mb, iter_time: 0.229s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 1.691e-03, size: 352, ETA: 9 days, 20:09:26 2022-05-20 18:28:07.088 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6260/7393, mem: 8935Mb, iter_time: 0.494s, data_time: 0.003s, total_loss: 7.5, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.693e-03, size: 672, ETA: 9 days, 20:15:50 2022-05-20 18:28:13.025 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6270/7393, mem: 8935Mb, iter_time: 0.593s, data_time: 0.001s, total_loss: 10.9, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 1.696e-03, size: 768, ETA: 9 days, 20:28:02 2022-05-20 18:28:15.730 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6280/7393, mem: 8935Mb, iter_time: 0.270s, data_time: 0.003s, total_loss: 8.7, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 1.699e-03, size: 448, ETA: 9 days, 20:21:13 2022-05-20 18:28:19.484 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6290/7393, mem: 8935Mb, iter_time: 0.374s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.702e-03, size: 448, ETA: 9 days, 20:20:32 2022-05-20 18:28:24.415 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6300/7393, mem: 8935Mb, iter_time: 0.493s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 1.704e-03, size: 672, ETA: 9 days, 20:26:47 2022-05-20 18:28:28.134 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6310/7393, mem: 8935Mb, iter_time: 0.371s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 1.707e-03, size: 480, ETA: 9 days, 20:25:55 2022-05-20 18:28:34.170 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6320/7393, mem: 8935Mb, iter_time: 0.603s, data_time: 0.001s, total_loss: 8.4, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.710e-03, size: 768, ETA: 9 days, 20:38:34 2022-05-20 18:28:38.252 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6330/7393, mem: 8935Mb, iter_time: 0.408s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 1.712e-03, size: 608, ETA: 9 days, 20:39:49 2022-05-20 18:28:43.467 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6340/7393, mem: 8935Mb, iter_time: 0.521s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 1.715e-03, size: 704, ETA: 9 days, 20:47:38 2022-05-20 18:28:49.055 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6350/7393, mem: 8935Mb, iter_time: 0.558s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.3, lr: 1.718e-03, size: 736, ETA: 9 days, 20:57:37 2022-05-20 18:28:51.174 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6360/7393, mem: 8935Mb, iter_time: 0.211s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.3, lr: 1.721e-03, size: 384, ETA: 9 days, 20:47:26 2022-05-20 18:28:57.349 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6370/7393, mem: 8935Mb, iter_time: 0.617s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 1.723e-03, size: 768, ETA: 9 days, 21:00:45 2022-05-20 18:28:59.673 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6380/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.002s, total_loss: 9.4, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 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2022-05-20 18:29:29.106 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6460/7393, mem: 8935Mb, iter_time: 0.577s, data_time: 0.003s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.748e-03, size: 736, ETA: 9 days, 20:42:48 2022-05-20 18:29:30.842 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6470/7393, mem: 8935Mb, iter_time: 0.173s, data_time: 0.004s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.750e-03, size: 320, ETA: 9 days, 20:30:38 2022-05-20 18:29:36.979 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6480/7393, mem: 8935Mb, iter_time: 0.613s, data_time: 0.003s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.753e-03, size: 768, ETA: 9 days, 20:43:32 2022-05-20 18:29:41.363 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6490/7393, mem: 8935Mb, iter_time: 0.438s, data_time: 0.002s, total_loss: 8.4, 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epoch: 1/300, iter: 6530/7393, mem: 8935Mb, iter_time: 0.266s, data_time: 0.005s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 1.767e-03, size: 352, ETA: 9 days, 20:13:16 2022-05-20 18:29:53.610 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6540/7393, mem: 8935Mb, iter_time: 0.261s, data_time: 0.003s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 1.3, lr: 1.769e-03, size: 384, ETA: 9 days, 20:06:13 2022-05-20 18:29:56.209 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6550/7393, mem: 8935Mb, iter_time: 0.258s, data_time: 0.005s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.772e-03, size: 384, ETA: 9 days, 19:59:03 2022-05-20 18:30:00.452 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6560/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.007s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.775e-03, 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yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6640/7393, mem: 8935Mb, iter_time: 0.338s, data_time: 0.008s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.796e-03, size: 480, ETA: 9 days, 19:44:27 2022-05-20 18:30:31.830 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6650/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.799e-03, size: 512, ETA: 9 days, 19:42:21 2022-05-20 18:30:35.596 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6660/7393, mem: 8935Mb, iter_time: 0.376s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 1.802e-03, size: 544, ETA: 9 days, 19:41:51 2022-05-20 18:30:39.458 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6670/7393, mem: 8935Mb, iter_time: 0.384s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.2, loss_iou: 0.4, loss_dfl: 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2022-05-20 18:31:10.776 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6750/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.9, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.826e-03, size: 576, ETA: 9 days, 19:44:21 2022-05-20 18:31:14.100 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6760/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.829e-03, size: 512, ETA: 9 days, 19:41:26 2022-05-20 18:31:18.263 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6770/7393, mem: 8935Mb, iter_time: 0.416s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.831e-03, size: 608, ETA: 9 days, 19:43:06 2022-05-20 18:31:22.362 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6780/7393, mem: 8935Mb, iter_time: 0.409s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.834e-03, size: 608, ETA: 9 days, 19:44:25 2022-05-20 18:31:26.250 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6790/7393, mem: 8935Mb, iter_time: 0.388s, data_time: 0.003s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 1.837e-03, size: 576, ETA: 9 days, 19:44:36 2022-05-20 18:31:28.294 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6800/7393, mem: 8935Mb, iter_time: 0.203s, data_time: 0.003s, total_loss: 8.1, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.840e-03, size: 352, ETA: 9 days, 19:34:45 2022-05-20 18:31:33.303 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6810/7393, mem: 8935Mb, iter_time: 0.500s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.842e-03, size: 672, ETA: 9 days, 19:41:00 2022-05-20 18:31:37.096 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6820/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 1.845e-03, size: 576, ETA: 9 days, 19:40:40 2022-05-20 18:31:41.399 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6830/7393, mem: 8935Mb, iter_time: 0.430s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 1.848e-03, size: 608, ETA: 9 days, 19:43:04 2022-05-20 18:31:43.998 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6840/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.850e-03, size: 448, ETA: 9 days, 19:36:17 2022-05-20 18:31:46.250 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6850/7393, mem: 8935Mb, iter_time: 0.224s, data_time: 0.003s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.853e-03, size: 352, ETA: 9 days, 19:27:39 2022-05-20 18:31:51.268 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6860/7393, mem: 8935Mb, iter_time: 0.501s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.856e-03, size: 672, ETA: 9 days, 19:33:54 2022-05-20 18:31:57.255 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6870/7393, mem: 8935Mb, iter_time: 0.598s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 1.859e-03, size: 768, ETA: 9 days, 19:45:21 2022-05-20 18:32:01.160 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6880/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.002s, total_loss: 9.3, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.861e-03, size: 576, ETA: 9 days, 19:45:37 2022-05-20 18:32:03.078 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6890/7393, mem: 8935Mb, iter_time: 0.190s, data_time: 0.005s, total_loss: 8.8, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.864e-03, size: 320, ETA: 9 days, 19:35:12 2022-05-20 18:32:07.603 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6900/7393, mem: 8935Mb, iter_time: 0.452s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.867e-03, size: 640, ETA: 9 days, 19:38:46 2022-05-20 18:32:12.434 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6910/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.2, lr: 1.869e-03, size: 672, ETA: 9 days, 19:43:58 2022-05-20 18:32:16.620 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6920/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.872e-03, size: 608, ETA: 9 days, 19:45:43 2022-05-20 18:32:19.074 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6930/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.875e-03, size: 416, ETA: 9 days, 19:38:15 2022-05-20 18:32:21.405 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6940/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.004s, total_loss: 7.5, loss_cls: 5.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.877e-03, size: 320, ETA: 9 days, 19:30:07 2022-05-20 18:32:23.349 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6950/7393, mem: 8935Mb, iter_time: 0.193s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.880e-03, size: 320, ETA: 9 days, 19:19:58 2022-05-20 18:32:29.841 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 6960/7393, mem: 8935Mb, iter_time: 0.648s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.9, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.304s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.6, lr: 1.894e-03, size: 512, ETA: 9 days, 19:33:59 2022-05-20 18:32:48.748 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7010/7393, mem: 8935Mb, iter_time: 0.348s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.896e-03, size: 544, ETA: 9 days, 19:32:02 2022-05-20 18:32:51.142 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7020/7393, mem: 8935Mb, iter_time: 0.238s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.3, lr: 1.899e-03, size: 384, ETA: 9 days, 19:24:20 2022-05-20 18:32:57.042 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7030/7393, mem: 8935Mb, iter_time: 0.589s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 1.902e-03, size: 736, ETA: 9 days, 19:35:02 2022-05-20 18:33:02.987 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7040/7393, mem: 8935Mb, iter_time: 0.594s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 1.905e-03, size: 768, ETA: 9 days, 19:45:59 2022-05-20 18:33:07.770 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7050/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.001s, total_loss: 8.1, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.907e-03, size: 672, ETA: 9 days, 19:50:49 2022-05-20 18:33:13.807 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7060/7393, mem: 8935Mb, iter_time: 0.603s, data_time: 0.001s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.910e-03, size: 768, ETA: 9 days, 20:02:12 2022-05-20 18:33:17.201 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7070/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 1.913e-03, size: 544, ETA: 9 days, 19:59:46 2022-05-20 18:33:19.383 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7080/7393, mem: 8935Mb, iter_time: 0.217s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.915e-03, size: 384, ETA: 9 days, 19:51:01 2022-05-20 18:33:21.804 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7090/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.918e-03, size: 384, ETA: 9 days, 19:43:32 2022-05-20 18:33:26.398 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7100/7393, mem: 8935Mb, iter_time: 0.459s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.921e-03, size: 640, ETA: 9 days, 19:47:20 2022-05-20 18:33:31.660 | INFO | yolox.core.trainer:after_iter:273 - 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data_time: 0.002s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.942e-03, size: 608, ETA: 9 days, 20:06:54 2022-05-20 18:34:03.190 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7190/7393, mem: 8935Mb, iter_time: 0.210s, data_time: 0.004s, total_loss: 7.5, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.945e-03, size: 352, ETA: 9 days, 19:57:54 2022-05-20 18:34:05.617 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7200/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.006s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 1.948e-03, size: 384, ETA: 9 days, 19:50:32 2022-05-20 18:34:09.013 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7210/7393, mem: 8935Mb, iter_time: 0.337s, data_time: 0.005s, total_loss: 8.2, loss_cls: 7.1, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.8, lr: 1.950e-03, size: 416, ETA: 9 days, 19:48:05 2022-05-20 18:34:11.844 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7220/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.003s, total_loss: 6.6, loss_cls: 5.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 1.953e-03, size: 448, ETA: 9 days, 19:42:50 2022-05-20 18:34:14.959 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7230/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.003s, total_loss: 8.8, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.956e-03, size: 480, ETA: 9 days, 19:39:02 2022-05-20 18:34:19.947 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7240/7393, mem: 8935Mb, iter_time: 0.498s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.959e-03, size: 672, ETA: 9 days, 19:44:47 2022-05-20 18:34:23.050 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7250/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.002s, total_loss: 7.1, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.961e-03, size: 512, ETA: 9 days, 19:40:57 2022-05-20 18:34:26.247 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7260/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.964e-03, size: 512, ETA: 9 days, 19:37:36 2022-05-20 18:34:28.918 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7270/7393, mem: 8935Mb, iter_time: 0.265s, data_time: 0.003s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 1.967e-03, size: 320, ETA: 9 days, 19:31:32 2022-05-20 18:34:34.273 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7280/7393, mem: 8935Mb, iter_time: 0.535s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 1.969e-03, size: 704, ETA: 9 days, 19:39:07 2022-05-20 18:34:35.999 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7290/7393, mem: 8935Mb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.972e-03, size: 320, ETA: 9 days, 19:28:21 2022-05-20 18:34:42.051 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7300/7393, mem: 8935Mb, iter_time: 0.604s, data_time: 0.004s, total_loss: 9.0, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.975e-03, size: 768, ETA: 9 days, 19:39:26 2022-05-20 18:34:47.233 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7310/7393, mem: 8935Mb, iter_time: 0.518s, data_time: 0.001s, total_loss: 9.1, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.978e-03, size: 704, ETA: 9 days, 19:46:07 2022-05-20 18:34:49.569 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7320/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.980e-03, size: 416, ETA: 9 days, 19:38:27 2022-05-20 18:34:53.565 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7330/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 1.983e-03, size: 544, ETA: 9 days, 19:39:06 2022-05-20 18:34:57.136 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7340/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.014s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 1.986e-03, size: 320, ETA: 9 days, 19:37:12 2022-05-20 18:35:02.798 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7350/7393, mem: 8935Mb, iter_time: 0.566s, data_time: 0.003s, total_loss: 7.0, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 1.988e-03, size: 736, ETA: 9 days, 19:46:15 2022-05-20 18:35:05.355 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7360/7393, mem: 8935Mb, iter_time: 0.255s, data_time: 0.002s, total_loss: 6.9, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 1.991e-03, size: 448, ETA: 9 days, 19:39:44 2022-05-20 18:35:10.647 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7370/7393, mem: 8935Mb, iter_time: 0.528s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.6, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 1.2, lr: 1.994e-03, size: 704, ETA: 9 days, 19:46:54 2022-05-20 18:35:14.596 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7380/7393, mem: 8935Mb, iter_time: 0.394s, data_time: 0.158s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 1.996e-03, size: 416, ETA: 9 days, 19:47:21 2022-05-20 18:35:19.873 | INFO | yolox.core.trainer:after_iter:273 - epoch: 1/300, iter: 7390/7393, mem: 8935Mb, iter_time: 0.527s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 1.999e-03, size: 704, ETA: 9 days, 19:54:25 2022-05-20 18:35:20.563 | INFO | yolox.core.trainer:save_ckpt:364 - Save weights to ./YOLOX_outputs/ppyoloe_s_sigmoid 2022-05-20 18:35:20.720 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch2 2022-05-20 18:35:20.721 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 18:35:20.721 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 18:35:23.130 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 10/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.002s, total_loss: 6.8, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 2.003e-03, size: 352, ETA: 9 days, 19:44:51 2022-05-20 18:35:26.310 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 20/7393, mem: 8935Mb, iter_time: 0.317s, data_time: 0.004s, total_loss: 8.3, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 2.4, lr: 2.005e-03, size: 640, ETA: 9 days, 19:41:27 2022-05-20 18:35:30.977 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 30/7393, mem: 8935Mb, iter_time: 0.466s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 2.008e-03, size: 704, ETA: 9 days, 19:45:29 2022-05-20 18:35:36.444 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 40/7393, mem: 8935Mb, iter_time: 0.546s, data_time: 0.001s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.011e-03, size: 768, ETA: 9 days, 19:53:27 2022-05-20 18:35:42.797 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 50/7393, mem: 8935Mb, iter_time: 0.634s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.014e-03, size: 768, ETA: 9 days, 20:05:47 2022-05-20 18:35:48.476 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 60/7393, mem: 8935Mb, iter_time: 0.567s, data_time: 0.001s, total_loss: 7.5, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.016e-03, size: 672, ETA: 9 days, 20:14:45 2022-05-20 18:35:52.909 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 70/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.6, lr: 2.019e-03, size: 544, ETA: 9 days, 20:17:33 2022-05-20 18:35:56.399 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 80/7393, mem: 8935Mb, iter_time: 0.348s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.022e-03, size: 544, ETA: 9 days, 20:15:41 2022-05-20 18:35:59.907 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 90/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.024e-03, size: 544, ETA: 9 days, 20:13:55 2022-05-20 18:36:04.405 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 100/7393, mem: 8935Mb, iter_time: 0.449s, data_time: 0.002s, total_loss: 6.9, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.027e-03, size: 672, ETA: 9 days, 20:17:01 2022-05-20 18:36:08.676 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 110/7393, mem: 8935Mb, iter_time: 0.427s, data_time: 0.001s, total_loss: 8.4, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 2.030e-03, size: 480, ETA: 9 days, 20:19:00 2022-05-20 18:36:11.181 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 120/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.5, lr: 2.032e-03, size: 320, ETA: 9 days, 20:12:18 2022-05-20 18:36:14.544 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 130/7393, mem: 8935Mb, iter_time: 0.335s, data_time: 0.002s, total_loss: 7.3, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.035e-03, size: 768, ETA: 9 days, 20:09:49 2022-05-20 18:36:19.434 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 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20:01:56 2022-05-20 18:36:33.355 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 180/7393, mem: 8935Mb, iter_time: 0.496s, data_time: 0.001s, total_loss: 9.4, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.049e-03, size: 640, ETA: 9 days, 20:07:17 2022-05-20 18:36:37.857 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 190/7393, mem: 8935Mb, iter_time: 0.450s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 2.051e-03, size: 640, ETA: 9 days, 20:10:22 2022-05-20 18:36:41.556 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 200/7393, mem: 8935Mb, iter_time: 0.369s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.054e-03, size: 384, ETA: 9 days, 20:09:33 2022-05-20 18:36:43.964 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 210/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.003s, total_loss: 7.8, loss_cls: 6.3, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.057e-03, size: 448, ETA: 9 days, 20:02:29 2022-05-20 18:36:47.379 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 220/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.005s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.060e-03, size: 640, ETA: 9 days, 20:00:18 2022-05-20 18:36:52.018 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 230/7393, mem: 8935Mb, iter_time: 0.463s, data_time: 0.003s, total_loss: 11.5, loss_cls: 10.0, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.1, lr: 2.062e-03, size: 704, ETA: 9 days, 20:04:03 2022-05-20 18:36:57.163 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 240/7393, mem: 8935Mb, iter_time: 0.514s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.1, lr: 2.065e-03, size: 704, ETA: 9 days, 20:10:14 2022-05-20 18:37:02.265 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 250/7393, mem: 8935Mb, iter_time: 0.510s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.068e-03, size: 672, ETA: 9 days, 20:16:12 2022-05-20 18:37:07.382 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 260/7393, mem: 8935Mb, iter_time: 0.511s, data_time: 0.002s, total_loss: 10.3, loss_cls: 9.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.070e-03, size: 672, ETA: 9 days, 20:22:13 2022-05-20 18:37:12.983 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 270/7393, mem: 8935Mb, iter_time: 0.560s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.073e-03, size: 768, ETA: 9 days, 20:30:32 2022-05-20 18:37:19.111 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 280/7393, mem: 8935Mb, iter_time: 0.612s, data_time: 0.003s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 2.076e-03, size: 640, ETA: 9 days, 20:41:22 2022-05-20 18:37:22.661 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 290/7393, mem: 8935Mb, iter_time: 0.354s, data_time: 0.001s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.078e-03, size: 320, ETA: 9 days, 20:39:48 2022-05-20 18:37:24.635 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 300/7393, mem: 8935Mb, iter_time: 0.196s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.081e-03, size: 320, ETA: 9 days, 20:30:41 2022-05-20 18:37:27.928 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 310/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.005s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.084e-03, size: 352, ETA: 9 days, 20:27:49 2022-05-20 18:37:32.684 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 320/7393, mem: 8935Mb, iter_time: 0.475s, data_time: 0.005s, total_loss: 9.7, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.2, lr: 2.087e-03, size: 768, ETA: 9 days, 20:32:01 2022-05-20 18:37:37.703 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 330/7393, mem: 8935Mb, iter_time: 0.501s, data_time: 0.001s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.089e-03, size: 480, ETA: 9 days, 20:37:30 2022-05-20 18:37:41.255 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 340/7393, mem: 8935Mb, iter_time: 0.354s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.092e-03, size: 672, ETA: 9 days, 20:35:57 2022-05-20 18:37:46.402 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 350/7393, mem: 8935Mb, iter_time: 0.514s, data_time: 0.001s, total_loss: 8.0, loss_cls: 6.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.095e-03, size: 768, ETA: 9 days, 20:42:01 2022-05-20 18:37:51.472 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 360/7393, mem: 8935Mb, iter_time: 0.507s, data_time: 0.001s, total_loss: 7.7, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.097e-03, size: 480, ETA: 9 days, 20:47:42 2022-05-20 18:37:54.271 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 370/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.100e-03, size: 352, ETA: 9 days, 20:42:35 2022-05-20 18:37:57.300 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 380/7393, mem: 8935Mb, iter_time: 0.302s, data_time: 0.006s, total_loss: 8.4, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.103e-03, size: 640, ETA: 9 days, 20:38:33 2022-05-20 18:38:01.076 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 390/7393, mem: 8935Mb, iter_time: 0.377s, data_time: 0.002s, total_loss: 6.9, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.106e-03, size: 416, ETA: 9 days, 20:38:05 2022-05-20 18:38:07.352 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 400/7393, mem: 8935Mb, iter_time: 0.626s, data_time: 0.005s, total_loss: 7.2, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.108e-03, size: 448, ETA: 9 days, 20:49:24 2022-05-20 18:38:11.604 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 410/7393, mem: 8935Mb, iter_time: 0.424s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 2.111e-03, size: 576, ETA: 9 days, 20:51:10 2022-05-20 18:38:18.916 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 420/7393, mem: 8935Mb, iter_time: 0.730s, data_time: 0.005s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 2.114e-03, size: 608, ETA: 9 days, 21:07:19 2022-05-20 18:38:22.447 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 430/7393, mem: 8935Mb, iter_time: 0.353s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.116e-03, size: 416, ETA: 9 days, 21:05:40 2022-05-20 18:38:24.805 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 440/7393, mem: 8935Mb, iter_time: 0.235s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 2.119e-03, size: 320, ETA: 9 days, 20:58:29 2022-05-20 18:38:36.760 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 450/7393, mem: 8935Mb, iter_time: 1.186s, data_time: 0.017s, total_loss: 9.4, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.122e-03, size: 608, ETA: 9 days, 21:36:00 2022-05-20 18:38:41.254 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 460/7393, mem: 8935Mb, iter_time: 0.449s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.8, lr: 2.124e-03, size: 704, ETA: 9 days, 21:38:50 2022-05-20 18:38:45.716 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 470/7393, mem: 8935Mb, iter_time: 0.446s, data_time: 0.001s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.127e-03, size: 480, ETA: 9 days, 21:41:30 2022-05-20 18:38:48.988 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 480/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.004s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.130e-03, size: 544, ETA: 9 days, 21:38:36 2022-05-20 18:38:53.304 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 490/7393, mem: 8935Mb, iter_time: 0.430s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.133e-03, size: 768, ETA: 9 days, 21:40:32 2022-05-20 18:38:58.767 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 500/7393, mem: 8935Mb, iter_time: 0.546s, data_time: 0.001s, total_loss: 9.6, loss_cls: 8.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 2.3, lr: 2.135e-03, size: 640, ETA: 9 days, 21:47:52 2022-05-20 18:39:03.248 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 510/7393, mem: 8935Mb, iter_time: 0.447s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.4, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.138e-03, size: 640, ETA: 9 days, 21:50:37 2022-05-20 18:39:11.680 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 520/7393, mem: 8935Mb, iter_time: 0.841s, data_time: 0.005s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.141e-03, size: 416, ETA: 9 days, 22:11:41 2022-05-20 18:39:14.642 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 530/7393, mem: 8935Mb, iter_time: 0.295s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.143e-03, size: 480, ETA: 9 days, 22:07:18 2022-05-20 18:39:17.610 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 540/7393, mem: 8935Mb, iter_time: 0.296s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.146e-03, size: 480, ETA: 9 days, 22:02:57 2022-05-20 18:39:21.686 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 550/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.149e-03, size: 736, ETA: 9 days, 22:03:45 2022-05-20 18:39:26.347 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 560/7393, mem: 8935Mb, iter_time: 0.466s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.151e-03, size: 448, ETA: 9 days, 22:07:18 2022-05-20 18:39:28.949 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 570/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.003s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.154e-03, size: 448, ETA: 9 days, 22:01:18 2022-05-20 18:39:32.084 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 580/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.006s, total_loss: 7.4, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.157e-03, size: 576, ETA: 9 days, 21:57:45 2022-05-20 18:39:35.404 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 590/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.002s, total_loss: 6.7, loss_cls: 5.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.160e-03, size: 384, ETA: 9 days, 21:55:05 2022-05-20 18:39:38.451 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 600/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.007s, total_loss: 5.8, loss_cls: 4.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.162e-03, size: 544, ETA: 9 days, 21:51:09 2022-05-20 18:39:41.477 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 610/7393, mem: 8935Mb, iter_time: 0.302s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.165e-03, size: 320, ETA: 9 days, 21:47:08 2022-05-20 18:39:44.538 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 620/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.007s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.168e-03, size: 640, ETA: 9 days, 21:43:17 2022-05-20 18:39:48.486 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 630/7393, mem: 8935Mb, iter_time: 0.394s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.170e-03, size: 416, ETA: 9 days, 21:43:32 2022-05-20 18:39:56.976 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 640/7393, mem: 8935Mb, iter_time: 0.846s, data_time: 0.018s, total_loss: 8.2, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.173e-03, size: 480, ETA: 9 days, 22:04:29 2022-05-20 18:40:01.487 | INFO | yolox.core.trainer:after_iter:273 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yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 760/7393, mem: 8935Mb, iter_time: 0.324s, data_time: 0.002s, total_loss: 8.4, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 2.206e-03, size: 768, ETA: 9 days, 21:56:45 2022-05-20 18:40:47.351 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 770/7393, mem: 8935Mb, iter_time: 0.527s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 2.208e-03, size: 544, ETA: 9 days, 22:02:59 2022-05-20 18:40:50.502 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 780/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.211e-03, size: 448, ETA: 9 days, 21:59:36 2022-05-20 18:40:53.440 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 790/7393, mem: 8935Mb, iter_time: 0.293s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, 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iter_time: 0.337s, data_time: 0.005s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.225e-03, size: 704, ETA: 9 days, 21:39:23 2022-05-20 18:41:09.995 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 840/7393, mem: 8935Mb, iter_time: 0.449s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.227e-03, size: 480, ETA: 9 days, 21:42:06 2022-05-20 18:41:13.676 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 850/7393, mem: 8935Mb, iter_time: 0.367s, data_time: 0.003s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 2.230e-03, size: 704, ETA: 9 days, 21:41:09 2022-05-20 18:41:18.413 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 860/7393, mem: 8935Mb, iter_time: 0.473s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.233e-03, size: 576, ETA: 9 days, 21:44:55 2022-05-20 18:41:21.973 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 870/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.002s, total_loss: 6.6, loss_cls: 5.1, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.235e-03, size: 512, ETA: 9 days, 21:43:26 2022-05-20 18:41:25.402 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 880/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.238e-03, size: 608, ETA: 9 days, 21:41:22 2022-05-20 18:41:29.869 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 890/7393, mem: 8935Mb, iter_time: 0.446s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.241e-03, size: 672, ETA: 9 days, 21:43:55 2022-05-20 18:41:33.833 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 900/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.243e-03, size: 352, ETA: 9 days, 21:44:14 2022-05-20 18:41:36.378 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 910/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.005s, total_loss: 9.3, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.246e-03, size: 544, ETA: 9 days, 21:38:14 2022-05-20 18:41:39.463 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 920/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.249e-03, size: 352, ETA: 9 days, 21:34:39 2022-05-20 18:41:42.724 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 930/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.004s, total_loss: 8.0, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.252e-03, size: 672, ETA: 9 days, 21:31:51 2022-05-20 18:41:47.313 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 940/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.002s, total_loss: 7.0, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.254e-03, size: 544, ETA: 9 days, 21:34:56 2022-05-20 18:41:50.626 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 950/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.257e-03, size: 480, ETA: 9 days, 21:32:22 2022-05-20 18:41:53.667 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 960/7393, mem: 8935Mb, iter_time: 0.302s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 2.260e-03, size: 512, ETA: 9 days, 21:28:34 2022-05-20 18:41:56.981 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 970/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.262e-03, size: 512, ETA: 9 days, 21:26:01 2022-05-20 18:42:01.003 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 980/7393, mem: 8935Mb, iter_time: 0.402s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.265e-03, size: 768, ETA: 9 days, 21:26:36 2022-05-20 18:42:06.763 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 990/7393, mem: 8935Mb, iter_time: 0.576s, data_time: 0.001s, total_loss: 9.4, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.268e-03, size: 704, ETA: 9 days, 21:34:50 2022-05-20 18:42:12.162 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1000/7393, mem: 8935Mb, iter_time: 0.539s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.271e-03, size: 768, ETA: 9 days, 21:41:27 2022-05-20 18:42:17.252 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1010/7393, mem: 8935Mb, iter_time: 0.508s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 2.273e-03, size: 480, ETA: 9 days, 21:46:41 2022-05-20 18:42:21.104 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1020/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.7, lr: 2.276e-03, size: 768, ETA: 9 days, 21:46:30 2022-05-20 18:42:26.158 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1030/7393, mem: 8935Mb, iter_time: 0.505s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 2.279e-03, size: 512, ETA: 9 days, 21:51:35 2022-05-20 18:42:29.657 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1040/7393, mem: 8935Mb, iter_time: 0.349s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.281e-03, size: 640, ETA: 9 days, 21:49:50 2022-05-20 18:42:33.788 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1050/7393, mem: 8935Mb, iter_time: 0.412s, data_time: 0.002s, total_loss: 6.4, loss_cls: 5.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.284e-03, size: 512, ETA: 9 days, 21:50:52 2022-05-20 18:42:37.760 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1060/7393, mem: 8935Mb, iter_time: 0.397s, data_time: 0.002s, total_loss: 7.3, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.287e-03, size: 768, ETA: 9 days, 21:51:12 2022-05-20 18:42:42.507 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1070/7393, mem: 8935Mb, iter_time: 0.474s, data_time: 0.001s, total_loss: 6.4, loss_cls: 5.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.289e-03, size: 352, ETA: 9 days, 21:54:54 2022-05-20 18:42:44.936 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1080/7393, mem: 8935Mb, iter_time: 0.242s, data_time: 0.004s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.536s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.303e-03, size: 640, ETA: 9 days, 21:51:35 2022-05-20 18:43:06.145 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1130/7393, mem: 8935Mb, iter_time: 0.490s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 2.306e-03, size: 768, ETA: 9 days, 21:55:57 2022-05-20 18:43:11.066 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1140/7393, mem: 8935Mb, iter_time: 0.492s, data_time: 0.001s, total_loss: 7.9, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.308e-03, size: 448, ETA: 9 days, 22:00:22 2022-05-20 18:43:14.665 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1150/7393, mem: 8935Mb, iter_time: 0.358s, data_time: 0.003s, total_loss: 6.7, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.2, lr: 2.311e-03, size: 640, ETA: 9 days, 21:59:03 2022-05-20 18:43:21.290 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1160/7393, mem: 8935Mb, iter_time: 0.661s, data_time: 0.010s, total_loss: 7.9, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.314e-03, size: 512, ETA: 9 days, 22:10:43 2022-05-20 18:43:24.725 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1170/7393, mem: 8935Mb, iter_time: 0.341s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.317e-03, size: 608, ETA: 9 days, 22:08:38 2022-05-20 18:43:28.830 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1180/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.319e-03, size: 576, ETA: 9 days, 22:09:31 2022-05-20 18:43:32.088 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1190/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.322e-03, size: 320, ETA: 9 days, 22:06:45 2022-05-20 18:43:38.343 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1200/7393, mem: 8935Mb, iter_time: 0.620s, data_time: 0.034s, total_loss: 9.3, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.325e-03, size: 704, ETA: 9 days, 22:16:38 2022-05-20 18:43:44.197 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1210/7393, mem: 8935Mb, iter_time: 0.584s, data_time: 0.003s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 2.327e-03, size: 544, ETA: 9 days, 22:24:57 2022-05-20 18:43:48.618 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1220/7393, mem: 8935Mb, iter_time: 0.441s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.330e-03, size: 480, ETA: 9 days, 22:27:10 2022-05-20 18:43:59.503 | INFO | yolox.core.trainer:after_iter:273 - 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size: 512, ETA: 9 days, 22:55:02 2022-05-20 18:44:14.932 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1270/7393, mem: 8935Mb, iter_time: 0.412s, data_time: 0.003s, total_loss: 7.2, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.344e-03, size: 768, ETA: 9 days, 22:55:57 2022-05-20 18:44:19.856 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1280/7393, mem: 8935Mb, iter_time: 0.492s, data_time: 0.004s, total_loss: 6.5, loss_cls: 5.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.346e-03, size: 448, ETA: 9 days, 23:00:14 2022-05-20 18:44:22.367 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1290/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 2.349e-03, size: 416, ETA: 9 days, 22:54:16 2022-05-20 18:44:25.449 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1300/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.004s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.352e-03, size: 544, ETA: 9 days, 22:50:43 2022-05-20 18:44:32.210 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1310/7393, mem: 8935Mb, iter_time: 0.675s, data_time: 0.003s, total_loss: 8.2, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.3, lr: 2.354e-03, size: 320, ETA: 9 days, 23:02:45 2022-05-20 18:44:34.184 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1320/7393, mem: 8935Mb, iter_time: 0.197s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.357e-03, size: 416, ETA: 9 days, 22:54:32 2022-05-20 18:44:37.098 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1330/7393, mem: 8935Mb, iter_time: 0.290s, data_time: 0.006s, total_loss: 8.3, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 2.360e-03, size: 480, ETA: 9 days, 22:50:17 2022-05-20 18:44:40.508 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1340/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.007s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.4, lr: 2.363e-03, size: 512, ETA: 9 days, 22:48:08 2022-05-20 18:44:44.718 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1350/7393, mem: 8935Mb, iter_time: 0.420s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.365e-03, size: 768, ETA: 9 days, 22:49:23 2022-05-20 18:44:50.185 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1360/7393, mem: 8935Mb, iter_time: 0.546s, data_time: 0.001s, total_loss: 9.3, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 2.368e-03, size: 640, ETA: 9 days, 22:55:55 2022-05-20 18:44:53.783 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1370/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.003s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.446s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.381e-03, size: 480, ETA: 9 days, 23:00:18 2022-05-20 18:45:13.960 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1420/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.003s, total_loss: 9.2, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 1.2, lr: 2.384e-03, size: 544, ETA: 9 days, 22:57:03 2022-05-20 18:45:17.866 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1430/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.003s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.387e-03, size: 608, ETA: 9 days, 22:57:01 2022-05-20 18:45:21.891 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1440/7393, mem: 8935Mb, iter_time: 0.402s, data_time: 0.003s, total_loss: 8.8, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.390e-03, size: 576, ETA: 9 days, 22:57:28 2022-05-20 18:45:25.648 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1450/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.001s, total_loss: 7.6, loss_cls: 6.5, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.392e-03, size: 576, ETA: 9 days, 22:56:48 2022-05-20 18:45:30.110 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1460/7393, mem: 8935Mb, iter_time: 0.446s, data_time: 0.004s, total_loss: 8.8, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.4, lr: 2.395e-03, size: 736, ETA: 9 days, 22:59:04 2022-05-20 18:45:35.670 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1470/7393, mem: 8935Mb, iter_time: 0.555s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.398e-03, size: 736, ETA: 9 days, 23:05:54 2022-05-20 18:45:40.903 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1480/7393, mem: 8935Mb, iter_time: 0.523s, data_time: 0.001s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.400e-03, size: 640, ETA: 9 days, 23:11:22 2022-05-20 18:45:45.392 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1490/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.403e-03, size: 640, ETA: 9 days, 23:13:44 2022-05-20 18:45:49.047 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1500/7393, mem: 8935Mb, iter_time: 0.363s, data_time: 0.003s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.406e-03, size: 352, ETA: 9 days, 23:12:33 2022-05-20 18:45:53.844 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1510/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.008s, total_loss: 7.0, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.408e-03, size: 384, ETA: 9 days, 23:16:07 2022-05-20 18:45:56.001 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1520/7393, mem: 8935Mb, iter_time: 0.215s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 2.411e-03, size: 352, ETA: 9 days, 23:08:48 2022-05-20 18:45:58.841 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1530/7393, mem: 8935Mb, iter_time: 0.283s, data_time: 0.003s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.414e-03, size: 608, ETA: 9 days, 23:04:20 2022-05-20 18:46:03.537 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1540/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.008s, total_loss: 7.8, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 0.9, loss_l1: 0.4, lr: 2.417e-03, size: 608, ETA: 9 days, 23:07:32 2022-05-20 18:46:07.468 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1550/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.419e-03, size: 512, ETA: 9 days, 23:07:30 2022-05-20 18:46:10.429 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1560/7393, mem: 8935Mb, iter_time: 0.295s, data_time: 0.003s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.422e-03, size: 416, ETA: 9 days, 23:03:33 2022-05-20 18:46:13.135 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1570/7393, mem: 8935Mb, iter_time: 0.270s, data_time: 0.008s, total_loss: 7.8, loss_cls: 6.3, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.425e-03, size: 384, ETA: 9 days, 22:58:34 2022-05-20 18:46:16.183 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1580/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.009s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.427e-03, size: 416, ETA: 9 days, 22:54:58 2022-05-20 18:46:20.015 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1590/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.005s, total_loss: 8.0, loss_cls: 7.0, loss_iou: 0.2, loss_dfl: 0.9, loss_l1: 0.4, lr: 2.430e-03, size: 672, ETA: 9 days, 22:54:37 2022-05-20 18:46:24.453 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1600/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.433e-03, size: 512, ETA: 9 days, 22:56:46 2022-05-20 18:46:27.432 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1610/7393, mem: 8935Mb, iter_time: 0.297s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.436e-03, size: 480, ETA: 9 days, 22:52:55 2022-05-20 18:46:30.041 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1620/7393, mem: 8935Mb, iter_time: 0.260s, data_time: 0.002s, total_loss: 9.3, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.438e-03, size: 320, ETA: 9 days, 22:47:34 2022-05-20 18:46:32.574 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1630/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.006s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.441e-03, size: 320, ETA: 9 days, 22:41:55 2022-05-20 18:46:35.143 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1640/7393, mem: 8935Mb, iter_time: 0.255s, data_time: 0.010s, total_loss: 7.3, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.444e-03, size: 352, ETA: 9 days, 22:36:22 2022-05-20 18:46:37.752 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1650/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.003s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.446e-03, size: 320, ETA: 9 days, 22:31:02 2022-05-20 18:46:41.077 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1660/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.003s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 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0.002s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.7, lr: 2.509e-03, size: 768, ETA: 9 days, 22:13:51 2022-05-20 18:48:08.594 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1890/7393, mem: 8935Mb, iter_time: 0.514s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 2.511e-03, size: 544, ETA: 9 days, 22:18:46 2022-05-20 18:48:12.298 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1900/7393, mem: 8935Mb, iter_time: 0.370s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.514e-03, size: 640, ETA: 9 days, 22:17:58 2022-05-20 18:48:16.036 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1910/7393, mem: 8935Mb, iter_time: 0.373s, data_time: 0.002s, total_loss: 6.9, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.517e-03, size: 384, ETA: 9 days, 22:17:18 2022-05-20 18:48:18.659 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1920/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.006s, total_loss: 7.7, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.519e-03, size: 416, ETA: 9 days, 22:12:07 2022-05-20 18:48:21.308 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1930/7393, mem: 8935Mb, iter_time: 0.264s, data_time: 0.005s, total_loss: 6.7, loss_cls: 5.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 2.522e-03, size: 416, ETA: 9 days, 22:07:08 2022-05-20 18:48:24.974 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1940/7393, mem: 8935Mb, iter_time: 0.366s, data_time: 0.004s, total_loss: 7.2, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 2.525e-03, size: 736, ETA: 9 days, 22:06:11 2022-05-20 18:48:30.637 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1950/7393, mem: 8935Mb, iter_time: 0.566s, data_time: 0.002s, total_loss: 7.7, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.528e-03, size: 736, ETA: 9 days, 22:13:08 2022-05-20 18:48:35.740 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1960/7393, mem: 8935Mb, iter_time: 0.510s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.530e-03, size: 608, ETA: 9 days, 22:17:50 2022-05-20 18:48:39.607 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1970/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.533e-03, size: 544, ETA: 9 days, 22:17:41 2022-05-20 18:48:43.550 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1980/7393, mem: 8935Mb, iter_time: 0.394s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.536e-03, size: 672, ETA: 9 days, 22:17:50 2022-05-20 18:48:48.031 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 1990/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.002s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 0.9, loss_l1: 0.5, lr: 2.538e-03, size: 544, ETA: 9 days, 22:20:05 2022-05-20 18:48:51.925 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2000/7393, mem: 8935Mb, iter_time: 0.389s, data_time: 0.002s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.541e-03, size: 672, ETA: 9 days, 22:20:02 2022-05-20 18:48:55.860 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2010/7393, mem: 8935Mb, iter_time: 0.393s, data_time: 0.001s, total_loss: 7.3, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.544e-03, size: 352, ETA: 9 days, 22:20:09 2022-05-20 18:48:58.105 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2020/7393, mem: 8935Mb, iter_time: 0.224s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.546e-03, size: 448, ETA: 9 days, 22:13:38 2022-05-20 18:49:02.106 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2030/7393, mem: 8935Mb, iter_time: 0.399s, data_time: 0.004s, total_loss: 9.1, loss_cls: 8.0, loss_iou: 0.2, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.549e-03, size: 768, ETA: 9 days, 22:14:00 2022-05-20 18:49:06.950 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2040/7393, mem: 8935Mb, iter_time: 0.484s, data_time: 0.001s, total_loss: 8.1, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.552e-03, size: 416, ETA: 9 days, 22:17:40 2022-05-20 18:49:10.644 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2050/7393, mem: 8935Mb, iter_time: 0.369s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.555e-03, size: 768, ETA: 9 days, 22:16:50 2022-05-20 18:49:16.373 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2060/7393, mem: 8935Mb, iter_time: 0.573s, data_time: 0.001s, total_loss: 8.4, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.557e-03, size: 704, ETA: 9 days, 22:23:56 2022-05-20 18:49:21.541 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2070/7393, mem: 8935Mb, iter_time: 0.516s, data_time: 0.002s, total_loss: 6.2, loss_cls: 4.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.560e-03, size: 672, ETA: 9 days, 22:28:50 2022-05-20 18:49:25.558 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2080/7393, mem: 8935Mb, iter_time: 0.401s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.563e-03, size: 384, ETA: 9 days, 22:29:15 2022-05-20 18:49:28.018 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2090/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.565e-03, size: 512, ETA: 9 days, 22:23:36 2022-05-20 18:49:30.840 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2100/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.568e-03, size: 320, ETA: 9 days, 22:19:23 2022-05-20 18:49:33.975 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2110/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.003s, total_loss: 9.0, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.571e-03, size: 672, ETA: 9 days, 22:16:22 2022-05-20 18:49:37.888 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2120/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.004s, total_loss: 8.4, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.574e-03, size: 320, ETA: 9 days, 22:16:21 2022-05-20 18:49:40.412 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2130/7393, mem: 8935Mb, iter_time: 0.251s, data_time: 0.003s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.576e-03, size: 576, ETA: 9 days, 22:10:59 2022-05-20 18:49:44.818 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2140/7393, mem: 8935Mb, iter_time: 0.440s, data_time: 0.004s, total_loss: 9.8, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 2.579e-03, size: 672, ETA: 9 days, 22:12:54 2022-05-20 18:49:48.902 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2150/7393, mem: 8935Mb, iter_time: 0.408s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.582e-03, size: 384, ETA: 9 days, 22:13:36 2022-05-20 18:49:51.485 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2160/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.584e-03, size: 512, ETA: 9 days, 22:08:29 2022-05-20 18:49:54.754 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2170/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.587e-03, size: 544, ETA: 9 days, 22:06:02 2022-05-20 18:49:58.246 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2180/7393, mem: 8935Mb, iter_time: 0.348s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.590e-03, size: 544, ETA: 9 days, 22:04:26 2022-05-20 18:50:02.449 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2190/7393, mem: 8935Mb, iter_time: 0.420s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 2.592e-03, size: 768, ETA: 9 days, 22:05:35 2022-05-20 18:50:07.216 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2200/7393, mem: 8935Mb, iter_time: 0.476s, data_time: 0.001s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.595e-03, size: 384, ETA: 9 days, 22:08:54 2022-05-20 18:50:09.488 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2210/7393, mem: 8935Mb, iter_time: 0.226s, data_time: 0.003s, total_loss: 8.3, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.598e-03, size: 416, ETA: 9 days, 22:02:38 2022-05-20 18:50:13.392 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2220/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.601e-03, size: 768, ETA: 9 days, 22:02:37 2022-05-20 18:50:18.476 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2230/7393, mem: 8935Mb, iter_time: 0.508s, data_time: 0.001s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.603e-03, size: 512, ETA: 9 days, 22:07:08 2022-05-20 18:50:21.236 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2240/7393, mem: 8935Mb, iter_time: 0.275s, data_time: 0.002s, total_loss: 6.8, loss_cls: 5.4, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.368s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.617e-03, size: 384, ETA: 9 days, 21:55:38 2022-05-20 18:50:37.327 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2290/7393, mem: 8935Mb, iter_time: 0.230s, data_time: 0.004s, total_loss: 7.3, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.620e-03, size: 416, ETA: 9 days, 21:49:35 2022-05-20 18:50:40.117 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2300/7393, mem: 8935Mb, iter_time: 0.278s, data_time: 0.006s, total_loss: 8.1, loss_cls: 7.0, loss_iou: 0.2, loss_dfl: 1.0, loss_l1: 0.7, lr: 2.622e-03, size: 512, ETA: 9 days, 21:45:21 2022-05-20 18:50:44.061 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2310/7393, mem: 8935Mb, iter_time: 0.392s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.625e-03, size: 672, ETA: 9 days, 21:45:26 2022-05-20 18:50:48.651 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2320/7393, mem: 8935Mb, iter_time: 0.459s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.628e-03, size: 576, ETA: 9 days, 21:48:04 2022-05-20 18:50:52.451 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2330/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 2.630e-03, size: 608, ETA: 9 days, 21:47:41 2022-05-20 18:50:56.594 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2340/7393, mem: 8935Mb, iter_time: 0.414s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.633e-03, size: 608, ETA: 9 days, 21:48:36 2022-05-20 18:51:01.250 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2350/7393, mem: 8935Mb, iter_time: 0.465s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.636e-03, size: 736, ETA: 9 days, 21:51:28 2022-05-20 18:51:06.044 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2360/7393, mem: 8935Mb, iter_time: 0.479s, data_time: 0.001s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.638e-03, size: 512, ETA: 9 days, 21:54:50 2022-05-20 18:51:09.243 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2370/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.641e-03, size: 544, ETA: 9 days, 21:52:11 2022-05-20 18:51:12.987 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2380/7393, mem: 8935Mb, iter_time: 0.374s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.644e-03, size: 576, ETA: 9 days, 21:51:35 2022-05-20 18:51:16.698 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2390/7393, mem: 8935Mb, iter_time: 0.371s, data_time: 0.002s, total_loss: 8.4, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.4, lr: 2.647e-03, size: 512, ETA: 9 days, 21:50:53 2022-05-20 18:51:20.506 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2400/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.3, lr: 2.649e-03, size: 704, ETA: 9 days, 21:50:32 2022-05-20 18:51:25.330 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2410/7393, mem: 8935Mb, iter_time: 0.482s, data_time: 0.001s, total_loss: 7.2, loss_cls: 5.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.652e-03, size: 608, ETA: 9 days, 21:54:00 2022-05-20 18:51:28.836 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2420/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.655e-03, size: 416, ETA: 9 days, 21:52:31 2022-05-20 18:51:32.301 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2430/7393, mem: 8935Mb, iter_time: 0.346s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.657e-03, size: 736, ETA: 9 days, 21:50:53 2022-05-20 18:51:36.804 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2440/7393, mem: 8935Mb, iter_time: 0.450s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 0.9, loss_l1: 0.5, lr: 2.660e-03, size: 352, ETA: 9 days, 21:53:08 2022-05-20 18:51:39.493 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2450/7393, mem: 8935Mb, iter_time: 0.268s, data_time: 0.003s, total_loss: 8.2, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.663e-03, size: 608, ETA: 9 days, 21:48:35 2022-05-20 18:51:44.308 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2460/7393, mem: 8935Mb, iter_time: 0.481s, data_time: 0.004s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 2.665e-03, size: 768, ETA: 9 days, 21:52:00 2022-05-20 18:51:49.724 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2470/7393, mem: 8935Mb, iter_time: 0.541s, data_time: 0.001s, total_loss: 8.2, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 2.668e-03, size: 608, ETA: 9 days, 21:57:39 2022-05-20 18:51:53.532 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2480/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.001s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.3, lr: 2.671e-03, size: 512, ETA: 9 days, 21:57:18 2022-05-20 18:51:56.855 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2490/7393, mem: 8935Mb, iter_time: 0.332s, data_time: 0.003s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.1, lr: 2.674e-03, size: 544, ETA: 9 days, 21:55:08 2022-05-20 18:52:00.313 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2500/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.002s, total_loss: 10.3, loss_cls: 9.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.676e-03, size: 512, ETA: 9 days, 21:53:28 2022-05-20 18:52:04.099 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2510/7393, mem: 8935Mb, iter_time: 0.378s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 2.4, lr: 2.679e-03, size: 704, ETA: 9 days, 21:53:02 2022-05-20 18:52:09.508 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2520/7393, mem: 8935Mb, iter_time: 0.540s, data_time: 0.001s, total_loss: 9.2, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 1.0, lr: 2.682e-03, size: 768, ETA: 9 days, 21:58:38 2022-05-20 18:52:14.269 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2530/7393, mem: 8935Mb, iter_time: 0.476s, data_time: 0.001s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.684e-03, size: 320, ETA: 9 days, 22:01:49 2022-05-20 18:52:17.096 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2540/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.002s, total_loss: 8.4, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.687e-03, size: 640, ETA: 9 days, 21:57:49 2022-05-20 18:52:20.693 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2550/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.690e-03, size: 320, ETA: 9 days, 21:56:41 2022-05-20 18:52:23.794 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2560/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.004s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.693e-03, size: 736, ETA: 9 days, 21:53:42 2022-05-20 18:52:28.380 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2570/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.002s, total_loss: 7.4, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.695e-03, size: 384, ETA: 9 days, 21:56:14 2022-05-20 18:52:31.766 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2580/7393, mem: 8935Mb, iter_time: 0.338s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.698e-03, size: 768, ETA: 9 days, 21:54:19 2022-05-20 18:52:37.584 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2590/7393, mem: 8935Mb, iter_time: 0.581s, data_time: 0.001s, total_loss: 11.4, loss_cls: 10.0, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.3, lr: 2.701e-03, size: 736, ETA: 9 days, 22:01:23 2022-05-20 18:52:42.727 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2600/7393, mem: 8935Mb, iter_time: 0.514s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.703e-03, size: 608, ETA: 9 days, 22:05:57 2022-05-20 18:52:47.387 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2610/7393, mem: 8935Mb, iter_time: 0.466s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.706e-03, size: 768, ETA: 9 days, 22:08:44 2022-05-20 18:52:53.131 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2620/7393, mem: 8935Mb, iter_time: 0.574s, data_time: 0.001s, total_loss: 8.3, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 2.709e-03, size: 704, ETA: 9 days, 22:15:29 2022-05-20 18:52:57.454 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2630/7393, mem: 8935Mb, iter_time: 0.432s, data_time: 0.001s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.711e-03, size: 416, ETA: 9 days, 22:17:01 2022-05-20 18:53:00.425 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2640/7393, mem: 8935Mb, iter_time: 0.296s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.714e-03, size: 608, ETA: 9 days, 22:13:34 2022-05-20 18:53:05.103 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2650/7393, mem: 8935Mb, iter_time: 0.467s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.717e-03, size: 736, ETA: 9 days, 22:16:24 2022-05-20 18:53:09.739 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2660/7393, mem: 8935Mb, iter_time: 0.463s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.720e-03, size: 416, ETA: 9 days, 22:19:03 2022-05-20 18:53:13.006 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2670/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.722e-03, size: 704, ETA: 9 days, 22:16:41 2022-05-20 18:53:18.194 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2680/7393, mem: 8935Mb, iter_time: 0.518s, data_time: 0.001s, total_loss: 9.1, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.725e-03, size: 704, ETA: 9 days, 22:21:22 2022-05-20 18:53:22.698 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2690/7393, mem: 8935Mb, iter_time: 0.450s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.728e-03, size: 512, ETA: 9 days, 22:23:32 2022-05-20 18:53:26.633 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2700/7393, mem: 8935Mb, iter_time: 0.393s, data_time: 0.002s, total_loss: 9.3, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.730e-03, size: 768, ETA: 9 days, 22:23:38 2022-05-20 18:53:31.348 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2710/7393, mem: 8935Mb, iter_time: 0.471s, data_time: 0.001s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.3, lr: 2.733e-03, size: 352, ETA: 9 days, 22:26:33 2022-05-20 18:53:34.716 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2720/7393, mem: 8935Mb, iter_time: 0.336s, data_time: 0.003s, total_loss: 8.6, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.2, lr: 2.736e-03, size: 768, ETA: 9 days, 22:24:34 2022-05-20 18:53:39.960 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2730/7393, mem: 8935Mb, iter_time: 0.524s, data_time: 0.001s, total_loss: 6.9, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.739e-03, size: 576, ETA: 9 days, 22:29:24 2022-05-20 18:53:43.156 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2740/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.741e-03, size: 352, ETA: 9 days, 22:26:48 2022-05-20 18:53:45.972 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2750/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.008s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 2.744e-03, size: 512, ETA: 9 days, 22:22:49 2022-05-20 18:53:49.455 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2760/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.006s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.747e-03, size: 512, ETA: 9 days, 22:21:14 2022-05-20 18:53:53.419 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2770/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.749e-03, size: 736, ETA: 9 days, 22:21:26 2022-05-20 18:53:57.846 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2780/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.001s, total_loss: 9.0, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 0.9, loss_l1: 0.5, lr: 2.752e-03, size: 352, ETA: 9 days, 22:23:18 2022-05-20 18:54:00.047 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2790/7393, mem: 8935Mb, iter_time: 0.219s, data_time: 0.004s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.755e-03, size: 416, ETA: 9 days, 22:17:07 2022-05-20 18:54:03.088 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2800/7393, mem: 8935Mb, iter_time: 0.303s, data_time: 0.006s, total_loss: 9.0, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.4, lr: 2.757e-03, size: 544, ETA: 9 days, 22:13:58 2022-05-20 18:54:06.130 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2810/7393, mem: 8935Mb, iter_time: 0.303s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.760e-03, size: 352, ETA: 9 days, 22:10:50 2022-05-20 18:54:08.514 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2820/7393, mem: 8935Mb, iter_time: 0.237s, data_time: 0.005s, total_loss: 8.8, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.472s, data_time: 0.001s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.774e-03, size: 352, ETA: 9 days, 22:12:14 2022-05-20 18:54:29.004 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2870/7393, mem: 8935Mb, iter_time: 0.292s, data_time: 0.003s, total_loss: 7.6, loss_cls: 6.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 2.776e-03, size: 640, ETA: 9 days, 22:08:43 2022-05-20 18:54:33.633 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2880/7393, mem: 8935Mb, iter_time: 0.462s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.779e-03, size: 672, ETA: 9 days, 22:11:18 2022-05-20 18:54:38.572 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2890/7393, mem: 8935Mb, iter_time: 0.493s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.782e-03, size: 704, ETA: 9 days, 22:15:00 2022-05-20 18:54:43.607 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2900/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 2.785e-03, size: 672, ETA: 9 days, 22:19:01 2022-05-20 18:54:48.108 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2910/7393, mem: 8935Mb, iter_time: 0.450s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.787e-03, size: 576, ETA: 9 days, 22:21:08 2022-05-20 18:54:52.503 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2920/7393, mem: 8935Mb, iter_time: 0.439s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.790e-03, size: 768, ETA: 9 days, 22:22:52 2022-05-20 18:54:58.291 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2930/7393, mem: 8935Mb, iter_time: 0.578s, data_time: 0.001s, total_loss: 8.2, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.793e-03, size: 704, ETA: 9 days, 22:29:33 2022-05-20 18:55:03.029 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2940/7393, mem: 8935Mb, iter_time: 0.473s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 2.795e-03, size: 576, ETA: 9 days, 22:32:30 2022-05-20 18:55:06.928 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2950/7393, mem: 8935Mb, iter_time: 0.389s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.4, loss_iou: 0.2, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.798e-03, size: 640, ETA: 9 days, 22:32:26 2022-05-20 18:55:10.770 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2960/7393, mem: 8935Mb, iter_time: 0.384s, data_time: 0.001s, total_loss: 8.8, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.801e-03, size: 448, ETA: 9 days, 22:32:11 2022-05-20 18:55:13.785 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2970/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.002s, total_loss: 6.6, loss_cls: 5.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.803e-03, size: 512, ETA: 9 days, 22:28:59 2022-05-20 18:55:17.649 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2980/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.003s, total_loss: 8.2, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 2.806e-03, size: 672, ETA: 9 days, 22:28:48 2022-05-20 18:55:21.703 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 2990/7393, mem: 8935Mb, iter_time: 0.405s, data_time: 0.002s, total_loss: 9.3, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.809e-03, size: 416, ETA: 9 days, 22:29:17 2022-05-20 18:55:24.528 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3000/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.5, lr: 2.812e-03, size: 576, ETA: 9 days, 22:25:25 2022-05-20 18:55:28.336 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3010/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 8.2, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.814e-03, size: 544, ETA: 9 days, 22:25:00 2022-05-20 18:55:32.381 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3020/7393, mem: 8935Mb, iter_time: 0.404s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.6, loss_iou: 0.2, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.817e-03, size: 704, ETA: 9 days, 22:25:28 2022-05-20 18:55:37.045 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3030/7393, mem: 8935Mb, iter_time: 0.466s, data_time: 0.002s, total_loss: 12.3, loss_cls: 11.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.820e-03, size: 544, ETA: 9 days, 22:28:07 2022-05-20 18:55:40.261 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3040/7393, mem: 8935Mb, iter_time: 0.321s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.822e-03, size: 448, ETA: 9 days, 22:25:39 2022-05-20 18:55:43.315 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3050/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.004s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.4, lr: 2.825e-03, size: 448, ETA: 9 days, 22:22:37 2022-05-20 18:55:46.887 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3060/7393, mem: 8935Mb, iter_time: 0.356s, data_time: 0.006s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.828e-03, size: 672, ETA: 9 days, 22:21:24 2022-05-20 18:55:51.495 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3070/7393, mem: 8935Mb, iter_time: 0.460s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.831e-03, size: 608, ETA: 9 days, 22:23:51 2022-05-20 18:55:55.657 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3080/7393, mem: 8935Mb, iter_time: 0.416s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.833e-03, size: 640, ETA: 9 days, 22:24:44 2022-05-20 18:55:59.315 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3090/7393, mem: 8935Mb, iter_time: 0.365s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.836e-03, size: 384, ETA: 9 days, 22:23:51 2022-05-20 18:56:01.790 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3100/7393, mem: 8935Mb, iter_time: 0.247s, data_time: 0.003s, total_loss: 8.0, loss_cls: 6.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.839e-03, size: 384, ETA: 9 days, 22:18:47 2022-05-20 18:56:05.322 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3110/7393, mem: 8935Mb, iter_time: 0.352s, data_time: 0.002s, total_loss: 10.4, loss_cls: 9.0, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.276s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 2.852e-03, size: 320, ETA: 9 days, 22:22:56 2022-05-20 18:56:24.824 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3160/7393, mem: 8935Mb, iter_time: 0.227s, data_time: 0.005s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.855e-03, size: 384, ETA: 9 days, 22:17:15 2022-05-20 18:56:27.399 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3170/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.002s, total_loss: 8.1, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 0.9, loss_l1: 0.5, lr: 2.858e-03, size: 384, ETA: 9 days, 22:12:34 2022-05-20 18:56:30.601 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3180/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.005s, total_loss: 6.8, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.860e-03, size: 640, ETA: 9 days, 22:10:05 2022-05-20 18:56:35.122 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3190/7393, mem: 8935Mb, iter_time: 0.451s, data_time: 0.002s, total_loss: 6.2, loss_cls: 5.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.863e-03, size: 576, ETA: 9 days, 22:12:13 2022-05-20 18:56:39.092 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3200/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.866e-03, size: 640, ETA: 9 days, 22:12:25 2022-05-20 18:56:42.979 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3210/7393, mem: 8935Mb, iter_time: 0.388s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 2.868e-03, size: 480, ETA: 9 days, 22:12:21 2022-05-20 18:56:46.329 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3220/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.2, lr: 2.871e-03, size: 544, ETA: 9 days, 22:10:24 2022-05-20 18:56:49.659 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3230/7393, mem: 8935Mb, iter_time: 0.332s, data_time: 0.002s, total_loss: 7.0, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.874e-03, size: 448, ETA: 9 days, 22:08:23 2022-05-20 18:56:52.891 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3240/7393, mem: 8935Mb, iter_time: 0.322s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 2.877e-03, size: 608, ETA: 9 days, 22:06:02 2022-05-20 18:56:56.417 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3250/7393, mem: 8935Mb, iter_time: 0.352s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 2.879e-03, size: 384, ETA: 9 days, 22:04:43 2022-05-20 18:56:59.652 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3260/7393, mem: 8935Mb, iter_time: 0.323s, data_time: 0.003s, total_loss: 7.1, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.882e-03, size: 640, ETA: 9 days, 22:02:23 2022-05-20 18:57:04.693 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3270/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.885e-03, size: 768, ETA: 9 days, 22:06:17 2022-05-20 18:57:09.381 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3280/7393, mem: 8935Mb, iter_time: 0.468s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.887e-03, size: 352, ETA: 9 days, 22:08:59 2022-05-20 18:57:12.340 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3290/7393, mem: 8935Mb, iter_time: 0.295s, data_time: 0.004s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 2.890e-03, size: 672, ETA: 9 days, 22:05:41 2022-05-20 18:57:16.725 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3300/7393, mem: 8935Mb, iter_time: 0.438s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.893e-03, size: 512, ETA: 9 days, 22:07:20 2022-05-20 18:57:20.269 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3310/7393, mem: 8935Mb, iter_time: 0.354s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.895e-03, size: 576, ETA: 9 days, 22:06:04 2022-05-20 18:57:24.129 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3320/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.898e-03, size: 576, ETA: 9 days, 22:05:54 2022-05-20 18:57:27.569 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3330/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 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yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3370/7393, mem: 8935Mb, iter_time: 0.383s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.7, lr: 2.912e-03, size: 704, ETA: 9 days, 21:58:29 2022-05-20 18:57:46.045 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3380/7393, mem: 8935Mb, iter_time: 0.453s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 2.914e-03, size: 512, ETA: 9 days, 22:00:38 2022-05-20 18:57:48.924 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3390/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.917e-03, size: 384, ETA: 9 days, 21:57:08 2022-05-20 18:57:51.983 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3400/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 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8935Mb, iter_time: 0.374s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.931e-03, size: 544, ETA: 9 days, 22:01:20 2022-05-20 18:58:13.936 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3450/7393, mem: 8935Mb, iter_time: 0.422s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 2.933e-03, size: 768, ETA: 9 days, 22:02:25 2022-05-20 18:58:18.975 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3460/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.001s, total_loss: 7.6, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.936e-03, size: 512, ETA: 9 days, 22:06:15 2022-05-20 18:58:21.846 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3470/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.939e-03, size: 416, ETA: 9 days, 22:02:44 2022-05-20 18:58:25.326 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3480/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.003s, total_loss: 7.8, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.941e-03, size: 704, ETA: 9 days, 22:01:17 2022-05-20 18:58:29.959 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3490/7393, mem: 8935Mb, iter_time: 0.463s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.944e-03, size: 512, ETA: 9 days, 22:03:44 2022-05-20 18:58:32.823 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3500/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.947e-03, size: 416, ETA: 9 days, 22:00:12 2022-05-20 18:58:36.348 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3510/7393, mem: 8935Mb, iter_time: 0.352s, data_time: 0.003s, total_loss: 9.5, loss_cls: 8.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 2.950e-03, size: 736, ETA: 9 days, 21:58:54 2022-05-20 18:58:41.038 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3520/7393, mem: 8935Mb, iter_time: 0.468s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.952e-03, size: 448, ETA: 9 days, 22:01:32 2022-05-20 18:58:44.234 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3530/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.955e-03, size: 640, ETA: 9 days, 21:59:08 2022-05-20 18:58:48.428 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3540/7393, mem: 8935Mb, iter_time: 0.419s, data_time: 0.002s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.958e-03, size: 576, ETA: 9 days, 22:00:06 2022-05-20 18:58:51.870 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3550/7393, mem: 8935Mb, iter_time: 0.344s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.960e-03, size: 480, ETA: 9 days, 21:58:32 2022-05-20 18:58:54.906 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3560/7393, mem: 8935Mb, iter_time: 0.303s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.963e-03, size: 512, ETA: 9 days, 21:55:37 2022-05-20 18:58:57.993 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3570/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 2.966e-03, size: 480, ETA: 9 days, 21:52:52 2022-05-20 18:59:01.218 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3580/7393, mem: 8935Mb, iter_time: 0.322s, data_time: 0.004s, total_loss: 6.9, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 2.968e-03, size: 544, ETA: 9 days, 21:50:34 2022-05-20 18:59:05.279 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3590/7393, mem: 8935Mb, iter_time: 0.405s, data_time: 0.002s, total_loss: 6.9, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 2.971e-03, size: 672, ETA: 9 days, 21:51:05 2022-05-20 18:59:09.536 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3600/7393, mem: 8935Mb, iter_time: 0.425s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.1, lr: 2.974e-03, size: 512, ETA: 9 days, 21:52:16 2022-05-20 18:59:13.065 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3610/7393, mem: 8935Mb, iter_time: 0.352s, data_time: 0.002s, total_loss: 7.3, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 2.977e-03, size: 608, ETA: 9 days, 21:51:00 2022-05-20 18:59:16.633 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3620/7393, mem: 8935Mb, iter_time: 0.356s, data_time: 0.002s, total_loss: 8.4, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.979e-03, size: 384, ETA: 9 days, 21:49:53 2022-05-20 18:59:19.742 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3630/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.003s, total_loss: 7.6, loss_cls: 6.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.982e-03, size: 672, ETA: 9 days, 21:47:13 2022-05-20 18:59:24.418 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3640/7393, mem: 8935Mb, iter_time: 0.467s, data_time: 0.001s, total_loss: 8.4, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 2.985e-03, size: 608, ETA: 9 days, 21:49:48 2022-05-20 18:59:28.659 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3650/7393, mem: 8935Mb, iter_time: 0.424s, data_time: 0.001s, total_loss: 8.5, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.2, lr: 2.987e-03, size: 640, ETA: 9 days, 21:50:55 2022-05-20 18:59:32.679 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3660/7393, mem: 8935Mb, iter_time: 0.401s, data_time: 0.002s, total_loss: 7.2, loss_cls: 5.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 2.990e-03, size: 512, ETA: 9 days, 21:51:18 2022-05-20 18:59:36.338 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3670/7393, mem: 8935Mb, iter_time: 0.365s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 2.993e-03, size: 672, ETA: 9 days, 21:50:29 2022-05-20 18:59:40.472 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3680/7393, mem: 8935Mb, iter_time: 0.413s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 2.996e-03, size: 416, ETA: 9 days, 21:51:14 2022-05-20 18:59:43.103 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3690/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.005s, total_loss: 8.3, loss_cls: 7.0, loss_iou: 0.3, loss_dfl: 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epoch: 2/300, iter: 3840/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 3.039e-03, size: 768, ETA: 9 days, 21:54:19 2022-05-20 19:00:48.830 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3850/7393, mem: 8935Mb, iter_time: 0.489s, data_time: 0.001s, total_loss: 10.4, loss_cls: 9.2, loss_iou: 0.2, loss_dfl: 1.2, loss_l1: 0.8, lr: 3.042e-03, size: 416, ETA: 9 days, 21:57:33 2022-05-20 19:00:51.074 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3860/7393, mem: 8935Mb, iter_time: 0.223s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 3.044e-03, size: 352, ETA: 9 days, 21:52:06 2022-05-20 19:00:53.658 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3870/7393, mem: 8935Mb, iter_time: 0.254s, data_time: 0.004s, total_loss: 7.0, loss_cls: 5.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 3.047e-03, 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0.002s, total_loss: 7.8, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.058e-03, size: 384, ETA: 9 days, 21:46:01 2022-05-20 19:01:11.131 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3920/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.003s, total_loss: 8.6, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 3.060e-03, size: 416, ETA: 9 days, 21:40:53 2022-05-20 19:01:14.178 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3930/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.003s, total_loss: 7.2, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 3.063e-03, size: 480, ETA: 9 days, 21:38:05 2022-05-20 19:01:17.130 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3940/7393, mem: 8935Mb, iter_time: 0.294s, data_time: 0.003s, total_loss: 7.3, loss_cls: 6.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 3.066e-03, size: 352, ETA: 9 days, 21:34:59 2022-05-20 19:01:19.366 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3950/7393, mem: 8935Mb, iter_time: 0.223s, data_time: 0.003s, total_loss: 6.5, loss_cls: 5.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.069e-03, size: 352, ETA: 9 days, 21:29:34 2022-05-20 19:01:22.888 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3960/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.006s, total_loss: 9.2, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.8, lr: 3.071e-03, size: 704, ETA: 9 days, 21:28:19 2022-05-20 19:01:28.509 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3970/7393, mem: 8935Mb, iter_time: 0.561s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.074e-03, size: 768, ETA: 9 days, 21:33:53 2022-05-20 19:01:33.885 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3980/7393, mem: 8935Mb, iter_time: 0.537s, data_time: 0.001s, total_loss: 6.9, loss_cls: 5.6, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 1.2, lr: 3.077e-03, size: 608, ETA: 9 days, 21:38:40 2022-05-20 19:01:37.692 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 3990/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 7.9, loss_cls: 6.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 3.079e-03, size: 512, ETA: 9 days, 21:38:20 2022-05-20 19:01:40.485 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4000/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.004s, total_loss: 7.3, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 3.082e-03, size: 352, ETA: 9 days, 21:34:44 2022-05-20 19:01:43.473 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4010/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.005s, total_loss: 8.7, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.2, lr: 3.085e-03, size: 576, ETA: 9 days, 21:31:47 2022-05-20 19:01:47.733 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4020/7393, mem: 8935Mb, iter_time: 0.425s, data_time: 0.003s, total_loss: 8.1, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 3.088e-03, size: 704, ETA: 9 days, 21:32:56 2022-05-20 19:01:51.847 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4030/7393, mem: 8935Mb, iter_time: 0.411s, data_time: 0.001s, total_loss: 7.5, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.090e-03, size: 320, ETA: 9 days, 21:33:37 2022-05-20 19:01:54.900 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4040/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.004s, total_loss: 8.6, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 3.093e-03, size: 736, ETA: 9 days, 21:30:53 2022-05-20 19:02:00.144 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4050/7393, mem: 8935Mb, iter_time: 0.524s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 3.096e-03, size: 608, ETA: 9 days, 21:35:12 2022-05-20 19:02:03.580 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4060/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 0.002s, total_loss: 6.3, loss_cls: 5.0, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.9, lr: 3.098e-03, size: 384, ETA: 9 days, 21:33:42 2022-05-20 19:02:06.667 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4070/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.004s, total_loss: 6.8, loss_cls: 5.5, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 3.101e-03, size: 672, ETA: 9 days, 21:31:05 2022-05-20 19:02:11.708 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4080/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 3.104e-03, size: 704, ETA: 9 days, 21:34:44 2022-05-20 19:02:16.795 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4090/7393, mem: 8935Mb, iter_time: 0.508s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.106e-03, size: 672, ETA: 9 days, 21:38:32 2022-05-20 19:02:21.027 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4100/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 3.109e-03, size: 512, ETA: 9 days, 21:39:35 2022-05-20 19:02:24.212 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4110/7393, mem: 8935Mb, iter_time: 0.318s, data_time: 0.003s, total_loss: 7.1, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.7, lr: 3.112e-03, size: 544, ETA: 9 days, 21:37:17 2022-05-20 19:02:27.636 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4120/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.2, lr: 3.115e-03, size: 512, ETA: 9 days, 21:35:45 2022-05-20 19:02:30.888 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4130/7393, mem: 8935Mb, iter_time: 0.324s, data_time: 0.003s, total_loss: 7.2, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 3.117e-03, size: 480, ETA: 9 days, 21:33:39 2022-05-20 19:02:34.504 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4140/7393, mem: 8935Mb, iter_time: 0.361s, data_time: 0.003s, total_loss: 7.4, loss_cls: 6.1, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 3.120e-03, size: 640, ETA: 9 days, 21:32:44 2022-05-20 19:02:39.415 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4150/7393, mem: 8935Mb, iter_time: 0.490s, data_time: 0.003s, total_loss: 8.2, loss_cls: 6.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 3.123e-03, size: 768, ETA: 9 days, 21:35:57 2022-05-20 19:02:44.486 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4160/7393, mem: 8935Mb, iter_time: 0.507s, data_time: 0.001s, total_loss: 8.0, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.0, lr: 3.125e-03, size: 512, ETA: 9 days, 21:39:40 2022-05-20 19:02:48.489 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4170/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 3.128e-03, size: 768, ETA: 9 days, 21:39:59 2022-05-20 19:02:53.298 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4180/7393, mem: 8935Mb, iter_time: 0.480s, data_time: 0.001s, total_loss: 8.2, loss_cls: 6.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.1, lr: 3.131e-03, size: 384, ETA: 9 days, 21:42:51 2022-05-20 19:02:55.440 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4190/7393, mem: 8935Mb, iter_time: 0.213s, data_time: 0.005s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 3.134e-03, size: 320, ETA: 9 days, 21:37:15 2022-05-20 19:02:58.828 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4200/7393, mem: 8935Mb, iter_time: 0.338s, data_time: 0.002s, total_loss: 6.7, loss_cls: 5.4, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 3.136e-03, size: 576, ETA: 9 days, 21:35:36 2022-05-20 19:03:02.839 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4210/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.003s, total_loss: 7.2, loss_cls: 5.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 3.139e-03, size: 544, ETA: 9 days, 21:35:56 2022-05-20 19:03:06.824 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4220/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.002s, total_loss: 7.6, loss_cls: 6.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 3.142e-03, size: 672, ETA: 9 days, 21:36:12 2022-05-20 19:03:11.361 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4230/7393, mem: 8935Mb, iter_time: 0.453s, data_time: 0.001s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 3.144e-03, size: 608, ETA: 9 days, 21:38:12 2022-05-20 19:03:15.805 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4240/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.003s, total_loss: 11.0, loss_cls: 9.4, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 1.4, lr: 3.147e-03, size: 704, ETA: 9 days, 21:39:55 2022-05-20 19:03:20.261 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4250/7393, mem: 8935Mb, iter_time: 0.445s, data_time: 0.003s, total_loss: 9.4, loss_cls: 8.3, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 3.150e-03, size: 480, ETA: 9 days, 21:41:39 2022-05-20 19:03:22.822 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4260/7393, mem: 8935Mb, iter_time: 0.255s, data_time: 0.002s, total_loss: 9.3, loss_cls: 8.2, loss_iou: 0.3, loss_dfl: 1.0, loss_l1: 0.6, lr: 3.152e-03, size: 320, ETA: 9 days, 21:37:25 2022-05-20 19:03:24.979 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4270/7393, mem: 8935Mb, iter_time: 0.215s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.9, lr: 3.155e-03, size: 320, ETA: 9 days, 21:31:54 2022-05-20 19:03:28.345 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4280/7393, mem: 8935Mb, iter_time: 0.335s, data_time: 0.007s, total_loss: 11.1, loss_cls: 9.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.9, lr: 3.158e-03, size: 576, ETA: 9 days, 21:30:10 2022-05-20 19:03:32.112 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4290/7393, mem: 8935Mb, iter_time: 0.376s, data_time: 0.002s, total_loss: 11.9, loss_cls: 10.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.161e-03, size: 512, ETA: 9 days, 21:29:45 2022-05-20 19:03:35.139 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4300/7393, mem: 8935Mb, iter_time: 0.302s, data_time: 0.003s, total_loss: 14.4, loss_cls: 11.8, loss_iou: 0.6, loss_dfl: 2.1, loss_l1: 2.6, lr: 3.163e-03, size: 448, ETA: 9 days, 21:26:59 2022-05-20 19:03:40.241 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4310/7393, mem: 8935Mb, iter_time: 0.509s, data_time: 0.002s, total_loss: 17.5, loss_cls: 14.9, loss_iou: 0.7, loss_dfl: 1.9, loss_l1: 3.1, lr: 3.166e-03, size: 704, ETA: 9 days, 21:30:45 2022-05-20 19:03:44.598 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4320/7393, mem: 8935Mb, iter_time: 0.435s, data_time: 0.001s, total_loss: 17.1, loss_cls: 14.5, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 2.2, lr: 3.169e-03, size: 448, ETA: 9 days, 21:32:11 2022-05-20 19:03:47.333 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4330/7393, mem: 8935Mb, iter_time: 0.273s, data_time: 0.002s, total_loss: 13.3, loss_cls: 10.7, loss_iou: 0.6, loss_dfl: 2.1, loss_l1: 2.4, lr: 3.171e-03, size: 480, ETA: 9 days, 21:28:31 2022-05-20 19:03:49.969 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4340/7393, mem: 8935Mb, iter_time: 0.263s, data_time: 0.003s, total_loss: 14.4, loss_cls: 12.1, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.174e-03, size: 320, ETA: 9 days, 21:24:32 2022-05-20 19:03:53.478 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4350/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.006s, total_loss: 12.5, loss_cls: 10.1, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.5, lr: 3.177e-03, size: 768, ETA: 9 days, 21:23:18 2022-05-20 19:03:59.168 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4360/7393, mem: 8935Mb, iter_time: 0.568s, data_time: 0.002s, total_loss: 11.5, loss_cls: 8.9, loss_iou: 0.6, loss_dfl: 2.0, loss_l1: 2.0, lr: 3.179e-03, size: 672, ETA: 9 days, 21:28:54 2022-05-20 19:04:04.083 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4370/7393, mem: 8935Mb, iter_time: 0.491s, data_time: 0.001s, total_loss: 17.2, loss_cls: 14.8, loss_iou: 0.6, loss_dfl: 2.0, loss_l1: 2.0, lr: 3.182e-03, size: 704, ETA: 9 days, 21:32:04 2022-05-20 19:04:08.922 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4380/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.001s, total_loss: 13.5, loss_cls: 10.9, loss_iou: 0.7, loss_dfl: 2.1, loss_l1: 2.2, lr: 3.185e-03, size: 608, ETA: 9 days, 21:35:00 2022-05-20 19:04:12.526 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4390/7393, mem: 8935Mb, iter_time: 0.360s, data_time: 0.002s, total_loss: 11.6, loss_cls: 9.0, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 2.0, lr: 3.188e-03, size: 448, ETA: 9 days, 21:34:04 2022-05-20 19:04:15.784 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4400/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.003s, total_loss: 16.2, loss_cls: 13.7, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 1.7, lr: 3.190e-03, size: 640, ETA: 9 days, 21:32:02 2022-05-20 19:04:19.646 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4410/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.002s, total_loss: 13.0, loss_cls: 10.6, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.5, lr: 3.193e-03, size: 352, ETA: 9 days, 21:31:54 2022-05-20 19:04:22.467 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4420/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.004s, total_loss: 10.9, loss_cls: 8.4, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 1.6, lr: 3.196e-03, size: 608, ETA: 9 days, 21:28:32 2022-05-20 19:04:26.454 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4430/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.1, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.6, lr: 3.198e-03, size: 544, ETA: 9 days, 21:28:48 2022-05-20 19:04:29.572 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4440/7393, mem: 8935Mb, iter_time: 0.311s, data_time: 0.002s, total_loss: 11.5, loss_cls: 9.2, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.201e-03, size: 352, ETA: 9 days, 21:26:21 2022-05-20 19:04:32.084 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4450/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.004s, total_loss: 11.3, loss_cls: 8.9, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.204e-03, size: 416, ETA: 9 days, 21:22:02 2022-05-20 19:04:35.060 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4460/7393, mem: 8935Mb, iter_time: 0.296s, data_time: 0.004s, total_loss: 12.1, loss_cls: 9.9, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.207e-03, size: 384, ETA: 9 days, 21:19:09 2022-05-20 19:04:37.536 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4470/7393, mem: 8935Mb, iter_time: 0.247s, data_time: 0.002s, total_loss: 12.9, loss_cls: 10.7, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.209e-03, size: 352, ETA: 9 days, 21:14:43 2022-05-20 19:04:40.282 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4480/7393, mem: 8935Mb, iter_time: 0.273s, data_time: 0.003s, total_loss: 13.4, loss_cls: 11.0, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 3.2, lr: 3.212e-03, size: 512, ETA: 9 days, 21:11:08 2022-05-20 19:04:43.418 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4490/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.006s, total_loss: 10.6, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.215e-03, size: 384, ETA: 9 days, 21:08:45 2022-05-20 19:04:46.405 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4500/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.006s, total_loss: 10.3, loss_cls: 8.0, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.8, lr: 3.217e-03, size: 576, ETA: 9 days, 21:05:56 2022-05-20 19:04:50.787 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4510/7393, mem: 8935Mb, iter_time: 0.437s, data_time: 0.004s, total_loss: 10.2, loss_cls: 7.7, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.6, lr: 3.220e-03, size: 672, ETA: 9 days, 21:07:25 2022-05-20 19:04:55.066 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4520/7393, mem: 8935Mb, iter_time: 0.427s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.4, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.223e-03, size: 512, ETA: 9 days, 21:08:36 2022-05-20 19:04:57.810 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4530/7393, mem: 8935Mb, iter_time: 0.274s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.3, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.5, lr: 3.225e-03, size: 352, ETA: 9 days, 21:05:03 2022-05-20 19:05:00.873 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4540/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.004s, total_loss: 10.5, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.8, loss_l1: 1.4, lr: 3.228e-03, size: 672, ETA: 9 days, 21:02:28 2022-05-20 19:05:05.606 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4550/7393, mem: 8935Mb, iter_time: 0.473s, data_time: 0.002s, total_loss: 11.8, loss_cls: 9.7, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.231e-03, size: 608, ETA: 9 days, 21:05:02 2022-05-20 19:05:09.659 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4560/7393, mem: 8935Mb, iter_time: 0.405s, data_time: 0.001s, total_loss: 10.0, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.8, lr: 3.234e-03, size: 608, ETA: 9 days, 21:05:31 2022-05-20 19:05:13.888 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4570/7393, mem: 8935Mb, iter_time: 0.422s, data_time: 0.003s, total_loss: 10.1, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.8, lr: 3.236e-03, size: 640, ETA: 9 days, 21:06:33 2022-05-20 19:05:17.848 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4580/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.003s, total_loss: 10.5, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.239e-03, size: 480, ETA: 9 days, 21:06:45 2022-05-20 19:05:20.949 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4590/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.004s, total_loss: 10.8, loss_cls: 8.5, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.7, lr: 3.242e-03, size: 544, ETA: 9 days, 21:04:18 2022-05-20 19:05:25.036 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4600/7393, mem: 8935Mb, iter_time: 0.408s, data_time: 0.002s, total_loss: 10.1, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.8, loss_l1: 2.4, lr: 3.244e-03, size: 704, ETA: 9 days, 21:04:53 2022-05-20 19:05:29.400 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4610/7393, mem: 8935Mb, iter_time: 0.435s, data_time: 0.002s, total_loss: 10.1, loss_cls: 7.8, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.5, lr: 3.247e-03, size: 416, ETA: 9 days, 21:06:17 2022-05-20 19:05:32.048 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4620/7393, mem: 8935Mb, iter_time: 0.264s, data_time: 0.002s, total_loss: 10.8, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.5, lr: 3.250e-03, size: 512, ETA: 9 days, 21:02:27 2022-05-20 19:05:35.391 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4630/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.253e-03, size: 544, ETA: 9 days, 21:00:46 2022-05-20 19:05:38.537 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4640/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.1, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.8, lr: 3.255e-03, size: 352, ETA: 9 days, 20:58:28 2022-05-20 19:05:40.993 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4650/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.005s, total_loss: 9.4, loss_cls: 7.0, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.7, lr: 3.258e-03, size: 320, ETA: 9 days, 20:54:04 2022-05-20 19:05:43.997 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4660/7393, mem: 8935Mb, iter_time: 0.299s, data_time: 0.009s, total_loss: 9.2, loss_cls: 6.9, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.261e-03, size: 384, ETA: 9 days, 20:51:21 2022-05-20 19:05:46.903 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4670/7393, mem: 8935Mb, iter_time: 0.289s, data_time: 0.005s, total_loss: 9.9, loss_cls: 7.5, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.4, lr: 3.263e-03, size: 544, ETA: 9 days, 20:48:19 2022-05-20 19:05:50.354 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4680/7393, mem: 8935Mb, iter_time: 0.344s, data_time: 0.004s, total_loss: 9.8, loss_cls: 7.5, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 2.0, lr: 3.266e-03, size: 480, ETA: 9 days, 20:46:58 2022-05-20 19:05:53.618 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4690/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.004s, total_loss: 9.8, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.269e-03, size: 544, ETA: 9 days, 20:45:03 2022-05-20 19:05:57.838 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4700/7393, mem: 8935Mb, iter_time: 0.421s, data_time: 0.003s, total_loss: 11.1, loss_cls: 8.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.271e-03, size: 704, ETA: 9 days, 20:46:02 2022-05-20 19:06:03.277 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4710/7393, mem: 8935Mb, iter_time: 0.543s, data_time: 0.002s, total_loss: 9.6, loss_cls: 6.9, loss_iou: 0.6, loss_dfl: 2.1, loss_l1: 2.7, lr: 3.274e-03, size: 768, ETA: 9 days, 20:50:45 2022-05-20 19:06:08.033 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4720/7393, mem: 8935Mb, iter_time: 0.475s, data_time: 0.001s, total_loss: 10.7, loss_cls: 8.4, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.277e-03, size: 384, ETA: 9 days, 20:53:22 2022-05-20 19:06:10.890 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4730/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.005s, total_loss: 11.0, loss_cls: 8.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.1, lr: 3.280e-03, size: 608, ETA: 9 days, 20:50:13 2022-05-20 19:06:15.316 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4740/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.8, lr: 3.282e-03, size: 672, ETA: 9 days, 20:51:50 2022-05-20 19:06:19.951 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4750/7393, mem: 8935Mb, iter_time: 0.463s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.1, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.285e-03, size: 608, ETA: 9 days, 20:54:05 2022-05-20 19:06:24.293 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4760/7393, mem: 8935Mb, iter_time: 0.434s, data_time: 0.002s, total_loss: 10.3, loss_cls: 7.9, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 1.6, lr: 3.288e-03, size: 672, ETA: 9 days, 20:55:26 2022-05-20 19:06:28.596 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4770/7393, mem: 8935Mb, iter_time: 0.430s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.0, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 2.0, lr: 3.290e-03, size: 480, ETA: 9 days, 20:56:40 2022-05-20 19:06:32.046 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4780/7393, mem: 8935Mb, iter_time: 0.344s, data_time: 0.002s, total_loss: 10.2, loss_cls: 7.9, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.6, lr: 3.293e-03, size: 672, ETA: 9 days, 20:55:20 2022-05-20 19:06:36.830 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4790/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.5, loss_iou: 0.6, loss_dfl: 2.0, loss_l1: 3.0, lr: 3.296e-03, size: 672, ETA: 9 days, 20:58:01 2022-05-20 19:06:41.092 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4800/7393, mem: 8935Mb, iter_time: 0.426s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.299e-03, size: 512, ETA: 9 days, 20:59:07 2022-05-20 19:06:44.331 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4810/7393, mem: 8935Mb, iter_time: 0.322s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 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8935Mb, iter_time: 0.450s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.3, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.312e-03, size: 480, ETA: 9 days, 20:55:48 2022-05-20 19:07:02.897 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4860/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.0, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 1.8, lr: 3.315e-03, size: 640, ETA: 9 days, 20:54:21 2022-05-20 19:07:06.710 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4870/7393, mem: 8935Mb, iter_time: 0.381s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.4, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.317e-03, size: 416, ETA: 9 days, 20:54:06 2022-05-20 19:07:09.084 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4880/7393, mem: 8935Mb, iter_time: 0.237s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.3, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.6, lr: 3.320e-03, size: 384, ETA: 9 days, 20:49:33 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loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.331e-03, size: 608, ETA: 9 days, 20:43:40 2022-05-20 19:07:27.141 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4930/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 10.0, loss_cls: 7.5, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 1.8, lr: 3.334e-03, size: 704, ETA: 9 days, 20:45:19 2022-05-20 19:07:31.453 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4940/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.1, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.336e-03, size: 416, ETA: 9 days, 20:46:34 2022-05-20 19:07:34.241 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4950/7393, mem: 8935Mb, iter_time: 0.278s, data_time: 0.002s, total_loss: 10.3, loss_cls: 7.9, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 2.3, lr: 3.339e-03, size: 544, ETA: 9 days, 20:43:16 2022-05-20 19:07:38.076 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4960/7393, mem: 8935Mb, iter_time: 0.383s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 3.342e-03, size: 640, ETA: 9 days, 20:43:06 2022-05-20 19:07:41.798 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4970/7393, mem: 8935Mb, iter_time: 0.372s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.1, loss_iou: 0.6, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.345e-03, size: 416, ETA: 9 days, 20:42:36 2022-05-20 19:07:44.648 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4980/7393, mem: 8935Mb, iter_time: 0.284s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.3, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 1.5, lr: 3.347e-03, size: 576, ETA: 9 days, 20:39:30 2022-05-20 19:07:48.924 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 4990/7393, mem: 8935Mb, iter_time: 0.427s, data_time: 0.002s, total_loss: 10.2, loss_cls: 7.9, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.3, lr: 3.350e-03, size: 704, ETA: 9 days, 20:40:38 2022-05-20 19:07:54.422 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5000/7393, mem: 8935Mb, iter_time: 0.549s, data_time: 0.002s, total_loss: 11.1, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.9, loss_l1: 2.1, lr: 3.353e-03, size: 768, ETA: 9 days, 20:45:24 2022-05-20 19:07:59.054 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5010/7393, mem: 8935Mb, iter_time: 0.463s, data_time: 0.001s, total_loss: 9.9, loss_cls: 7.7, loss_iou: 0.6, loss_dfl: 1.6, loss_l1: 1.4, lr: 3.355e-03, size: 320, ETA: 9 days, 20:47:36 2022-05-20 19:08:02.311 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5020/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.002s, total_loss: 10.4, loss_cls: 7.9, loss_iou: 0.6, loss_dfl: 2.0, loss_l1: 2.4, lr: 3.358e-03, size: 768, ETA: 9 days, 20:45:42 2022-05-20 19:08:07.117 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5030/7393, mem: 8935Mb, iter_time: 0.480s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.1, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.8, lr: 3.361e-03, size: 416, ETA: 9 days, 20:48:25 2022-05-20 19:08:09.598 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5040/7393, mem: 8935Mb, iter_time: 0.247s, data_time: 0.004s, total_loss: 10.0, loss_cls: 7.7, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.5, lr: 3.363e-03, size: 416, ETA: 9 days, 20:44:13 2022-05-20 19:08:12.528 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5050/7393, mem: 8935Mb, iter_time: 0.292s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.366e-03, size: 512, ETA: 9 days, 20:41:22 2022-05-20 19:08:15.627 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5060/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.006s, total_loss: 10.5, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.8, loss_l1: 2.1, lr: 3.369e-03, size: 320, ETA: 9 days, 20:39:01 2022-05-20 19:08:18.987 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5070/7393, mem: 8935Mb, iter_time: 0.335s, data_time: 0.004s, total_loss: 10.0, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 3.372e-03, size: 768, ETA: 9 days, 20:37:26 2022-05-20 19:08:24.290 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5080/7393, mem: 8935Mb, iter_time: 0.530s, data_time: 0.002s, total_loss: 11.6, loss_cls: 9.4, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.1, lr: 3.374e-03, size: 576, ETA: 9 days, 20:41:36 2022-05-20 19:08:28.517 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5090/7393, mem: 8935Mb, iter_time: 0.422s, data_time: 0.001s, total_loss: 10.7, loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.377e-03, size: 704, ETA: 9 days, 20:42:35 2022-05-20 19:08:33.669 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5100/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.380e-03, size: 672, ETA: 9 days, 20:46:18 2022-05-20 19:08:37.956 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5110/7393, mem: 8935Mb, iter_time: 0.428s, data_time: 0.001s, total_loss: 9.7, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.5, lr: 3.382e-03, size: 512, ETA: 9 days, 20:47:27 2022-05-20 19:08:41.949 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5120/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.003s, total_loss: 9.8, loss_cls: 7.5, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 2.4, lr: 3.385e-03, size: 736, ETA: 9 days, 20:47:44 2022-05-20 19:08:47.535 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5130/7393, mem: 8935Mb, iter_time: 0.558s, data_time: 0.001s, total_loss: 11.0, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.8, loss_l1: 3.4, lr: 3.388e-03, size: 736, ETA: 9 days, 20:52:43 2022-05-20 19:08:51.969 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5140/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.001s, total_loss: 10.0, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.8, lr: 3.391e-03, size: 352, ETA: 9 days, 20:54:18 2022-05-20 19:08:55.183 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5150/7393, mem: 8935Mb, iter_time: 0.320s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.0, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.393e-03, size: 768, ETA: 9 days, 20:52:17 2022-05-20 19:08:59.910 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5160/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.001s, total_loss: 10.0, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.396e-03, size: 352, ETA: 9 days, 20:54:44 2022-05-20 19:09:01.887 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5170/7393, mem: 8935Mb, iter_time: 0.197s, data_time: 0.003s, total_loss: 10.0, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.399e-03, size: 352, ETA: 9 days, 20:49:07 2022-05-20 19:09:05.269 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5180/7393, mem: 8935Mb, iter_time: 0.337s, data_time: 0.006s, total_loss: 9.9, loss_cls: 7.7, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.9, lr: 3.401e-03, size: 672, ETA: 9 days, 20:47:36 2022-05-20 19:09:10.421 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5190/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.5, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 2.0, lr: 3.404e-03, size: 768, ETA: 9 days, 20:51:17 2022-05-20 19:09:15.064 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5200/7393, mem: 8935Mb, iter_time: 0.464s, data_time: 0.001s, total_loss: 9.8, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.407e-03, size: 320, ETA: 9 days, 20:53:28 2022-05-20 19:09:17.516 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5210/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.409e-03, size: 544, ETA: 9 days, 20:49:15 2022-05-20 19:09:21.416 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5220/7393, mem: 8935Mb, iter_time: 0.389s, data_time: 0.005s, total_loss: 10.3, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.8, loss_l1: 1.6, lr: 3.412e-03, size: 608, ETA: 9 days, 20:49:15 2022-05-20 19:09:25.602 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5230/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.002s, total_loss: 10.1, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.415e-03, size: 608, ETA: 9 days, 20:50:06 2022-05-20 19:09:29.501 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5240/7393, mem: 8935Mb, iter_time: 0.389s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.4, loss_iou: 0.6, loss_dfl: 1.6, loss_l1: 1.0, lr: 3.418e-03, size: 544, ETA: 9 days, 20:50:07 2022-05-20 19:09:33.019 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5250/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.420e-03, size: 544, ETA: 9 days, 20:49:01 2022-05-20 19:09:36.869 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5260/7393, mem: 8935Mb, iter_time: 0.384s, data_time: 0.002s, total_loss: 12.0, loss_cls: 9.8, loss_iou: 0.5, loss_dfl: 1.8, loss_l1: 2.0, lr: 3.423e-03, size: 640, ETA: 9 days, 20:48:53 2022-05-20 19:09:40.931 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5270/7393, mem: 8935Mb, iter_time: 0.405s, data_time: 0.002s, total_loss: 10.1, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 2.0, loss_l1: 1.7, lr: 3.426e-03, size: 480, ETA: 9 days, 20:49:22 2022-05-20 19:09:44.540 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5280/7393, mem: 8935Mb, iter_time: 0.360s, data_time: 0.002s, total_loss: 11.4, loss_cls: 9.3, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.5, lr: 3.428e-03, size: 704, ETA: 9 days, 20:48:32 2022-05-20 19:09:49.507 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5290/7393, mem: 8935Mb, iter_time: 0.496s, data_time: 0.001s, total_loss: 9.8, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.431e-03, size: 640, ETA: 9 days, 20:51:39 2022-05-20 19:09:53.484 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5300/7393, mem: 8935Mb, iter_time: 0.397s, data_time: 0.003s, total_loss: 9.4, loss_cls: 7.2, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.434e-03, size: 480, ETA: 9 days, 20:51:53 2022-05-20 19:09:56.689 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5310/7393, mem: 8935Mb, iter_time: 0.320s, data_time: 0.004s, total_loss: 9.0, loss_cls: 6.8, loss_iou: 0.6, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.436e-03, size: 576, ETA: 9 days, 20:49:53 2022-05-20 19:10:01.059 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5320/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.439e-03, size: 736, ETA: 9 days, 20:51:16 2022-05-20 19:10:05.872 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5330/7393, mem: 8935Mb, iter_time: 0.481s, data_time: 0.002s, total_loss: 10.8, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.442e-03, size: 480, ETA: 9 days, 20:53:55 2022-05-20 19:10:08.588 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5340/7393, mem: 8935Mb, iter_time: 0.271s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.445e-03, size: 416, ETA: 9 days, 20:50:30 2022-05-20 19:10:11.126 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5350/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.0, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.447e-03, size: 384, ETA: 9 days, 20:46:35 2022-05-20 19:10:13.576 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5360/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.003s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.450e-03, size: 320, ETA: 9 days, 20:42:24 2022-05-20 19:10:16.875 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5370/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.008s, total_loss: 10.7, loss_cls: 8.3, loss_iou: 0.6, loss_dfl: 2.0, loss_l1: 2.0, lr: 3.453e-03, size: 576, ETA: 9 days, 20:40:41 2022-05-20 19:10:20.208 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5380/7393, mem: 8935Mb, iter_time: 0.332s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 3.455e-03, size: 384, ETA: 9 days, 20:39:04 2022-05-20 19:10:22.892 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5390/7393, mem: 8935Mb, iter_time: 0.267s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.1, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.458e-03, size: 576, ETA: 9 days, 20:35:35 2022-05-20 19:10:27.383 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5400/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.004s, total_loss: 10.5, loss_cls: 8.2, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 2.0, lr: 3.461e-03, size: 736, ETA: 9 days, 20:37:18 2022-05-20 19:10:32.614 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5410/7393, mem: 8935Mb, iter_time: 0.523s, data_time: 0.002s, total_loss: 10.3, loss_cls: 7.9, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.6, lr: 3.464e-03, size: 640, ETA: 9 days, 20:41:09 2022-05-20 19:10:36.412 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5420/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.466e-03, size: 448, ETA: 9 days, 20:40:53 2022-05-20 19:10:39.307 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5430/7393, mem: 8935Mb, iter_time: 0.289s, data_time: 0.002s, total_loss: 10.3, loss_cls: 7.9, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.8, lr: 3.469e-03, size: 544, ETA: 9 days, 20:38:01 2022-05-20 19:10:43.535 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5440/7393, mem: 8935Mb, iter_time: 0.422s, data_time: 0.003s, total_loss: 10.6, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.6, lr: 3.472e-03, size: 704, ETA: 9 days, 20:38:58 2022-05-20 19:10:47.724 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5450/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.474e-03, size: 352, ETA: 9 days, 20:39:50 2022-05-20 19:10:50.108 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5460/7393, mem: 8935Mb, iter_time: 0.237s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.0, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.6, lr: 3.477e-03, size: 512, ETA: 9 days, 20:35:29 2022-05-20 19:10:53.982 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5470/7393, mem: 8935Mb, iter_time: 0.387s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.480e-03, size: 672, ETA: 9 days, 20:35:26 2022-05-20 19:10:58.964 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5480/7393, mem: 8935Mb, iter_time: 0.498s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.1, loss_iou: 0.6, loss_dfl: 2.1, loss_l1: 2.9, lr: 3.482e-03, size: 704, ETA: 9 days, 20:38:33 2022-05-20 19:11:03.692 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5490/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.3, loss_iou: 0.6, loss_dfl: 1.8, loss_l1: 1.4, lr: 3.485e-03, size: 576, ETA: 9 days, 20:40:56 2022-05-20 19:11:07.768 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5500/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.002s, total_loss: 11.0, loss_cls: 9.0, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.0, lr: 3.488e-03, size: 640, ETA: 9 days, 20:41:28 2022-05-20 19:11:11.474 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5510/7393, mem: 8935Mb, iter_time: 0.369s, data_time: 0.003s, total_loss: 9.9, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.9, lr: 3.491e-03, size: 384, ETA: 9 days, 20:40:54 2022-05-20 19:11:14.007 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5520/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.004s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 3.493e-03, size: 512, ETA: 9 days, 20:37:01 2022-05-20 19:11:17.113 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5530/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.496e-03, size: 416, ETA: 9 days, 20:34:46 2022-05-20 19:11:20.245 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5540/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.007s, total_loss: 10.1, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.8, loss_l1: 1.6, lr: 3.499e-03, size: 544, ETA: 9 days, 20:32:36 2022-05-20 19:11:24.595 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5550/7393, mem: 8935Mb, iter_time: 0.434s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.501e-03, size: 768, ETA: 9 days, 20:33:54 2022-05-20 19:11:30.291 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5560/7393, mem: 8935Mb, iter_time: 0.569s, data_time: 0.001s, total_loss: 10.0, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.504e-03, size: 704, ETA: 9 days, 20:39:02 2022-05-20 19:11:35.138 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5570/7393, mem: 8935Mb, iter_time: 0.484s, data_time: 0.001s, total_loss: 10.7, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 3.507e-03, size: 608, ETA: 9 days, 20:41:44 2022-05-20 19:11:38.826 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5580/7393, mem: 8935Mb, iter_time: 0.368s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 3.510e-03, size: 480, ETA: 9 days, 20:41:09 2022-05-20 19:11:41.700 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5590/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.512e-03, size: 448, ETA: 9 days, 20:38:16 2022-05-20 19:11:45.489 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5600/7393, mem: 8935Mb, iter_time: 0.378s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 3.515e-03, size: 736, ETA: 9 days, 20:37:58 2022-05-20 19:11:50.489 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5610/7393, mem: 8935Mb, iter_time: 0.500s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.5, lr: 3.518e-03, size: 576, ETA: 9 days, 20:41:06 2022-05-20 19:11:53.887 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5620/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 0.9, lr: 3.520e-03, size: 416, ETA: 9 days, 20:39:42 2022-05-20 19:11:56.721 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5630/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.007s, total_loss: 10.3, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.523e-03, size: 448, ETA: 9 days, 20:36:42 2022-05-20 19:11:59.358 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5640/7393, mem: 8935Mb, iter_time: 0.263s, data_time: 0.004s, total_loss: 10.0, loss_cls: 7.9, loss_iou: 0.5, 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days, 20:15:29 2022-05-20 19:12:23.655 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5720/7393, mem: 8935Mb, iter_time: 0.330s, data_time: 0.003s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 0.9, lr: 3.547e-03, size: 480, ETA: 9 days, 20:13:51 2022-05-20 19:12:27.218 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5730/7393, mem: 8935Mb, iter_time: 0.354s, data_time: 0.004s, total_loss: 10.7, loss_cls: 8.7, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.6, lr: 3.550e-03, size: 576, ETA: 9 days, 20:12:53 2022-05-20 19:12:30.769 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5740/7393, mem: 8935Mb, iter_time: 0.354s, data_time: 0.004s, total_loss: 9.8, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.0, lr: 3.553e-03, size: 480, ETA: 9 days, 20:11:57 2022-05-20 19:12:34.114 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5750/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.6, lr: 3.556e-03, size: 608, ETA: 9 days, 20:10:26 2022-05-20 19:12:38.311 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5760/7393, mem: 8935Mb, iter_time: 0.419s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.558e-03, size: 608, ETA: 9 days, 20:11:18 2022-05-20 19:12:42.343 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5770/7393, mem: 8935Mb, iter_time: 0.403s, data_time: 0.002s, total_loss: 11.1, loss_cls: 9.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.561e-03, size: 576, ETA: 9 days, 20:11:43 2022-05-20 19:12:45.675 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5780/7393, mem: 8935Mb, iter_time: 0.332s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 3.564e-03, size: 416, ETA: 9 days, 20:10:09 2022-05-20 19:12:48.069 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5790/7393, mem: 8935Mb, iter_time: 0.239s, data_time: 0.004s, total_loss: 9.6, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.566e-03, size: 352, ETA: 9 days, 20:06:00 2022-05-20 19:12:51.091 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5800/7393, mem: 8935Mb, iter_time: 0.301s, data_time: 0.006s, total_loss: 9.9, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.569e-03, size: 512, ETA: 9 days, 20:03:35 2022-05-20 19:12:54.303 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5810/7393, mem: 8935Mb, iter_time: 0.320s, data_time: 0.006s, total_loss: 10.2, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.0, lr: 3.572e-03, size: 480, ETA: 9 days, 20:01:42 2022-05-20 19:12:58.132 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5820/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 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loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.604e-03, size: 576, ETA: 9 days, 20:14:59 2022-05-20 19:13:50.017 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5940/7393, mem: 8935Mb, iter_time: 0.429s, data_time: 0.002s, total_loss: 10.8, loss_cls: 8.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 2.1, lr: 3.607e-03, size: 704, ETA: 9 days, 20:16:06 2022-05-20 19:13:54.503 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5950/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.001s, total_loss: 10.0, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.5, lr: 3.610e-03, size: 512, ETA: 9 days, 20:17:46 2022-05-20 19:13:57.720 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5960/7393, mem: 8935Mb, iter_time: 0.321s, data_time: 0.002s, total_loss: 10.0, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.612e-03, size: 544, ETA: 9 days, 20:15:55 2022-05-20 19:14:00.843 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5970/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.002s, total_loss: 10.1, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.615e-03, size: 416, ETA: 9 days, 20:13:48 2022-05-20 19:14:04.179 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5980/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.003s, total_loss: 11.0, loss_cls: 8.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.618e-03, size: 672, ETA: 9 days, 20:12:17 2022-05-20 19:14:08.600 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 5990/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.002s, total_loss: 10.0, loss_cls: 7.6, loss_iou: 0.6, loss_dfl: 1.9, loss_l1: 1.5, lr: 3.620e-03, size: 544, ETA: 9 days, 20:13:45 2022-05-20 19:14:12.627 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6000/7393, mem: 8935Mb, iter_time: 0.402s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.6, lr: 3.623e-03, size: 704, ETA: 9 days, 20:14:08 2022-05-20 19:14:17.160 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6010/7393, mem: 8935Mb, iter_time: 0.453s, data_time: 0.002s, total_loss: 11.5, loss_cls: 9.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 2.1, lr: 3.626e-03, size: 480, ETA: 9 days, 20:15:54 2022-05-20 19:14:20.555 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6020/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.629e-03, size: 640, ETA: 9 days, 20:14:33 2022-05-20 19:14:24.727 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6030/7393, mem: 8935Mb, iter_time: 0.417s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.5, loss_iou: 0.6, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.631e-03, size: 544, ETA: 9 days, 20:15:20 2022-05-20 19:14:28.065 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6040/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 3.634e-03, size: 448, ETA: 9 days, 20:13:50 2022-05-20 19:14:31.479 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6050/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.004s, total_loss: 9.9, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.8, lr: 3.637e-03, size: 672, ETA: 9 days, 20:12:30 2022-05-20 19:14:35.522 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6060/7393, mem: 8935Mb, iter_time: 0.403s, data_time: 0.002s, total_loss: 11.0, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 3.639e-03, size: 416, ETA: 9 days, 20:12:53 2022-05-20 19:14:37.875 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6070/7393, mem: 8935Mb, iter_time: 0.234s, data_time: 0.005s, total_loss: 8.8, loss_cls: 6.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.642e-03, size: 320, ETA: 9 days, 20:08:42 2022-05-20 19:14:40.217 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6080/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.006s, total_loss: 9.5, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.645e-03, size: 320, ETA: 9 days, 20:04:28 2022-05-20 19:14:43.293 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6090/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.004s, total_loss: 10.7, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.8, lr: 3.648e-03, size: 512, ETA: 9 days, 20:02:15 2022-05-20 19:14:46.734 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6100/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.004s, total_loss: 10.6, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.6, lr: 3.650e-03, size: 480, ETA: 9 days, 20:01:00 2022-05-20 19:14:50.159 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6110/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.005s, total_loss: 11.0, loss_cls: 9.1, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 3.653e-03, size: 544, ETA: 9 days, 19:59:45 2022-05-20 19:14:53.286 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6120/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 3.656e-03, size: 384, ETA: 9 days, 19:57:41 2022-05-20 19:14:55.766 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6130/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.5, lr: 3.658e-03, size: 416, ETA: 9 days, 19:53:51 2022-05-20 19:14:58.842 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6140/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.004s, total_loss: 10.3, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 3.661e-03, size: 480, ETA: 9 days, 19:51:38 2022-05-20 19:15:01.760 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6150/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.003s, total_loss: 9.6, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 3.664e-03, size: 384, ETA: 9 days, 19:49:01 2022-05-20 19:15:04.561 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6160/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.005s, total_loss: 9.8, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.666e-03, size: 512, ETA: 9 days, 19:46:04 2022-05-20 19:15:08.384 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6170/7393, mem: 8935Mb, iter_time: 0.381s, data_time: 0.003s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.5, lr: 3.669e-03, size: 672, ETA: 9 days, 19:45:55 2022-05-20 19:15:12.392 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6180/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 10.8, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 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loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.3, lr: 3.702e-03, size: 608, ETA: 9 days, 19:30:37 2022-05-20 19:15:53.218 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6300/7393, mem: 8935Mb, iter_time: 0.393s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 3.704e-03, size: 544, ETA: 9 days, 19:30:46 2022-05-20 19:15:56.343 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6310/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.4, lr: 3.707e-03, size: 416, ETA: 9 days, 19:28:45 2022-05-20 19:15:58.848 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6320/7393, mem: 8935Mb, iter_time: 0.249s, data_time: 0.003s, total_loss: 10.2, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.710e-03, size: 416, ETA: 9 days, 19:25:03 2022-05-20 19:16:02.714 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6330/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.1, lr: 3.712e-03, size: 768, ETA: 9 days, 19:25:00 2022-05-20 19:16:07.476 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6340/7393, mem: 8935Mb, iter_time: 0.475s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.715e-03, size: 352, ETA: 9 days, 19:27:21 2022-05-20 19:16:10.063 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6350/7393, mem: 8935Mb, iter_time: 0.258s, data_time: 0.003s, total_loss: 10.9, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.7, lr: 3.718e-03, size: 544, ETA: 9 days, 19:23:54 2022-05-20 19:16:14.193 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6360/7393, mem: 8935Mb, iter_time: 0.412s, data_time: 0.003s, total_loss: 11.2, loss_cls: 9.3, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.1, lr: 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0.362s, data_time: 0.005s, total_loss: 11.0, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 3.731e-03, size: 640, ETA: 9 days, 19:20:00 2022-05-20 19:16:31.750 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6410/7393, mem: 8935Mb, iter_time: 0.376s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.734e-03, size: 352, ETA: 9 days, 19:19:43 2022-05-20 19:16:34.082 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6420/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.005s, total_loss: 9.6, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 3.737e-03, size: 448, ETA: 9 days, 19:15:35 2022-05-20 19:16:37.382 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6430/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.003s, total_loss: 10.9, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 3.739e-03, size: 608, ETA: 9 days, 19:14:03 2022-05-20 19:16:41.933 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6440/7393, mem: 8935Mb, iter_time: 0.454s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.742e-03, size: 704, ETA: 9 days, 19:15:51 2022-05-20 19:16:46.044 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6450/7393, mem: 8935Mb, iter_time: 0.411s, data_time: 0.001s, total_loss: 10.7, loss_cls: 8.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.745e-03, size: 320, ETA: 9 days, 19:16:29 2022-05-20 19:16:49.222 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6460/7393, mem: 8935Mb, iter_time: 0.317s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.5, lr: 3.748e-03, size: 768, ETA: 9 days, 19:14:39 2022-05-20 19:16:54.275 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6470/7393, mem: 8935Mb, iter_time: 0.505s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.750e-03, size: 448, ETA: 9 days, 19:17:46 2022-05-20 19:16:56.693 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6480/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 3.753e-03, size: 320, ETA: 9 days, 19:13:54 2022-05-20 19:16:59.182 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6490/7393, mem: 8935Mb, iter_time: 0.247s, data_time: 0.005s, total_loss: 9.6, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.756e-03, size: 448, ETA: 9 days, 19:10:13 2022-05-20 19:17:02.240 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6500/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.005s, total_loss: 9.2, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.1, lr: 3.758e-03, size: 320, ETA: 9 days, 19:08:04 2022-05-20 19:17:05.948 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6510/7393, mem: 8935Mb, iter_time: 0.370s, data_time: 0.006s, total_loss: 9.8, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.7, lr: 3.761e-03, size: 768, ETA: 9 days, 19:07:37 2022-05-20 19:17:10.833 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6520/7393, mem: 8935Mb, iter_time: 0.488s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.3, lr: 3.764e-03, size: 384, ETA: 9 days, 19:10:18 2022-05-20 19:17:14.186 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6530/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.005s, total_loss: 9.5, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.8, lr: 3.767e-03, size: 768, ETA: 9 days, 19:08:56 2022-05-20 19:17:19.920 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6540/7393, mem: 8935Mb, iter_time: 0.573s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 3.769e-03, size: 704, ETA: 9 days, 19:13:50 2022-05-20 19:17:24.563 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6550/7393, mem: 8935Mb, iter_time: 0.464s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.772e-03, size: 544, ETA: 9 days, 19:15:52 2022-05-20 19:17:27.729 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6560/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.9, lr: 3.775e-03, size: 416, ETA: 9 days, 19:14:01 2022-05-20 19:17:30.351 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6570/7393, mem: 8935Mb, iter_time: 0.261s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.777e-03, size: 416, ETA: 9 days, 19:10:42 2022-05-20 19:17:34.192 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6580/7393, mem: 8935Mb, iter_time: 0.383s, data_time: 0.008s, total_loss: 10.8, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.780e-03, size: 672, ETA: 9 days, 19:10:36 2022-05-20 19:17:38.773 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6590/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.0, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 3.783e-03, size: 576, ETA: 9 days, 19:12:28 2022-05-20 19:17:42.643 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6600/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.2, lr: 3.785e-03, size: 608, ETA: 9 days, 19:12:28 2022-05-20 19:17:47.367 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6610/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.002s, total_loss: 10.1, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.788e-03, size: 768, ETA: 9 days, 19:14:42 2022-05-20 19:17:52.830 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6620/7393, mem: 8935Mb, iter_time: 0.546s, data_time: 0.001s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 3.791e-03, size: 640, ETA: 9 days, 19:18:52 2022-05-20 19:17:56.510 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6630/7393, mem: 8935Mb, iter_time: 0.367s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 3.794e-03, size: 384, ETA: 9 days, 19:18:22 2022-05-20 19:17:59.090 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6640/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.004s, total_loss: 9.4, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 3.796e-03, size: 416, ETA: 9 days, 19:14:58 2022-05-20 19:18:02.407 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6650/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.008s, total_loss: 10.0, loss_cls: 8.1, loss_iou: 0.5, 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days, 19:15:04 2022-05-20 19:18:32.971 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6730/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.004s, total_loss: 11.6, loss_cls: 9.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.9, lr: 3.821e-03, size: 608, ETA: 9 days, 19:12:21 2022-05-20 19:18:36.828 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6740/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.3, lr: 3.823e-03, size: 512, ETA: 9 days, 19:12:18 2022-05-20 19:18:40.843 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6750/7393, mem: 8935Mb, iter_time: 0.401s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.826e-03, size: 736, ETA: 9 days, 19:12:40 2022-05-20 19:18:45.174 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6760/7393, mem: 8935Mb, iter_time: 0.433s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.0, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 3.829e-03, size: 320, ETA: 9 days, 19:13:52 2022-05-20 19:18:47.277 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6770/7393, mem: 8935Mb, iter_time: 0.209s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.1, loss_iou: 0.6, loss_dfl: 1.6, loss_l1: 1.6, lr: 3.831e-03, size: 480, ETA: 9 days, 19:09:16 2022-05-20 19:18:50.327 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6780/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.009s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 3.834e-03, size: 384, ETA: 9 days, 19:07:07 2022-05-20 19:18:53.868 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6790/7393, mem: 8935Mb, iter_time: 0.353s, data_time: 0.003s, total_loss: 10.7, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 3.837e-03, size: 736, ETA: 9 days, 19:06:15 2022-05-20 19:18:58.808 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6800/7393, mem: 8935Mb, iter_time: 0.493s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.0, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 2.1, lr: 3.840e-03, size: 512, ETA: 9 days, 19:09:01 2022-05-20 19:19:02.547 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6810/7393, mem: 8935Mb, iter_time: 0.373s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 3.842e-03, size: 704, ETA: 9 days, 19:08:40 2022-05-20 19:19:07.149 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6820/7393, mem: 8935Mb, iter_time: 0.460s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.7, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.5, lr: 3.845e-03, size: 512, ETA: 9 days, 19:10:33 2022-05-20 19:19:10.030 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6830/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.7, loss_iou: 0.5, 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days, 19:00:03 2022-05-20 19:19:38.000 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6910/7393, mem: 8935Mb, iter_time: 0.391s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 3.869e-03, size: 480, ETA: 9 days, 19:00:10 2022-05-20 19:19:41.526 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6920/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.002s, total_loss: 10.8, loss_cls: 8.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 3.872e-03, size: 672, ETA: 9 days, 18:59:15 2022-05-20 19:19:46.562 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6930/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.875e-03, size: 704, ETA: 9 days, 19:02:15 2022-05-20 19:19:51.270 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6940/7393, mem: 8935Mb, iter_time: 0.470s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.877e-03, size: 544, ETA: 9 days, 19:04:23 2022-05-20 19:19:54.922 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6950/7393, mem: 8935Mb, iter_time: 0.365s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.9, lr: 3.880e-03, size: 608, ETA: 9 days, 19:03:50 2022-05-20 19:19:58.777 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6960/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.5, lr: 3.883e-03, size: 544, ETA: 9 days, 19:03:47 2022-05-20 19:20:02.588 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6970/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.002s, total_loss: 12.0, loss_cls: 10.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.886e-03, size: 640, ETA: 9 days, 19:03:37 2022-05-20 19:20:07.344 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6980/7393, mem: 8935Mb, iter_time: 0.475s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.6, lr: 3.888e-03, size: 704, ETA: 9 days, 19:05:53 2022-05-20 19:20:11.984 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 6990/7393, mem: 8935Mb, iter_time: 0.464s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.5, lr: 3.891e-03, size: 544, ETA: 9 days, 19:07:51 2022-05-20 19:20:14.914 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7000/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 0.9, lr: 3.894e-03, size: 320, ETA: 9 days, 19:05:25 2022-05-20 19:20:17.341 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7010/7393, mem: 8935Mb, iter_time: 0.242s, data_time: 0.003s, total_loss: 9.7, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.896e-03, size: 512, ETA: 9 days, 19:01:43 2022-05-20 19:20:21.048 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7020/7393, mem: 8935Mb, iter_time: 0.370s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.899e-03, size: 640, ETA: 9 days, 19:01:18 2022-05-20 19:20:25.914 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7030/7393, mem: 8935Mb, iter_time: 0.486s, data_time: 0.001s, total_loss: 11.6, loss_cls: 9.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.8, lr: 3.902e-03, size: 768, ETA: 9 days, 19:03:50 2022-05-20 19:20:31.781 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7040/7393, mem: 8935Mb, iter_time: 0.586s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 0.9, lr: 3.905e-03, size: 736, ETA: 9 days, 19:08:55 2022-05-20 19:20:37.232 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7050/7393, mem: 8935Mb, iter_time: 0.545s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 3.907e-03, size: 704, ETA: 9 days, 19:12:56 2022-05-20 19:20:41.854 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7060/7393, mem: 8935Mb, iter_time: 0.462s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.910e-03, size: 544, ETA: 9 days, 19:14:50 2022-05-20 19:20:44.947 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7070/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.1, lr: 3.913e-03, size: 416, ETA: 9 days, 19:12:51 2022-05-20 19:20:47.401 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7080/7393, mem: 8935Mb, iter_time: 0.243s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.915e-03, size: 384, ETA: 9 days, 19:09:12 2022-05-20 19:20:49.992 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7090/7393, mem: 8935Mb, iter_time: 0.258s, data_time: 0.006s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.918e-03, size: 320, ETA: 9 days, 19:05:56 2022-05-20 19:20:52.791 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7100/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.008s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 2.0, lr: 3.921e-03, size: 416, ETA: 9 days, 19:03:12 2022-05-20 19:20:56.629 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7110/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.008s, total_loss: 10.4, loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.9, lr: 3.923e-03, size: 704, ETA: 9 days, 19:03:05 2022-05-20 19:21:01.041 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7120/7393, mem: 8935Mb, iter_time: 0.441s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 3.926e-03, size: 448, ETA: 9 days, 19:04:27 2022-05-20 19:21:03.531 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7130/7393, mem: 8935Mb, iter_time: 0.248s, data_time: 0.004s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.5, lr: 3.929e-03, size: 352, ETA: 9 days, 19:00:57 2022-05-20 19:21:06.393 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7140/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 3.932e-03, size: 576, ETA: 9 days, 18:58:23 2022-05-20 19:21:10.465 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7150/7393, mem: 8935Mb, iter_time: 0.406s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.934e-03, size: 640, ETA: 9 days, 18:58:53 2022-05-20 19:21:14.906 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7160/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.937e-03, size: 640, ETA: 9 days, 19:00:19 2022-05-20 19:21:18.781 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7170/7393, mem: 8935Mb, iter_time: 0.387s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.940e-03, size: 448, ETA: 9 days, 19:00:20 2022-05-20 19:21:21.943 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7180/7393, mem: 8935Mb, iter_time: 0.315s, data_time: 0.003s, total_loss: 11.3, loss_cls: 9.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 3.942e-03, size: 608, ETA: 9 days, 18:58:32 2022-05-20 19:21:26.588 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7190/7393, mem: 8935Mb, iter_time: 0.464s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 3.945e-03, size: 736, ETA: 9 days, 19:00:29 2022-05-20 19:21:31.009 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7200/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.948e-03, size: 320, ETA: 9 days, 19:01:53 2022-05-20 19:21:33.637 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7210/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 3.950e-03, size: 640, ETA: 9 days, 18:58:44 2022-05-20 19:21:38.647 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7220/7393, mem: 8935Mb, iter_time: 0.500s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 3.953e-03, size: 768, ETA: 9 days, 19:01:36 2022-05-20 19:21:44.158 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7230/7393, mem: 8935Mb, iter_time: 0.551s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 3.956e-03, size: 640, ETA: 9 days, 19:05:43 2022-05-20 19:21:48.478 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7240/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.003s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.959e-03, size: 608, ETA: 9 days, 19:06:51 2022-05-20 19:21:52.202 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7250/7393, mem: 8935Mb, iter_time: 0.372s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.961e-03, size: 480, ETA: 9 days, 19:06:28 2022-05-20 19:21:55.549 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7260/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.004s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 3.964e-03, size: 576, ETA: 9 days, 19:05:09 2022-05-20 19:21:59.289 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7270/7393, mem: 8935Mb, iter_time: 0.373s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.967e-03, size: 544, ETA: 9 days, 19:04:49 2022-05-20 19:22:02.624 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7280/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.5, lr: 3.969e-03, size: 480, ETA: 9 days, 19:03:28 2022-05-20 19:22:06.027 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7290/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 11.4, loss_cls: 9.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.972e-03, size: 608, ETA: 9 days, 19:02:17 2022-05-20 19:22:10.145 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7300/7393, mem: 8935Mb, iter_time: 0.411s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 3.975e-03, size: 576, ETA: 9 days, 19:02:54 2022-05-20 19:22:13.701 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7310/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.978e-03, size: 512, ETA: 9 days, 19:02:06 2022-05-20 19:22:16.698 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7320/7393, mem: 8935Mb, iter_time: 0.299s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 3.980e-03, size: 416, ETA: 9 days, 18:59:55 2022-05-20 19:22:20.201 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7330/7393, mem: 8935Mb, iter_time: 0.349s, data_time: 0.003s, total_loss: 10.5, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.0, lr: 3.983e-03, size: 704, ETA: 9 days, 18:58:59 2022-05-20 19:22:25.497 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7340/7393, mem: 8935Mb, iter_time: 0.529s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.5, lr: 3.986e-03, size: 736, ETA: 9 days, 19:02:33 2022-05-20 19:22:30.147 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7350/7393, mem: 8935Mb, iter_time: 0.465s, data_time: 0.001s, total_loss: 11.1, loss_cls: 9.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.988e-03, size: 448, ETA: 9 days, 19:04:29 2022-05-20 19:22:32.613 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7360/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 3.991e-03, size: 352, ETA: 9 days, 19:00:58 2022-05-20 19:22:35.784 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7370/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.006s, total_loss: 10.6, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 3.994e-03, size: 608, ETA: 9 days, 18:59:13 2022-05-20 19:22:39.768 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7380/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.033s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 3.996e-03, size: 352, ETA: 9 days, 18:59:30 2022-05-20 19:22:42.757 | INFO | yolox.core.trainer:after_iter:273 - epoch: 2/300, iter: 7390/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.6, lr: 3.999e-03, size: 640, ETA: 9 days, 18:57:17 2022-05-20 19:22:44.074 | INFO | yolox.core.trainer:save_ckpt:364 - Save weights to ./YOLOX_outputs/ppyoloe_s_sigmoid 2022-05-20 19:22:44.290 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch3 2022-05-20 19:22:44.290 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 19:22:44.291 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 19:22:47.300 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 10/7393, mem: 8935Mb, iter_time: 0.296s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.003e-03, size: 384, ETA: 9 days, 18:55:25 2022-05-20 19:22:50.703 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 20/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.5, lr: 4.005e-03, size: 608, ETA: 9 days, 18:54:16 2022-05-20 19:22:53.782 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 30/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.008e-03, size: 416, ETA: 9 days, 18:52:18 2022-05-20 19:22:56.868 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 40/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.003s, total_loss: 10.2, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.011e-03, size: 480, ETA: 9 days, 18:50:20 2022-05-20 19:23:00.732 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 50/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.003s, total_loss: 9.7, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 4.014e-03, size: 608, ETA: 9 days, 18:50:19 2022-05-20 19:23:05.213 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 60/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.001s, total_loss: 9.2, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.016e-03, size: 672, ETA: 9 days, 18:51:50 2022-05-20 19:23:09.996 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 70/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.001s, total_loss: 9.8, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 4.019e-03, size: 672, ETA: 9 days, 18:54:05 2022-05-20 19:23:13.998 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 80/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.022e-03, size: 544, ETA: 9 days, 18:54:25 2022-05-20 19:23:19.077 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 90/7393, mem: 8935Mb, iter_time: 0.507s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.024e-03, size: 768, ETA: 9 days, 18:57:24 2022-05-20 19:23:22.534 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 100/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.027e-03, size: 352, ETA: 9 days, 18:56:22 2022-05-20 19:23:25.336 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 110/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.003s, total_loss: 9.5, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.9, lr: 4.030e-03, size: 480, ETA: 9 days, 18:53:44 2022-05-20 19:23:28.864 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 120/7393, mem: 8935Mb, iter_time: 0.352s, data_time: 0.003s, total_loss: 10.9, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.9, loss_l1: 1.6, lr: 4.032e-03, size: 576, ETA: 9 days, 18:52:52 2022-05-20 19:23:32.753 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 130/7393, mem: 8935Mb, iter_time: 0.388s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.035e-03, size: 576, ETA: 9 days, 18:52:55 2022-05-20 19:23:35.277 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 140/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.038e-03, size: 320, ETA: 9 days, 18:49:37 2022-05-20 19:23:38.008 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 150/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.009s, total_loss: 10.0, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.041e-03, size: 384, ETA: 9 days, 18:46:48 2022-05-20 19:23:42.883 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 160/7393, mem: 8935Mb, iter_time: 0.487s, data_time: 0.004s, total_loss: 11.1, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.4, lr: 4.043e-03, size: 768, ETA: 9 days, 18:49:16 2022-05-20 19:23:46.251 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 170/7393, mem: 8935Mb, iter_time: 0.336s, data_time: 0.002s, total_loss: 11.3, loss_cls: 9.8, loss_iou: 0.3, loss_dfl: 1.5, loss_l1: 1.6, lr: 4.046e-03, size: 320, ETA: 9 days, 18:48:02 2022-05-20 19:23:48.809 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 180/7393, mem: 8935Mb, iter_time: 0.254s, data_time: 0.010s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 4.049e-03, size: 352, ETA: 9 days, 18:44:47 2022-05-20 19:23:52.769 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 190/7393, mem: 8935Mb, iter_time: 0.395s, data_time: 0.006s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.051e-03, size: 672, ETA: 9 days, 18:45:00 2022-05-20 19:23:56.383 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 200/7393, mem: 8935Mb, iter_time: 0.361s, data_time: 0.003s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.054e-03, size: 480, ETA: 9 days, 18:44:23 2022-05-20 19:23:59.866 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 210/7393, mem: 8935Mb, iter_time: 0.348s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.057e-03, size: 576, ETA: 9 days, 18:43:26 2022-05-20 19:24:02.638 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 220/7393, mem: 8935Mb, iter_time: 0.276s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.060e-03, size: 384, ETA: 9 days, 18:40:45 2022-05-20 19:24:05.991 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 230/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 2.0, lr: 4.062e-03, size: 576, ETA: 9 days, 18:39:29 2022-05-20 19:24:09.282 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 240/7393, mem: 8935Mb, iter_time: 0.328s, data_time: 0.002s, total_loss: 10.9, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.5, lr: 4.065e-03, size: 480, ETA: 9 days, 18:38:04 2022-05-20 19:24:13.474 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 250/7393, mem: 8935Mb, iter_time: 0.419s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.068e-03, size: 672, ETA: 9 days, 18:38:52 2022-05-20 19:24:17.467 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 260/7393, mem: 8935Mb, iter_time: 0.399s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.6, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.070e-03, size: 544, ETA: 9 days, 18:39:10 2022-05-20 19:24:21.983 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 270/7393, mem: 8935Mb, iter_time: 0.451s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 4.073e-03, size: 704, ETA: 9 days, 18:40:45 2022-05-20 19:24:25.604 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 280/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.076e-03, size: 448, ETA: 9 days, 18:40:09 2022-05-20 19:24:29.610 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 290/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.078e-03, size: 672, ETA: 9 days, 18:40:29 2022-05-20 19:24:34.436 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 300/7393, mem: 8935Mb, iter_time: 0.482s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.1, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.081e-03, size: 672, ETA: 9 days, 18:42:49 2022-05-20 19:24:38.425 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 310/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 4.084e-03, size: 544, ETA: 9 days, 18:43:07 2022-05-20 19:24:43.250 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 320/7393, mem: 8935Mb, iter_time: 0.482s, data_time: 0.003s, total_loss: 9.4, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 4.087e-03, size: 736, ETA: 9 days, 18:45:27 2022-05-20 19:24:49.066 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 330/7393, mem: 8935Mb, iter_time: 0.581s, data_time: 0.001s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.089e-03, size: 768, ETA: 9 days, 18:50:11 2022-05-20 19:24:53.333 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 340/7393, mem: 8935Mb, iter_time: 0.426s, data_time: 0.001s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.092e-03, size: 512, ETA: 9 days, 18:51:08 2022-05-20 19:24:58.007 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 350/7393, mem: 8935Mb, iter_time: 0.467s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.095e-03, size: 736, ETA: 9 days, 18:53:05 2022-05-20 19:25:01.398 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 360/7393, mem: 8935Mb, iter_time: 0.338s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.097e-03, size: 352, ETA: 9 days, 18:51:55 2022-05-20 19:25:05.574 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 370/7393, mem: 8935Mb, iter_time: 0.417s, data_time: 0.003s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.100e-03, size: 704, ETA: 9 days, 18:52:39 2022-05-20 19:25:08.872 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 380/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 9.0, loss_cls: 6.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.8, lr: 4.103e-03, size: 384, ETA: 9 days, 18:51:16 2022-05-20 19:25:11.748 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 390/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 4.106e-03, size: 512, ETA: 9 days, 18:48:51 2022-05-20 19:25:15.072 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 400/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.003s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.108e-03, size: 512, ETA: 9 days, 18:47:31 2022-05-20 19:25:18.880 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 410/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 4.111e-03, size: 608, ETA: 9 days, 18:47:22 2022-05-20 19:25:23.105 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 420/7393, mem: 8935Mb, iter_time: 0.422s, data_time: 0.001s, total_loss: 9.5, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.114e-03, size: 640, ETA: 9 days, 18:48:13 2022-05-20 19:25:26.021 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 430/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.116e-03, size: 352, ETA: 9 days, 18:45:55 2022-05-20 19:25:29.339 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 440/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.004s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.119e-03, size: 576, ETA: 9 days, 18:44:35 2022-05-20 19:25:31.950 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 450/7393, mem: 8935Mb, iter_time: 0.260s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.122e-03, size: 320, ETA: 9 days, 18:41:33 2022-05-20 19:25:34.495 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 460/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.006s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.7, lr: 4.124e-03, size: 352, ETA: 9 days, 18:38:21 2022-05-20 19:25:38.603 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 470/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 4.127e-03, size: 672, ETA: 9 days, 18:38:55 2022-05-20 19:25:42.742 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 480/7393, mem: 8935Mb, iter_time: 0.413s, data_time: 0.001s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.2, lr: 4.130e-03, size: 576, ETA: 9 days, 18:39:34 2022-05-20 19:25:45.803 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 490/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.133e-03, size: 448, ETA: 9 days, 18:37:38 2022-05-20 19:25:48.926 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 500/7393, mem: 8935Mb, iter_time: 0.311s, data_time: 0.003s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.135e-03, size: 480, ETA: 9 days, 18:35:50 2022-05-20 19:25:53.510 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 510/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.002s, total_loss: 11.3, loss_cls: 9.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 4.138e-03, size: 736, ETA: 9 days, 18:37:33 2022-05-20 19:25:58.692 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 520/7393, mem: 8935Mb, iter_time: 0.518s, data_time: 0.001s, total_loss: 11.1, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.141e-03, size: 672, ETA: 9 days, 18:40:42 2022-05-20 19:26:03.812 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 530/7393, mem: 8935Mb, iter_time: 0.512s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.143e-03, size: 704, ETA: 9 days, 18:43:43 2022-05-20 19:26:08.978 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 540/7393, mem: 8935Mb, iter_time: 0.516s, data_time: 0.001s, total_loss: 9.8, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.6, lr: 4.146e-03, size: 704, ETA: 9 days, 18:46:49 2022-05-20 19:26:14.211 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 550/7393, mem: 8935Mb, iter_time: 0.523s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 4.149e-03, size: 704, ETA: 9 days, 18:50:05 2022-05-20 19:26:18.523 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 560/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.151e-03, size: 576, ETA: 9 days, 18:51:08 2022-05-20 19:26:21.411 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 570/7393, mem: 8935Mb, iter_time: 0.288s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.7, lr: 4.154e-03, size: 416, ETA: 9 days, 18:48:47 2022-05-20 19:26:25.124 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 580/7393, mem: 8935Mb, iter_time: 0.370s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.7, lr: 4.157e-03, size: 640, ETA: 9 days, 18:48:24 2022-05-20 19:26:29.062 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 590/7393, mem: 8935Mb, iter_time: 0.393s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.160e-03, size: 544, ETA: 9 days, 18:48:34 2022-05-20 19:26:32.169 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 600/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.162e-03, size: 448, ETA: 9 days, 18:46:44 2022-05-20 19:26:34.896 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 610/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.003s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.165e-03, size: 448, ETA: 9 days, 18:44:00 2022-05-20 19:26:37.586 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 620/7393, mem: 8935Mb, iter_time: 0.268s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.168e-03, size: 416, ETA: 9 days, 18:41:11 2022-05-20 19:26:41.371 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 630/7393, mem: 8935Mb, iter_time: 0.378s, data_time: 0.002s, total_loss: 10.8, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 2.2, lr: 4.170e-03, size: 640, ETA: 9 days, 18:40:59 2022-05-20 19:26:44.508 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 640/7393, mem: 8935Mb, iter_time: 0.313s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.173e-03, size: 352, ETA: 9 days, 18:39:14 2022-05-20 19:26:48.926 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 650/7393, mem: 8935Mb, iter_time: 0.441s, data_time: 0.003s, total_loss: 10.8, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 2.0, lr: 4.176e-03, size: 736, ETA: 9 days, 18:40:32 2022-05-20 19:26:52.386 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 660/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.001s, total_loss: 9.1, loss_cls: 7.1, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 4.179e-03, size: 384, ETA: 9 days, 18:39:34 2022-05-20 19:26:54.762 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 670/7393, mem: 8935Mb, iter_time: 0.237s, data_time: 0.005s, total_loss: 9.8, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.181e-03, size: 352, ETA: 9 days, 18:36:01 2022-05-20 19:26:58.344 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 680/7393, mem: 8935Mb, iter_time: 0.357s, data_time: 0.005s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.4, lr: 4.184e-03, size: 576, ETA: 9 days, 18:35:20 2022-05-20 19:27:01.918 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 690/7393, mem: 8935Mb, iter_time: 0.357s, data_time: 0.002s, total_loss: 11.6, loss_cls: 9.9, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.4, lr: 4.187e-03, size: 544, ETA: 9 days, 18:34:38 2022-05-20 19:27:06.765 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 700/7393, mem: 8935Mb, iter_time: 0.484s, data_time: 0.002s, total_loss: 11.8, loss_cls: 9.2, loss_iou: 0.6, loss_dfl: 2.0, loss_l1: 1.9, lr: 4.189e-03, size: 736, ETA: 9 days, 18:36:57 2022-05-20 19:27:10.531 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 710/7393, mem: 8935Mb, iter_time: 0.376s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 2.1, lr: 4.192e-03, size: 448, ETA: 9 days, 18:36:43 2022-05-20 19:27:12.905 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 720/7393, mem: 8935Mb, iter_time: 0.237s, data_time: 0.003s, total_loss: 11.3, loss_cls: 9.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.195e-03, size: 384, ETA: 9 days, 18:33:10 2022-05-20 19:27:16.463 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 730/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.003s, total_loss: 11.0, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.197e-03, size: 608, ETA: 9 days, 18:32:26 2022-05-20 19:27:19.344 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 740/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.200e-03, size: 320, ETA: 9 days, 18:30:06 2022-05-20 19:27:23.457 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 750/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.006s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.203e-03, size: 672, ETA: 9 days, 18:30:41 2022-05-20 19:27:28.289 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 760/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 4.206e-03, size: 672, ETA: 9 days, 18:32:58 2022-05-20 19:27:31.694 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 770/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.208e-03, size: 448, ETA: 9 days, 18:31:52 2022-05-20 19:27:34.394 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 780/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.211e-03, size: 448, ETA: 9 days, 18:29:07 2022-05-20 19:27:37.388 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 790/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.003s, total_loss: 10.5, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 4.214e-03, size: 512, ETA: 9 days, 18:27:03 2022-05-20 19:27:41.093 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 800/7393, mem: 8935Mb, iter_time: 0.367s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.216e-03, size: 576, ETA: 9 days, 18:26:36 2022-05-20 19:27:44.139 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 810/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.219e-03, size: 448, ETA: 9 days, 18:24:40 2022-05-20 19:27:48.572 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 820/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.002s, total_loss: 9.8, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.8, lr: 4.222e-03, size: 736, ETA: 9 days, 18:26:00 2022-05-20 19:27:53.392 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 830/7393, mem: 8935Mb, iter_time: 0.481s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.0, lr: 4.225e-03, size: 608, ETA: 9 days, 18:28:15 2022-05-20 19:27:58.119 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 840/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.7, lr: 4.227e-03, size: 704, ETA: 9 days, 18:30:16 2022-05-20 19:28:01.743 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 850/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.230e-03, size: 448, ETA: 9 days, 18:29:42 2022-05-20 19:28:06.239 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 860/7393, mem: 8935Mb, iter_time: 0.449s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.9, lr: 4.233e-03, size: 736, ETA: 9 days, 18:31:11 2022-05-20 19:28:10.346 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 870/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.002s, total_loss: 11.5, loss_cls: 9.5, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 2.3, lr: 4.235e-03, size: 512, ETA: 9 days, 18:31:44 2022-05-20 19:28:13.841 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 880/7393, mem: 8935Mb, iter_time: 0.349s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.6, lr: 4.238e-03, size: 576, ETA: 9 days, 18:30:52 2022-05-20 19:28:17.030 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 890/7393, mem: 8935Mb, iter_time: 0.318s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.8, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 4.241e-03, size: 480, ETA: 9 days, 18:29:17 2022-05-20 19:28:19.715 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 900/7393, mem: 8935Mb, iter_time: 0.268s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 0.9, lr: 4.243e-03, size: 416, ETA: 9 days, 18:26:31 2022-05-20 19:28:24.481 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 910/7393, mem: 8935Mb, iter_time: 0.476s, data_time: 0.004s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.246e-03, size: 768, ETA: 9 days, 18:28:37 2022-05-20 19:28:28.242 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 920/7393, mem: 8935Mb, iter_time: 0.376s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 4.249e-03, size: 416, ETA: 9 days, 18:28:22 2022-05-20 19:28:31.053 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 930/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.252e-03, size: 480, ETA: 9 days, 18:25:53 2022-05-20 19:28:34.723 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 940/7393, mem: 8935Mb, iter_time: 0.366s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.254e-03, size: 608, ETA: 9 days, 18:25:26 2022-05-20 19:28:38.994 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 950/7393, mem: 8935Mb, iter_time: 0.427s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.257e-03, size: 640, ETA: 9 days, 18:26:23 2022-05-20 19:28:44.310 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 960/7393, mem: 8935Mb, iter_time: 0.531s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.0, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.4, lr: 4.260e-03, size: 768, ETA: 9 days, 18:29:46 2022-05-20 19:28:48.176 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 970/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.001s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.262e-03, size: 448, ETA: 9 days, 18:29:46 2022-05-20 19:28:51.510 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 980/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.1, lr: 4.265e-03, size: 576, ETA: 9 days, 18:28:31 2022-05-20 19:28:54.377 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 990/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.268e-03, size: 384, ETA: 9 days, 18:26:11 2022-05-20 19:28:57.628 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1000/7393, mem: 8935Mb, iter_time: 0.324s, data_time: 0.010s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 2.2, lr: 4.271e-03, size: 480, ETA: 9 days, 18:24:44 2022-05-20 19:29:00.510 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1010/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.008s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.273e-03, size: 384, ETA: 9 days, 18:22:27 2022-05-20 19:29:02.760 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1020/7393, mem: 8935Mb, iter_time: 0.224s, data_time: 0.003s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.276e-03, size: 320, ETA: 9 days, 18:18:41 2022-05-20 19:29:06.878 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1030/7393, mem: 8935Mb, iter_time: 0.411s, data_time: 0.004s, total_loss: 10.9, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.0, lr: 4.279e-03, size: 672, ETA: 9 days, 18:19:16 2022-05-20 19:29:11.947 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1040/7393, mem: 8935Mb, iter_time: 0.506s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.281e-03, size: 704, ETA: 9 days, 18:22:04 2022-05-20 19:29:15.570 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1050/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.284e-03, size: 448, ETA: 9 days, 18:21:30 2022-05-20 19:29:19.868 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1060/7393, mem: 8935Mb, iter_time: 0.429s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 4.287e-03, size: 704, ETA: 9 days, 18:22:30 2022-05-20 19:29:24.606 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1070/7393, mem: 8935Mb, iter_time: 0.473s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 4.289e-03, size: 640, ETA: 9 days, 18:24:32 2022-05-20 19:29:27.946 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1080/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 4.292e-03, size: 448, ETA: 9 days, 18:23:19 2022-05-20 19:29:32.028 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1090/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.5, lr: 4.295e-03, size: 672, ETA: 9 days, 18:23:48 2022-05-20 19:29:35.779 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1100/7393, mem: 8935Mb, iter_time: 0.374s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.298e-03, size: 512, ETA: 9 days, 18:23:32 2022-05-20 19:29:40.186 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1110/7393, mem: 8935Mb, iter_time: 0.440s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.7, loss_l1: 2.2, lr: 4.300e-03, size: 704, ETA: 9 days, 18:24:47 2022-05-20 19:29:45.110 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1120/7393, mem: 8935Mb, iter_time: 0.492s, data_time: 0.001s, total_loss: 10.7, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.303e-03, size: 672, ETA: 9 days, 18:27:14 2022-05-20 19:29:50.632 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1130/7393, mem: 8935Mb, iter_time: 0.552s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.306e-03, size: 768, ETA: 9 days, 18:31:03 2022-05-20 19:29:56.094 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1140/7393, mem: 8935Mb, iter_time: 0.546s, data_time: 0.001s, total_loss: 9.7, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.7, loss_l1: 1.9, lr: 4.308e-03, size: 704, ETA: 9 days, 18:34:44 2022-05-20 19:30:00.199 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1150/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.311e-03, size: 544, ETA: 9 days, 18:35:17 2022-05-20 19:30:02.737 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1160/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.002s, total_loss: 9.0, loss_cls: 6.9, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.314e-03, size: 320, ETA: 9 days, 18:32:13 2022-05-20 19:30:07.271 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1170/7393, mem: 8935Mb, iter_time: 0.452s, data_time: 0.004s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 2.2, lr: 4.317e-03, size: 736, ETA: 9 days, 18:33:44 2022-05-20 19:30:10.866 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1180/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.319e-03, size: 416, ETA: 9 days, 18:33:06 2022-05-20 19:30:15.409 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1190/7393, mem: 8935Mb, iter_time: 0.454s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.322e-03, size: 768, ETA: 9 days, 18:34:39 2022-05-20 19:30:20.124 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1200/7393, mem: 8935Mb, iter_time: 0.471s, data_time: 0.002s, total_loss: 11.5, loss_cls: 9.5, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 4.325e-03, size: 576, ETA: 9 days, 18:36:36 2022-05-20 19:30:22.883 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1210/7393, mem: 8935Mb, iter_time: 0.275s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.327e-03, size: 352, ETA: 9 days, 18:34:02 2022-05-20 19:30:25.946 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1220/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.011s, total_loss: 9.4, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.8, lr: 4.330e-03, size: 512, ETA: 9 days, 18:32:11 2022-05-20 19:30:30.168 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1230/7393, mem: 8935Mb, iter_time: 0.422s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.333e-03, size: 672, ETA: 9 days, 18:32:59 2022-05-20 19:30:34.338 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1240/7393, mem: 8935Mb, iter_time: 0.416s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.335e-03, size: 576, ETA: 9 days, 18:33:41 2022-05-20 19:30:38.331 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1250/7393, mem: 8935Mb, iter_time: 0.399s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.338e-03, size: 608, ETA: 9 days, 18:33:58 2022-05-20 19:30:41.188 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1260/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.341e-03, size: 352, ETA: 9 days, 18:31:39 2022-05-20 19:30:44.818 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1270/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.6, lr: 4.344e-03, size: 640, ETA: 9 days, 18:31:06 2022-05-20 19:30:48.679 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1280/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.7, loss_l1: 1.7, lr: 4.346e-03, size: 544, ETA: 9 days, 18:31:05 2022-05-20 19:30:52.990 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1290/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.349e-03, size: 672, ETA: 9 days, 18:32:05 2022-05-20 19:30:55.945 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1300/7393, mem: 8935Mb, iter_time: 0.295s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.352e-03, size: 320, ETA: 9 days, 18:30:00 2022-05-20 19:30:59.098 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1310/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.009s, total_loss: 9.7, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.354e-03, size: 544, ETA: 9 days, 18:28:22 2022-05-20 19:31:03.957 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1320/7393, mem: 8935Mb, iter_time: 0.485s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.357e-03, size: 736, ETA: 9 days, 18:30:38 2022-05-20 19:31:07.600 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1330/7393, mem: 8935Mb, iter_time: 0.363s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.360e-03, size: 416, ETA: 9 days, 18:30:06 2022-05-20 19:31:12.137 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1340/7393, mem: 8935Mb, iter_time: 0.453s, data_time: 0.002s, total_loss: 11.3, loss_cls: 9.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.5, lr: 4.363e-03, size: 768, ETA: 9 days, 18:31:38 2022-05-20 19:31:17.878 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1350/7393, mem: 8935Mb, iter_time: 0.574s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 4.365e-03, size: 736, ETA: 9 days, 18:35:53 2022-05-20 19:31:22.196 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1360/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.368e-03, size: 544, ETA: 9 days, 18:36:55 2022-05-20 19:31:26.699 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1370/7393, mem: 8935Mb, iter_time: 0.450s, data_time: 0.001s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.5, lr: 4.371e-03, size: 704, ETA: 9 days, 18:38:21 2022-05-20 19:31:30.669 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 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days, 18:37:08 2022-05-20 19:31:46.350 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1420/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.384e-03, size: 768, ETA: 9 days, 18:39:00 2022-05-20 19:31:51.406 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1430/7393, mem: 8935Mb, iter_time: 0.505s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.387e-03, size: 640, ETA: 9 days, 18:41:41 2022-05-20 19:31:54.604 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1440/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 4.390e-03, size: 416, ETA: 9 days, 18:40:10 2022-05-20 19:31:58.187 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1450/7393, mem: 8935Mb, iter_time: 0.358s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.392e-03, size: 608, ETA: 9 days, 18:39:30 2022-05-20 19:32:01.947 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1460/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.6, lr: 4.395e-03, size: 544, ETA: 9 days, 18:39:15 2022-05-20 19:32:06.720 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1470/7393, mem: 8935Mb, iter_time: 0.477s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 4.398e-03, size: 736, ETA: 9 days, 18:41:18 2022-05-20 19:32:11.394 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1480/7393, mem: 8935Mb, iter_time: 0.467s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 4.400e-03, size: 608, ETA: 9 days, 18:43:06 2022-05-20 19:32:16.082 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1490/7393, mem: 8935Mb, iter_time: 0.468s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.1, lr: 4.403e-03, size: 672, ETA: 9 days, 18:44:57 2022-05-20 19:32:19.172 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1500/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.406e-03, size: 352, ETA: 9 days, 18:43:11 2022-05-20 19:32:23.595 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1510/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.006s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 4.408e-03, size: 736, ETA: 9 days, 18:44:25 2022-05-20 19:32:28.793 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1520/7393, mem: 8935Mb, iter_time: 0.519s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, 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1560/7393, mem: 8935Mb, iter_time: 0.263s, data_time: 0.006s, total_loss: 10.0, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 4.422e-03, size: 384, ETA: 9 days, 18:42:11 2022-05-20 19:32:45.446 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1570/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.008s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.425e-03, size: 512, ETA: 9 days, 18:41:16 2022-05-20 19:32:47.836 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1580/7393, mem: 8935Mb, iter_time: 0.238s, data_time: 0.002s, total_loss: 8.8, loss_cls: 6.9, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.427e-03, size: 320, ETA: 9 days, 18:37:56 2022-05-20 19:32:51.939 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1590/7393, mem: 8935Mb, iter_time: 0.409s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.430e-03, size: 704, ETA: 9 days, 18:38:26 2022-05-20 19:32:56.985 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1600/7393, mem: 8935Mb, iter_time: 0.504s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.433e-03, size: 672, ETA: 9 days, 18:41:04 2022-05-20 19:33:00.464 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1610/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.436e-03, size: 448, ETA: 9 days, 18:40:11 2022-05-20 19:33:03.629 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1620/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.438e-03, size: 544, ETA: 9 days, 18:38:36 2022-05-20 19:33:07.182 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1630/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.441e-03, size: 544, ETA: 9 days, 18:37:54 2022-05-20 19:33:12.038 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1640/7393, mem: 8935Mb, iter_time: 0.485s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.444e-03, size: 736, ETA: 9 days, 18:40:06 2022-05-20 19:33:16.489 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1650/7393, mem: 8935Mb, iter_time: 0.445s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.446e-03, size: 576, ETA: 9 days, 18:41:23 2022-05-20 19:33:20.006 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1660/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.449e-03, size: 544, ETA: 9 days, 18:40:36 2022-05-20 19:33:22.599 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1670/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.0, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.5, lr: 4.452e-03, size: 320, ETA: 9 days, 18:37:45 2022-05-20 19:33:26.476 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1680/7393, mem: 8935Mb, iter_time: 0.387s, data_time: 0.004s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.454e-03, size: 640, ETA: 9 days, 18:37:45 2022-05-20 19:33:29.537 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1690/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.457e-03, size: 384, ETA: 9 days, 18:35:56 2022-05-20 19:33:32.452 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1700/7393, mem: 8935Mb, iter_time: 0.290s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.460e-03, size: 512, ETA: 9 days, 18:33:48 2022-05-20 19:33:35.038 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1710/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.463e-03, size: 352, ETA: 9 days, 18:30:56 2022-05-20 19:33:37.717 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1720/7393, mem: 8935Mb, iter_time: 0.267s, data_time: 0.010s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.465e-03, size: 384, ETA: 9 days, 18:28:16 2022-05-20 19:33:41.147 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1730/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.005s, total_loss: 9.3, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.468e-03, size: 576, ETA: 9 days, 18:27:17 2022-05-20 19:33:43.943 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1740/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.471e-03, size: 352, ETA: 9 days, 18:24:54 2022-05-20 19:33:46.281 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1750/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.006s, total_loss: 9.3, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.473e-03, size: 352, ETA: 9 days, 18:21:30 2022-05-20 19:33:49.434 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1760/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.007s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.476e-03, size: 448, ETA: 9 days, 18:19:51 2022-05-20 19:33:52.508 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1770/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.003s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 4.479e-03, size: 480, ETA: 9 days, 18:18:05 2022-05-20 19:33:55.042 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1780/7393, mem: 8935Mb, iter_time: 0.253s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.482e-03, size: 384, ETA: 9 days, 18:15:08 2022-05-20 19:33:59.517 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1790/7393, mem: 8935Mb, iter_time: 0.447s, data_time: 0.006s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.484e-03, size: 736, ETA: 9 days, 18:16:28 2022-05-20 19:34:02.818 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1800/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.487e-03, size: 320, ETA: 9 days, 18:15:13 2022-05-20 19:34:05.941 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1810/7393, mem: 8935Mb, iter_time: 0.311s, data_time: 0.005s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.490e-03, size: 512, ETA: 9 days, 18:13:34 2022-05-20 19:34:10.822 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1820/7393, mem: 8935Mb, iter_time: 0.488s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.492e-03, size: 768, ETA: 9 days, 18:15:48 2022-05-20 19:34:15.025 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1830/7393, mem: 8935Mb, iter_time: 0.420s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 4.495e-03, size: 512, ETA: 9 days, 18:16:33 2022-05-20 19:34:19.681 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1840/7393, mem: 8935Mb, iter_time: 0.465s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 4.498e-03, size: 736, ETA: 9 days, 18:18:18 2022-05-20 19:34:23.078 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1850/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 4.500e-03, size: 352, ETA: 9 days, 18:17:15 2022-05-20 19:34:26.033 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1860/7393, mem: 8935Mb, iter_time: 0.294s, data_time: 0.006s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.503e-03, size: 480, ETA: 9 days, 18:15:14 2022-05-20 19:34:28.664 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1870/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.506e-03, size: 384, ETA: 9 days, 18:12:30 2022-05-20 19:34:31.087 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1880/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 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0.288s, data_time: 0.002s, total_loss: 11.0, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.519e-03, size: 416, ETA: 9 days, 18:07:33 2022-05-20 19:34:51.502 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1930/7393, mem: 8935Mb, iter_time: 0.574s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.1, lr: 4.522e-03, size: 704, ETA: 9 days, 18:11:40 2022-05-20 19:34:55.290 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1940/7393, mem: 8935Mb, iter_time: 0.378s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.525e-03, size: 480, ETA: 9 days, 18:11:30 2022-05-20 19:34:58.063 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1950/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.9, lr: 4.528e-03, size: 448, ETA: 9 days, 18:09:06 2022-05-20 19:35:01.265 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1960/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.004s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.530e-03, size: 512, ETA: 9 days, 18:07:38 2022-05-20 19:35:03.798 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1970/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.533e-03, size: 352, ETA: 9 days, 18:04:43 2022-05-20 19:35:06.504 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1980/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.008s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.536e-03, size: 384, ETA: 9 days, 18:02:10 2022-05-20 19:35:09.086 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 1990/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.006s, total_loss: 9.1, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.538e-03, size: 352, ETA: 9 days, 17:59:21 2022-05-20 19:35:12.445 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2000/7393, mem: 8935Mb, iter_time: 0.335s, data_time: 0.005s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.541e-03, size: 576, ETA: 9 days, 17:58:14 2022-05-20 19:35:16.681 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2010/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.3, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.5, lr: 4.544e-03, size: 640, ETA: 9 days, 17:59:03 2022-05-20 19:35:20.869 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2020/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.546e-03, size: 608, ETA: 9 days, 17:59:46 2022-05-20 19:35:24.004 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2030/7393, mem: 8935Mb, iter_time: 0.313s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.549e-03, size: 448, ETA: 9 days, 17:58:11 2022-05-20 19:35:26.405 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2040/7393, mem: 8935Mb, iter_time: 0.239s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.552e-03, size: 384, ETA: 9 days, 17:54:59 2022-05-20 19:35:28.815 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2050/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 4.555e-03, size: 384, ETA: 9 days, 17:51:49 2022-05-20 19:35:31.631 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2060/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.008s, total_loss: 8.5, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.557e-03, size: 320, ETA: 9 days, 17:49:32 2022-05-20 19:35:34.778 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2070/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.008s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.560e-03, size: 480, ETA: 9 days, 17:47:58 2022-05-20 19:35:38.012 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2080/7393, mem: 8935Mb, iter_time: 0.322s, data_time: 0.005s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 4.563e-03, size: 480, ETA: 9 days, 17:46:36 2022-05-20 19:35:40.959 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2090/7393, mem: 8935Mb, iter_time: 0.294s, data_time: 0.002s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.565e-03, size: 480, ETA: 9 days, 17:44:37 2022-05-20 19:35:45.330 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2100/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.568e-03, size: 704, ETA: 9 days, 17:45:44 2022-05-20 19:35:50.970 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2110/7393, mem: 8935Mb, iter_time: 0.564s, data_time: 0.001s, total_loss: 11.3, loss_cls: 9.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.571e-03, size: 768, ETA: 9 days, 17:49:36 2022-05-20 19:35:54.566 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2120/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 2.0, lr: 4.574e-03, size: 384, ETA: 9 days, 17:49:02 2022-05-20 19:35:57.144 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2130/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.004s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.576e-03, size: 384, ETA: 9 days, 17:46:14 2022-05-20 19:36:01.128 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2140/7393, mem: 8935Mb, iter_time: 0.397s, data_time: 0.005s, total_loss: 10.7, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.579e-03, size: 672, ETA: 9 days, 17:46:29 2022-05-20 19:36:06.166 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2150/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.7, lr: 4.582e-03, size: 704, ETA: 9 days, 17:49:03 2022-05-20 19:36:10.953 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2160/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.5, lr: 4.584e-03, size: 640, ETA: 9 days, 17:51:03 2022-05-20 19:36:15.544 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2170/7393, mem: 8935Mb, iter_time: 0.459s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.587e-03, size: 672, ETA: 9 days, 17:52:38 2022-05-20 19:36:20.825 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2180/7393, mem: 8935Mb, iter_time: 0.528s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.7, lr: 4.590e-03, size: 736, ETA: 9 days, 17:55:43 2022-05-20 19:36:24.180 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2190/7393, mem: 8935Mb, iter_time: 0.335s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 4.592e-03, size: 352, ETA: 9 days, 17:54:37 2022-05-20 19:36:27.545 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2200/7393, mem: 8935Mb, iter_time: 0.336s, data_time: 0.004s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.7, lr: 4.595e-03, size: 576, ETA: 9 days, 17:53:33 2022-05-20 19:36:30.262 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2210/7393, mem: 8935Mb, iter_time: 0.271s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.598e-03, size: 320, ETA: 9 days, 17:51:04 2022-05-20 19:36:32.393 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2220/7393, mem: 8935Mb, iter_time: 0.212s, data_time: 0.004s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 4.601e-03, size: 320, ETA: 9 days, 17:47:19 2022-05-20 19:36:35.322 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2230/7393, mem: 8935Mb, iter_time: 0.292s, data_time: 0.008s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.603e-03, size: 352, ETA: 9 days, 17:45:18 2022-05-20 19:36:39.076 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2240/7393, mem: 8935Mb, iter_time: 0.374s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 4.606e-03, size: 608, ETA: 9 days, 17:45:03 2022-05-20 19:36:43.246 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2250/7393, mem: 8935Mb, iter_time: 0.416s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.609e-03, size: 608, ETA: 9 days, 17:45:43 2022-05-20 19:36:47.778 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2260/7393, mem: 8935Mb, iter_time: 0.453s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.611e-03, size: 672, ETA: 9 days, 17:47:10 2022-05-20 19:36:50.977 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2270/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.614e-03, size: 384, ETA: 9 days, 17:45:45 2022-05-20 19:36:53.335 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2280/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.004s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.617e-03, size: 352, ETA: 9 days, 17:42:29 2022-05-20 19:36:56.651 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2290/7393, mem: 8935Mb, iter_time: 0.330s, data_time: 0.005s, total_loss: 9.2, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.620e-03, size: 448, ETA: 9 days, 17:41:18 2022-05-20 19:36:59.267 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2300/7393, mem: 8935Mb, iter_time: 0.260s, data_time: 0.003s, total_loss: 9.4, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 4.622e-03, size: 352, ETA: 9 days, 17:38:37 2022-05-20 19:37:03.140 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2310/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.006s, total_loss: 11.0, loss_cls: 9.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.625e-03, size: 640, ETA: 9 days, 17:38:39 2022-05-20 19:37:06.095 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2320/7393, mem: 8935Mb, iter_time: 0.295s, data_time: 0.002s, total_loss: 10.0, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 2.1, lr: 4.628e-03, size: 352, ETA: 9 days, 17:36:43 2022-05-20 19:37:09.616 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2330/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.003s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.2, lr: 4.630e-03, size: 608, ETA: 9 days, 17:35:59 2022-05-20 19:37:13.501 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2340/7393, mem: 8935Mb, iter_time: 0.388s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.633e-03, size: 576, ETA: 9 days, 17:36:02 2022-05-20 19:37:18.011 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2350/7393, mem: 8935Mb, iter_time: 0.450s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, 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days, 17:41:08 2022-05-20 19:37:49.381 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2430/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.002s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.657e-03, size: 352, ETA: 9 days, 17:38:31 2022-05-20 19:37:53.969 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2440/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.1, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.660e-03, size: 736, ETA: 9 days, 17:40:04 2022-05-20 19:37:57.165 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2450/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 4.663e-03, size: 320, ETA: 9 days, 17:38:39 2022-05-20 19:38:01.663 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2460/7393, mem: 8935Mb, iter_time: 0.449s, data_time: 0.005s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.665e-03, size: 768, ETA: 9 days, 17:40:01 2022-05-20 19:38:05.340 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2470/7393, mem: 8935Mb, iter_time: 0.367s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.668e-03, size: 352, ETA: 9 days, 17:39:37 2022-05-20 19:38:09.323 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2480/7393, mem: 8935Mb, iter_time: 0.397s, data_time: 0.008s, total_loss: 11.5, loss_cls: 9.6, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.671e-03, size: 672, ETA: 9 days, 17:39:53 2022-05-20 19:38:14.158 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2490/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.6, lr: 4.674e-03, size: 672, ETA: 9 days, 17:41:57 2022-05-20 19:38:17.874 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2500/7393, mem: 8935Mb, iter_time: 0.371s, data_time: 0.002s, total_loss: 9.9, loss_cls: 7.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.2, lr: 4.676e-03, size: 512, ETA: 9 days, 17:41:39 2022-05-20 19:38:20.852 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2510/7393, mem: 8935Mb, iter_time: 0.297s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.679e-03, size: 480, ETA: 9 days, 17:39:47 2022-05-20 19:38:23.430 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2520/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.682e-03, size: 352, ETA: 9 days, 17:37:04 2022-05-20 19:38:27.622 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2530/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.009s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.324s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.695e-03, size: 544, ETA: 9 days, 17:29:17 2022-05-20 19:38:43.066 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2580/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.698e-03, size: 640, ETA: 9 days, 17:29:36 2022-05-20 19:38:47.964 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2590/7393, mem: 8935Mb, iter_time: 0.489s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 4.701e-03, size: 704, ETA: 9 days, 17:31:48 2022-05-20 19:38:53.371 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2600/7393, mem: 8935Mb, iter_time: 0.540s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.703e-03, size: 736, ETA: 9 days, 17:35:05 2022-05-20 19:38:56.865 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2610/7393, mem: 8935Mb, iter_time: 0.349s, data_time: 0.003s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 4.706e-03, size: 384, ETA: 9 days, 17:34:19 2022-05-20 19:38:59.450 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2620/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.003s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.709e-03, size: 384, ETA: 9 days, 17:31:37 2022-05-20 19:39:03.080 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2630/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.003s, total_loss: 9.8, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.711e-03, size: 576, ETA: 9 days, 17:31:08 2022-05-20 19:39:06.428 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2640/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.714e-03, size: 512, ETA: 9 days, 17:30:04 2022-05-20 19:39:10.788 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2650/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.717e-03, size: 704, ETA: 9 days, 17:31:08 2022-05-20 19:39:16.549 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2660/7393, mem: 8935Mb, iter_time: 0.576s, data_time: 0.002s, total_loss: 11.3, loss_cls: 9.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 4.720e-03, size: 768, ETA: 9 days, 17:35:08 2022-05-20 19:39:22.518 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2670/7393, mem: 8935Mb, iter_time: 0.596s, data_time: 0.001s, total_loss: 9.8, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.722e-03, size: 768, ETA: 9 days, 17:39:34 2022-05-20 19:39:26.614 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2680/7393, mem: 8935Mb, iter_time: 0.409s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 4.725e-03, size: 480, ETA: 9 days, 17:40:04 2022-05-20 19:39:31.360 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2690/7393, mem: 8935Mb, iter_time: 0.474s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 4.728e-03, size: 768, ETA: 9 days, 17:41:56 2022-05-20 19:39:34.862 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2700/7393, mem: 8935Mb, iter_time: 0.349s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.730e-03, size: 352, ETA: 9 days, 17:41:11 2022-05-20 19:39:37.845 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2710/7393, mem: 8935Mb, iter_time: 0.297s, data_time: 0.005s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 4.733e-03, size: 512, ETA: 9 days, 17:39:20 2022-05-20 19:39:40.792 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2720/7393, mem: 8935Mb, iter_time: 0.294s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.736e-03, size: 416, ETA: 9 days, 17:37:25 2022-05-20 19:39:45.457 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2730/7393, mem: 8935Mb, iter_time: 0.466s, data_time: 0.005s, total_loss: 10.3, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.9, lr: 4.739e-03, size: 768, ETA: 9 days, 17:39:06 2022-05-20 19:39:50.478 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2740/7393, mem: 8935Mb, iter_time: 0.502s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.741e-03, size: 640, ETA: 9 days, 17:41:32 2022-05-20 19:39:53.907 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2750/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.744e-03, size: 448, ETA: 9 days, 17:40:38 2022-05-20 19:39:56.654 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2760/7393, mem: 8935Mb, iter_time: 0.274s, data_time: 0.005s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.7, lr: 4.747e-03, size: 448, ETA: 9 days, 17:38:18 2022-05-20 19:39:59.872 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2770/7393, mem: 8935Mb, iter_time: 0.321s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.749e-03, size: 544, ETA: 9 days, 17:36:57 2022-05-20 19:40:03.037 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2780/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.752e-03, size: 480, ETA: 9 days, 17:35:30 2022-05-20 19:40:07.224 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2790/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.002s, total_loss: 11.5, loss_cls: 9.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.755e-03, size: 672, ETA: 9 days, 17:36:11 2022-05-20 19:40:10.492 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2800/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.757e-03, size: 416, ETA: 9 days, 17:34:57 2022-05-20 19:40:14.639 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2810/7393, mem: 8935Mb, iter_time: 0.414s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.760e-03, size: 704, ETA: 9 days, 17:35:33 2022-05-20 19:40:18.643 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2820/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.8, loss_iou: 0.4, 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days, 17:35:29 2022-05-20 19:40:50.010 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2900/7393, mem: 8935Mb, iter_time: 0.452s, data_time: 0.006s, total_loss: 8.8, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.785e-03, size: 736, ETA: 9 days, 17:36:52 2022-05-20 19:40:55.122 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2910/7393, mem: 8935Mb, iter_time: 0.511s, data_time: 0.001s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 4.787e-03, size: 672, ETA: 9 days, 17:39:28 2022-05-20 19:40:59.487 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2920/7393, mem: 8935Mb, iter_time: 0.435s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.5, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.790e-03, size: 608, ETA: 9 days, 17:40:30 2022-05-20 19:41:04.455 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2930/7393, mem: 8935Mb, iter_time: 0.496s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.793e-03, size: 736, ETA: 9 days, 17:42:48 2022-05-20 19:41:09.598 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2940/7393, mem: 8935Mb, iter_time: 0.514s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.795e-03, size: 672, ETA: 9 days, 17:45:27 2022-05-20 19:41:13.563 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2950/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.798e-03, size: 544, ETA: 9 days, 17:45:40 2022-05-20 19:41:18.288 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2960/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 4.801e-03, size: 736, ETA: 9 days, 17:47:27 2022-05-20 19:41:23.699 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2970/7393, mem: 8935Mb, iter_time: 0.541s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.803e-03, size: 704, ETA: 9 days, 17:50:39 2022-05-20 19:41:26.981 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2980/7393, mem: 8935Mb, iter_time: 0.327s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.806e-03, size: 384, ETA: 9 days, 17:49:27 2022-05-20 19:41:30.588 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 2990/7393, mem: 8935Mb, iter_time: 0.360s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.809e-03, size: 640, ETA: 9 days, 17:48:55 2022-05-20 19:41:35.908 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3000/7393, mem: 8935Mb, iter_time: 0.532s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, 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days, 17:57:38 2022-05-20 19:42:09.038 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3080/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.833e-03, size: 416, ETA: 9 days, 17:56:27 2022-05-20 19:42:12.377 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3090/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.004s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.6, lr: 4.836e-03, size: 576, ETA: 9 days, 17:55:22 2022-05-20 19:42:16.510 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3100/7393, mem: 8935Mb, iter_time: 0.413s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.839e-03, size: 640, ETA: 9 days, 17:55:55 2022-05-20 19:42:21.352 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3110/7393, mem: 8935Mb, iter_time: 0.484s, data_time: 0.001s, total_loss: 10.7, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.3, lr: 4.841e-03, size: 704, ETA: 9 days, 17:57:56 2022-05-20 19:42:24.415 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3120/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.844e-03, size: 320, ETA: 9 days, 17:56:17 2022-05-20 19:42:26.944 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3130/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.006s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.847e-03, size: 320, ETA: 9 days, 17:53:30 2022-05-20 19:42:30.636 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3140/7393, mem: 8935Mb, iter_time: 0.367s, data_time: 0.005s, total_loss: 11.0, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.9, lr: 4.849e-03, size: 576, ETA: 9 days, 17:53:06 2022-05-20 19:42:33.317 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3150/7393, mem: 8935Mb, iter_time: 0.267s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 4.852e-03, size: 352, ETA: 9 days, 17:50:40 2022-05-20 19:42:36.928 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3160/7393, mem: 8935Mb, iter_time: 0.360s, data_time: 0.005s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.855e-03, size: 608, ETA: 9 days, 17:50:09 2022-05-20 19:42:40.130 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3170/7393, mem: 8935Mb, iter_time: 0.320s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.858e-03, size: 416, ETA: 9 days, 17:48:48 2022-05-20 19:42:44.731 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3180/7393, mem: 8935Mb, iter_time: 0.459s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.860e-03, size: 736, ETA: 9 days, 17:50:18 2022-05-20 19:42:49.838 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3190/7393, mem: 8935Mb, iter_time: 0.510s, data_time: 0.001s, total_loss: 10.5, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.7, loss_l1: 1.9, lr: 4.863e-03, size: 672, ETA: 9 days, 17:52:50 2022-05-20 19:42:55.170 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3200/7393, mem: 8935Mb, iter_time: 0.533s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 4.866e-03, size: 736, ETA: 9 days, 17:55:50 2022-05-20 19:42:58.703 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3210/7393, mem: 8935Mb, iter_time: 0.353s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.868e-03, size: 384, ETA: 9 days, 17:55:09 2022-05-20 19:43:02.346 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3220/7393, mem: 8935Mb, iter_time: 0.363s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 4.871e-03, size: 640, ETA: 9 days, 17:54:41 2022-05-20 19:43:06.983 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3230/7393, mem: 8935Mb, iter_time: 0.463s, data_time: 0.001s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.874e-03, size: 672, ETA: 9 days, 17:56:16 2022-05-20 19:43:12.454 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3240/7393, mem: 8935Mb, iter_time: 0.547s, data_time: 0.001s, total_loss: 11.3, loss_cls: 9.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.8, lr: 4.877e-03, size: 768, ETA: 9 days, 17:59:32 2022-05-20 19:43:16.844 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3250/7393, mem: 8935Mb, iter_time: 0.438s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.1, lr: 4.879e-03, size: 544, ETA: 9 days, 18:00:36 2022-05-20 19:43:19.441 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3260/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.882e-03, size: 320, ETA: 9 days, 17:58:01 2022-05-20 19:43:22.171 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3270/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.005s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.885e-03, size: 352, ETA: 9 days, 17:55:42 2022-05-20 19:43:24.915 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3280/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.005s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.887e-03, size: 352, ETA: 9 days, 17:53:23 2022-05-20 19:43:27.932 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3290/7393, mem: 8935Mb, iter_time: 0.301s, data_time: 0.006s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.7, lr: 4.890e-03, size: 480, ETA: 9 days, 17:51:40 2022-05-20 19:43:32.134 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3300/7393, mem: 8935Mb, iter_time: 0.419s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.893e-03, size: 672, ETA: 9 days, 17:52:20 2022-05-20 19:43:37.653 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3310/7393, mem: 8935Mb, iter_time: 0.551s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 4.895e-03, size: 768, ETA: 9 days, 17:55:41 2022-05-20 19:43:43.158 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3320/7393, mem: 8935Mb, iter_time: 0.550s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.898e-03, size: 704, ETA: 9 days, 17:59:01 2022-05-20 19:43:48.081 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3330/7393, mem: 8935Mb, iter_time: 0.492s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 4.901e-03, size: 672, ETA: 9 days, 18:01:09 2022-05-20 19:43:51.380 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3340/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 4.904e-03, size: 416, ETA: 9 days, 18:00:00 2022-05-20 19:43:54.940 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3350/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.003s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.906e-03, size: 608, ETA: 9 days, 17:59:23 2022-05-20 19:43:58.937 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3360/7393, mem: 8935Mb, iter_time: 0.399s, data_time: 0.002s, total_loss: 12.5, loss_cls: 10.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 4.909e-03, size: 576, ETA: 9 days, 17:59:38 2022-05-20 19:44:02.446 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3370/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.912e-03, size: 544, ETA: 9 days, 17:58:55 2022-05-20 19:44:07.213 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3380/7393, mem: 8935Mb, iter_time: 0.476s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.914e-03, size: 736, ETA: 9 days, 18:00:44 2022-05-20 19:44:10.812 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3390/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 4.917e-03, size: 416, ETA: 9 days, 18:00:12 2022-05-20 19:44:13.926 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 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days, 17:55:29 2022-05-20 19:44:27.913 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3440/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.931e-03, size: 640, ETA: 9 days, 17:55:40 2022-05-20 19:44:31.581 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3450/7393, mem: 8935Mb, iter_time: 0.366s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.933e-03, size: 512, ETA: 9 days, 17:55:17 2022-05-20 19:44:35.313 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3460/7393, mem: 8935Mb, iter_time: 0.373s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 4.936e-03, size: 608, ETA: 9 days, 17:55:01 2022-05-20 19:44:38.163 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3470/7393, mem: 8935Mb, iter_time: 0.284s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 4.939e-03, size: 352, ETA: 9 days, 17:52:58 2022-05-20 19:44:41.907 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3480/7393, mem: 8935Mb, iter_time: 0.373s, data_time: 0.003s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 4.941e-03, size: 640, ETA: 9 days, 17:52:43 2022-05-20 19:44:46.250 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3490/7393, mem: 8935Mb, iter_time: 0.434s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 4.944e-03, size: 640, ETA: 9 days, 17:53:40 2022-05-20 19:44:51.102 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3500/7393, mem: 8935Mb, iter_time: 0.485s, data_time: 0.001s, total_loss: 8.6, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 4.947e-03, size: 704, ETA: 9 days, 17:55:39 2022-05-20 19:44:54.843 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3510/7393, mem: 8935Mb, iter_time: 0.373s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 4.950e-03, size: 480, ETA: 9 days, 17:55:24 2022-05-20 19:44:58.129 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3520/7393, mem: 8935Mb, iter_time: 0.328s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 4.952e-03, size: 544, ETA: 9 days, 17:54:14 2022-05-20 19:45:00.929 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3530/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.955e-03, size: 320, ETA: 9 days, 17:52:06 2022-05-20 19:45:04.029 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3540/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.009s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.501s, data_time: 0.003s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.968e-03, size: 768, ETA: 9 days, 17:51:32 2022-05-20 19:45:25.738 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3590/7393, mem: 8935Mb, iter_time: 0.575s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 2.3, lr: 4.971e-03, size: 736, ETA: 9 days, 17:55:18 2022-05-20 19:45:29.066 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3600/7393, mem: 8935Mb, iter_time: 0.332s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 4.974e-03, size: 320, ETA: 9 days, 17:54:14 2022-05-20 19:45:32.934 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3610/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.006s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 4.977e-03, size: 640, ETA: 9 days, 17:54:13 2022-05-20 19:45:36.400 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3620/7393, mem: 8935Mb, iter_time: 0.346s, data_time: 0.002s, total_loss: 10.8, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 4.979e-03, size: 480, ETA: 9 days, 17:53:25 2022-05-20 19:45:39.802 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3630/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 4.982e-03, size: 576, ETA: 9 days, 17:52:30 2022-05-20 19:45:42.902 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3640/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.004s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 4.985e-03, size: 448, ETA: 9 days, 17:50:58 2022-05-20 19:45:47.442 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3650/7393, mem: 8935Mb, iter_time: 0.453s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.7, loss_l1: 1.6, lr: 4.987e-03, size: 736, ETA: 9 days, 17:52:19 2022-05-20 19:45:51.678 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3660/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 4.990e-03, size: 544, ETA: 9 days, 17:53:03 2022-05-20 19:45:56.164 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3670/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.002s, total_loss: 10.7, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.7, lr: 4.993e-03, size: 704, ETA: 9 days, 17:54:17 2022-05-20 19:45:59.327 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3680/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 4.996e-03, size: 352, ETA: 9 days, 17:52:53 2022-05-20 19:46:03.699 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3690/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.003s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 4.998e-03, size: 736, ETA: 9 days, 17:53:52 2022-05-20 19:46:08.855 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3700/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 5.001e-03, size: 672, ETA: 9 days, 17:56:26 2022-05-20 19:46:12.440 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3710/7393, mem: 8935Mb, iter_time: 0.358s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.004e-03, size: 480, ETA: 9 days, 17:55:52 2022-05-20 19:46:14.762 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3720/7393, mem: 8935Mb, iter_time: 0.231s, data_time: 0.005s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 5.006e-03, size: 320, ETA: 9 days, 17:52:48 2022-05-20 19:46:17.783 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3730/7393, mem: 8935Mb, iter_time: 0.301s, data_time: 0.006s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.009e-03, size: 448, ETA: 9 days, 17:51:07 2022-05-20 19:46:20.446 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3740/7393, mem: 8935Mb, iter_time: 0.265s, data_time: 0.005s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.012e-03, size: 352, ETA: 9 days, 17:48:44 2022-05-20 19:46:23.460 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3750/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.008s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.014e-03, size: 320, ETA: 9 days, 17:47:02 2022-05-20 19:46:27.876 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3760/7393, mem: 8935Mb, iter_time: 0.441s, data_time: 0.003s, total_loss: 10.7, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.4, lr: 5.017e-03, size: 736, ETA: 9 days, 17:48:07 2022-05-20 19:46:33.505 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3770/7393, mem: 8935Mb, iter_time: 0.562s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.020e-03, size: 736, ETA: 9 days, 17:51:36 2022-05-20 19:46:37.265 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3780/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 5.023e-03, size: 448, ETA: 9 days, 17:51:24 2022-05-20 19:46:40.549 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3790/7393, mem: 8935Mb, iter_time: 0.328s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.025e-03, size: 544, ETA: 9 days, 17:50:14 2022-05-20 19:46:44.907 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3800/7393, mem: 8935Mb, iter_time: 0.435s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.028e-03, size: 672, ETA: 9 days, 17:51:13 2022-05-20 19:46:47.912 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3810/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.031e-03, size: 352, ETA: 9 days, 17:49:31 2022-05-20 19:46:50.767 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3820/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.033e-03, size: 480, ETA: 9 days, 17:47:31 2022-05-20 19:46:54.105 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3830/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.003s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, 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days, 17:52:38 2022-05-20 19:47:26.951 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3910/7393, mem: 8935Mb, iter_time: 0.264s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.058e-03, size: 320, ETA: 9 days, 17:50:14 2022-05-20 19:47:29.820 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3920/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.007s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.060e-03, size: 320, ETA: 9 days, 17:48:17 2022-05-20 19:47:34.295 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3930/7393, mem: 8935Mb, iter_time: 0.447s, data_time: 0.005s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.063e-03, size: 736, ETA: 9 days, 17:49:28 2022-05-20 19:47:38.009 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3940/7393, mem: 8935Mb, iter_time: 0.371s, data_time: 0.001s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.066e-03, size: 448, ETA: 9 days, 17:49:10 2022-05-20 19:47:41.119 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3950/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.069e-03, size: 512, ETA: 9 days, 17:47:41 2022-05-20 19:47:43.594 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3960/7393, mem: 8935Mb, iter_time: 0.247s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.1, lr: 5.071e-03, size: 320, ETA: 9 days, 17:44:58 2022-05-20 19:47:46.035 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3970/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.005s, total_loss: 10.6, loss_cls: 8.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.2, lr: 5.074e-03, size: 352, ETA: 9 days, 17:42:08 2022-05-20 19:47:50.733 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3980/7393, mem: 8935Mb, iter_time: 0.468s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.077e-03, size: 736, ETA: 9 days, 17:43:44 2022-05-20 19:47:56.143 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 3990/7393, mem: 8935Mb, iter_time: 0.541s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.079e-03, size: 704, ETA: 9 days, 17:46:45 2022-05-20 19:47:59.743 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4000/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.082e-03, size: 448, ETA: 9 days, 17:46:14 2022-05-20 19:48:04.227 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4010/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, 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days, 17:45:45 2022-05-20 19:48:34.724 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4090/7393, mem: 8935Mb, iter_time: 0.428s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.106e-03, size: 704, ETA: 9 days, 17:46:34 2022-05-20 19:48:38.015 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4100/7393, mem: 8935Mb, iter_time: 0.328s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.109e-03, size: 384, ETA: 9 days, 17:45:26 2022-05-20 19:48:41.450 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4110/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 0.003s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.7, loss_l1: 1.4, lr: 5.112e-03, size: 576, ETA: 9 days, 17:44:36 2022-05-20 19:48:44.836 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4120/7393, mem: 8935Mb, iter_time: 0.338s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 5.115e-03, size: 512, ETA: 9 days, 17:43:40 2022-05-20 19:48:48.598 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4130/7393, mem: 8935Mb, iter_time: 0.376s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.117e-03, size: 608, ETA: 9 days, 17:43:28 2022-05-20 19:48:52.668 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4140/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.120e-03, size: 608, ETA: 9 days, 17:43:52 2022-05-20 19:48:57.626 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4150/7393, mem: 8935Mb, iter_time: 0.495s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.123e-03, size: 736, ETA: 9 days, 17:45:59 2022-05-20 19:49:03.434 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4160/7393, mem: 8935Mb, iter_time: 0.580s, data_time: 0.001s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.125e-03, size: 768, ETA: 9 days, 17:49:45 2022-05-20 19:49:06.917 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4170/7393, mem: 8935Mb, iter_time: 0.348s, data_time: 0.002s, total_loss: 8.5, loss_cls: 6.6, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.128e-03, size: 320, ETA: 9 days, 17:49:00 2022-05-20 19:49:09.590 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4180/7393, mem: 8935Mb, iter_time: 0.266s, data_time: 0.009s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 5.131e-03, size: 352, ETA: 9 days, 17:46:41 2022-05-20 19:49:13.603 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4190/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.006s, total_loss: 9.3, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.134e-03, size: 640, ETA: 9 days, 17:46:54 2022-05-20 19:49:16.690 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4200/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.136e-03, size: 384, ETA: 9 days, 17:45:24 2022-05-20 19:49:20.106 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4210/7393, mem: 8935Mb, iter_time: 0.341s, data_time: 0.005s, total_loss: 10.2, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.139e-03, size: 576, ETA: 9 days, 17:44:31 2022-05-20 19:49:23.129 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4220/7393, mem: 8935Mb, iter_time: 0.301s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.142e-03, size: 384, ETA: 9 days, 17:42:53 2022-05-20 19:49:27.182 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4230/7393, mem: 8935Mb, iter_time: 0.404s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 5.144e-03, size: 672, ETA: 9 days, 17:43:15 2022-05-20 19:49:31.749 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4240/7393, mem: 8935Mb, iter_time: 0.456s, data_time: 0.001s, total_loss: 9.7, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.147e-03, size: 640, ETA: 9 days, 17:44:36 2022-05-20 19:49:37.118 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4250/7393, mem: 8935Mb, iter_time: 0.536s, data_time: 0.001s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.150e-03, size: 768, ETA: 9 days, 17:47:30 2022-05-20 19:49:42.149 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4260/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.001s, total_loss: 10.6, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 2.0, lr: 5.152e-03, size: 640, ETA: 9 days, 17:49:44 2022-05-20 19:49:47.039 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4270/7393, mem: 8935Mb, iter_time: 0.489s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 5.155e-03, size: 704, ETA: 9 days, 17:51:42 2022-05-20 19:49:51.740 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4280/7393, mem: 8935Mb, iter_time: 0.470s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 5.158e-03, size: 640, ETA: 9 days, 17:53:19 2022-05-20 19:49:54.846 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4290/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.161e-03, size: 384, ETA: 9 days, 17:51:50 2022-05-20 19:49:57.790 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4300/7393, mem: 8935Mb, iter_time: 0.292s, data_time: 0.004s, total_loss: 8.7, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.163e-03, size: 512, ETA: 9 days, 17:50:02 2022-05-20 19:50:01.199 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4310/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 5.166e-03, size: 544, ETA: 9 days, 17:49:09 2022-05-20 19:50:05.311 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4320/7393, mem: 8935Mb, iter_time: 0.411s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.169e-03, size: 640, ETA: 9 days, 17:49:38 2022-05-20 19:50:10.647 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4330/7393, mem: 8935Mb, iter_time: 0.533s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.171e-03, size: 768, ETA: 9 days, 17:52:27 2022-05-20 19:50:15.027 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4340/7393, mem: 8935Mb, iter_time: 0.437s, data_time: 0.001s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.174e-03, size: 544, ETA: 9 days, 17:53:25 2022-05-20 19:50:18.103 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4350/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.177e-03, size: 480, ETA: 9 days, 17:51:54 2022-05-20 19:50:21.340 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4360/7393, mem: 8935Mb, iter_time: 0.323s, data_time: 0.003s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.179e-03, size: 512, ETA: 9 days, 17:50:42 2022-05-20 19:50:23.919 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4370/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.004s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.182e-03, size: 320, ETA: 9 days, 17:48:13 2022-05-20 19:50:26.658 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4380/7393, mem: 8935Mb, iter_time: 0.273s, data_time: 0.006s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 5.185e-03, size: 448, ETA: 9 days, 17:46:03 2022-05-20 19:50:31.588 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4390/7393, mem: 8935Mb, iter_time: 0.490s, data_time: 0.006s, total_loss: 11.0, loss_cls: 9.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 5.188e-03, size: 768, ETA: 9 days, 17:48:02 2022-05-20 19:50:36.861 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4400/7393, mem: 8935Mb, iter_time: 0.527s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.190e-03, size: 672, ETA: 9 days, 17:50:43 2022-05-20 19:50:39.883 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4410/7393, mem: 8935Mb, iter_time: 0.301s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.193e-03, size: 352, ETA: 9 days, 17:49:06 2022-05-20 19:50:43.528 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4420/7393, mem: 8935Mb, iter_time: 0.363s, data_time: 0.004s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.196e-03, size: 640, ETA: 9 days, 17:48:40 2022-05-20 19:50:48.924 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4430/7393, mem: 8935Mb, iter_time: 0.539s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.7, lr: 5.198e-03, size: 768, ETA: 9 days, 17:51:35 2022-05-20 19:50:52.846 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4440/7393, mem: 8935Mb, iter_time: 0.392s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 5.201e-03, size: 448, ETA: 9 days, 17:51:41 2022-05-20 19:50:55.316 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4450/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.004s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.204e-03, size: 384, ETA: 9 days, 17:49:01 2022-05-20 19:50:59.332 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4460/7393, mem: 8935Mb, iter_time: 0.401s, data_time: 0.003s, total_loss: 9.4, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.207e-03, size: 672, ETA: 9 days, 17:49:17 2022-05-20 19:51:03.073 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4470/7393, mem: 8935Mb, iter_time: 0.374s, data_time: 0.002s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.209e-03, size: 480, ETA: 9 days, 17:49:03 2022-05-20 19:51:06.893 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4480/7393, mem: 8935Mb, iter_time: 0.381s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.8, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.4, lr: 5.212e-03, size: 640, ETA: 9 days, 17:48:57 2022-05-20 19:51:10.407 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4490/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.215e-03, size: 480, ETA: 9 days, 17:48:17 2022-05-20 19:51:15.086 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4500/7393, mem: 8935Mb, iter_time: 0.467s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.9, lr: 5.217e-03, size: 736, ETA: 9 days, 17:49:49 2022-05-20 19:51:19.931 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4510/7393, mem: 8935Mb, iter_time: 0.484s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.220e-03, size: 640, ETA: 9 days, 17:51:41 2022-05-20 19:51:24.121 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4520/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 5.223e-03, size: 608, ETA: 9 days, 17:52:18 2022-05-20 19:51:27.019 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4530/7393, mem: 8935Mb, iter_time: 0.289s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 5.225e-03, size: 384, ETA: 9 days, 17:50:27 2022-05-20 19:51:29.391 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4540/7393, mem: 8935Mb, iter_time: 0.236s, data_time: 0.004s, total_loss: 9.0, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 5.228e-03, size: 352, ETA: 9 days, 17:47:36 2022-05-20 19:51:32.911 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4550/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.005s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.231e-03, size: 512, ETA: 9 days, 17:46:56 2022-05-20 19:51:37.660 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4560/7393, mem: 8935Mb, iter_time: 0.474s, data_time: 0.002s, total_loss: 10.9, loss_cls: 9.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.9, lr: 5.234e-03, size: 736, ETA: 9 days, 17:48:36 2022-05-20 19:51:43.256 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4570/7393, mem: 8935Mb, iter_time: 0.559s, data_time: 0.001s, total_loss: 10.9, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.236e-03, size: 736, ETA: 9 days, 17:51:52 2022-05-20 19:51:46.730 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4580/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.239e-03, size: 384, ETA: 9 days, 17:51:07 2022-05-20 19:51:51.279 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4590/7393, mem: 8935Mb, iter_time: 0.454s, data_time: 0.004s, total_loss: 9.5, loss_cls: 7.6, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 5.242e-03, size: 768, ETA: 9 days, 17:52:24 2022-05-20 19:51:56.526 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4600/7393, mem: 8935Mb, iter_time: 0.524s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.244e-03, size: 672, ETA: 9 days, 17:55:01 2022-05-20 19:52:01.647 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4610/7393, mem: 8935Mb, iter_time: 0.512s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 5.247e-03, size: 704, ETA: 9 days, 17:57:22 2022-05-20 19:52:04.841 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4620/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.250e-03, size: 352, ETA: 9 days, 17:56:06 2022-05-20 19:52:07.432 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4630/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.004s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.253e-03, size: 448, ETA: 9 days, 17:53:39 2022-05-20 19:52:09.836 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4640/7393, mem: 8935Mb, iter_time: 0.239s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.255e-03, size: 352, ETA: 9 days, 17:50:52 2022-05-20 19:52:12.611 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4650/7393, mem: 8935Mb, iter_time: 0.276s, data_time: 0.006s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.9, lr: 5.258e-03, size: 416, ETA: 9 days, 17:48:48 2022-05-20 19:52:15.165 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4660/7393, mem: 8935Mb, iter_time: 0.254s, data_time: 0.005s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.261e-03, size: 320, ETA: 9 days, 17:46:18 2022-05-20 19:52:17.901 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4670/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.004s, total_loss: 8.3, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.263e-03, size: 320, ETA: 9 days, 17:44:09 2022-05-20 19:52:20.720 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4680/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.008s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.266e-03, size: 352, ETA: 9 days, 17:42:10 2022-05-20 19:52:23.373 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4690/7393, mem: 8935Mb, iter_time: 0.264s, data_time: 0.008s, total_loss: 9.7, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 5.269e-03, size: 384, ETA: 9 days, 17:39:52 2022-05-20 19:52:26.194 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4700/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.006s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 5.271e-03, size: 352, ETA: 9 days, 17:37:53 2022-05-20 19:52:29.402 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4710/7393, mem: 8935Mb, iter_time: 0.320s, data_time: 0.012s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.274e-03, size: 416, ETA: 9 days, 17:36:38 2022-05-20 19:52:32.394 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4720/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.006s, total_loss: 8.6, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 5.277e-03, size: 480, ETA: 9 days, 17:34:59 2022-05-20 19:52:37.172 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4730/7393, mem: 8935Mb, iter_time: 0.477s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 5.280e-03, size: 768, ETA: 9 days, 17:36:42 2022-05-20 19:52:42.559 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4740/7393, mem: 8935Mb, iter_time: 0.538s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.282e-03, size: 672, ETA: 9 days, 17:39:33 2022-05-20 19:52:47.113 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4750/7393, mem: 8935Mb, iter_time: 0.455s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 5.285e-03, size: 640, ETA: 9 days, 17:40:50 2022-05-20 19:52:50.944 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4760/7393, mem: 8935Mb, iter_time: 0.383s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.288e-03, size: 544, ETA: 9 days, 17:40:47 2022-05-20 19:52:55.782 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4770/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 11.7, loss_cls: 10.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.6, lr: 5.290e-03, size: 736, ETA: 9 days, 17:42:36 2022-05-20 19:53:00.482 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4780/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.293e-03, size: 608, ETA: 9 days, 17:44:09 2022-05-20 19:53:04.574 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4790/7393, mem: 8935Mb, iter_time: 0.409s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.4, lr: 5.296e-03, size: 608, ETA: 9 days, 17:44:35 2022-05-20 19:53:08.514 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4800/7393, mem: 8935Mb, iter_time: 0.393s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.299e-03, size: 576, ETA: 9 days, 17:44:43 2022-05-20 19:53:12.500 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4810/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.301e-03, size: 608, ETA: 9 days, 17:44:56 2022-05-20 19:53:17.049 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4820/7393, mem: 8935Mb, iter_time: 0.454s, data_time: 0.001s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 5.304e-03, size: 672, ETA: 9 days, 17:46:12 2022-05-20 19:53:22.438 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4830/7393, mem: 8935Mb, iter_time: 0.538s, data_time: 0.002s, total_loss: 10.7, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.307e-03, size: 736, ETA: 9 days, 17:49:03 2022-05-20 19:53:25.656 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4840/7393, mem: 8935Mb, iter_time: 0.321s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.1, lr: 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0.455s, data_time: 0.004s, total_loss: 10.9, loss_cls: 9.0, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 0.9, lr: 5.320e-03, size: 768, ETA: 9 days, 17:45:44 2022-05-20 19:53:46.084 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4890/7393, mem: 8935Mb, iter_time: 0.606s, data_time: 0.001s, total_loss: 12.0, loss_cls: 10.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 5.323e-03, size: 768, ETA: 9 days, 17:49:49 2022-05-20 19:53:52.019 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4900/7393, mem: 8935Mb, iter_time: 0.593s, data_time: 0.001s, total_loss: 10.8, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 5.326e-03, size: 768, ETA: 9 days, 17:53:40 2022-05-20 19:53:57.526 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4910/7393, mem: 8935Mb, iter_time: 0.550s, data_time: 0.001s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 5.328e-03, size: 704, ETA: 9 days, 17:56:42 2022-05-20 19:54:00.706 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4920/7393, mem: 8935Mb, iter_time: 0.317s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.331e-03, size: 320, ETA: 9 days, 17:55:25 2022-05-20 19:54:04.629 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4930/7393, mem: 8935Mb, iter_time: 0.391s, data_time: 0.006s, total_loss: 10.7, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.0, lr: 5.334e-03, size: 640, ETA: 9 days, 17:55:31 2022-05-20 19:54:08.148 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4940/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.336e-03, size: 480, ETA: 9 days, 17:54:51 2022-05-20 19:54:12.730 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 4950/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.002s, total_loss: 10.6, loss_cls: 9.0, 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iter: 4990/7393, mem: 8935Mb, iter_time: 0.356s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.350e-03, size: 512, ETA: 9 days, 17:59:06 2022-05-20 19:54:34.601 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5000/7393, mem: 8935Mb, iter_time: 0.480s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.353e-03, size: 736, ETA: 9 days, 18:00:50 2022-05-20 19:54:38.840 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5010/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.355e-03, size: 544, ETA: 9 days, 18:01:30 2022-05-20 19:54:42.837 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5020/7393, mem: 8935Mb, iter_time: 0.399s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.358e-03, size: 640, ETA: 9 days, 18:01:44 2022-05-20 19:54:46.813 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5030/7393, mem: 8935Mb, iter_time: 0.397s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 5.361e-03, size: 576, ETA: 9 days, 18:01:56 2022-05-20 19:54:49.618 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5040/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.363e-03, size: 352, ETA: 9 days, 17:59:57 2022-05-20 19:54:52.014 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5050/7393, mem: 8935Mb, iter_time: 0.239s, data_time: 0.007s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.366e-03, size: 320, ETA: 9 days, 17:57:13 2022-05-20 19:54:54.976 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5060/7393, mem: 8935Mb, iter_time: 0.295s, data_time: 0.008s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.369e-03, size: 512, ETA: 9 days, 17:55:32 2022-05-20 19:54:59.415 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5070/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.3, lr: 5.372e-03, size: 704, ETA: 9 days, 17:56:34 2022-05-20 19:55:04.659 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5080/7393, mem: 8935Mb, iter_time: 0.524s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 5.374e-03, size: 704, ETA: 9 days, 17:59:06 2022-05-20 19:55:07.765 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5090/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.377e-03, size: 320, ETA: 9 days, 17:57:41 2022-05-20 19:55:10.221 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5100/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.005s, total_loss: 8.5, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.380e-03, size: 416, ETA: 9 days, 17:55:04 2022-05-20 19:55:15.190 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5110/7393, mem: 8935Mb, iter_time: 0.495s, data_time: 0.004s, total_loss: 9.7, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.382e-03, size: 768, ETA: 9 days, 17:57:03 2022-05-20 19:55:18.542 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5120/7393, mem: 8935Mb, iter_time: 0.335s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.385e-03, size: 320, ETA: 9 days, 17:56:06 2022-05-20 19:55:23.028 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5130/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.004s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.416s, data_time: 0.004s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 5.399e-03, size: 704, ETA: 9 days, 17:59:15 2022-05-20 19:55:43.962 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5180/7393, mem: 8935Mb, iter_time: 0.434s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.401e-03, size: 576, ETA: 9 days, 18:00:07 2022-05-20 19:55:48.437 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5190/7393, mem: 8935Mb, iter_time: 0.447s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.6, lr: 5.404e-03, size: 672, ETA: 9 days, 18:01:13 2022-05-20 19:55:52.233 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5200/7393, mem: 8935Mb, iter_time: 0.379s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.407e-03, size: 512, ETA: 9 days, 18:01:04 2022-05-20 19:55:56.101 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5210/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 5.409e-03, size: 640, ETA: 9 days, 18:01:04 2022-05-20 19:55:59.137 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5220/7393, mem: 8935Mb, iter_time: 0.303s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.412e-03, size: 384, ETA: 9 days, 17:59:31 2022-05-20 19:56:02.583 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5230/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.415e-03, size: 608, ETA: 9 days, 17:58:43 2022-05-20 19:56:07.172 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5240/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.418e-03, size: 672, ETA: 9 days, 18:00:02 2022-05-20 19:56:11.244 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5250/7393, mem: 8935Mb, iter_time: 0.407s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.2, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.420e-03, size: 544, ETA: 9 days, 18:00:23 2022-05-20 19:56:16.182 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5260/7393, mem: 8935Mb, iter_time: 0.493s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.423e-03, size: 768, ETA: 9 days, 18:02:20 2022-05-20 19:56:19.762 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5270/7393, mem: 8935Mb, iter_time: 0.357s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 5.426e-03, size: 384, ETA: 9 days, 18:01:48 2022-05-20 19:56:24.347 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5280/7393, mem: 8935Mb, iter_time: 0.458s, data_time: 0.003s, total_loss: 10.9, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.428e-03, size: 768, ETA: 9 days, 18:03:05 2022-05-20 19:56:28.414 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5290/7393, mem: 8935Mb, iter_time: 0.406s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 5.431e-03, size: 480, ETA: 9 days, 18:03:27 2022-05-20 19:56:32.090 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5300/7393, mem: 8935Mb, iter_time: 0.367s, data_time: 0.003s, total_loss: 10.7, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.434e-03, size: 608, ETA: 9 days, 18:03:04 2022-05-20 19:56:34.911 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5310/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.436e-03, size: 352, ETA: 9 days, 18:01:09 2022-05-20 19:56:38.344 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5320/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.004s, total_loss: 8.9, loss_cls: 6.9, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.3, lr: 5.439e-03, size: 544, ETA: 9 days, 18:00:20 2022-05-20 19:56:41.642 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5330/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.442e-03, size: 512, ETA: 9 days, 17:59:17 2022-05-20 19:56:44.778 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5340/7393, mem: 8935Mb, iter_time: 0.313s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 5.445e-03, size: 512, ETA: 9 days, 17:57:56 2022-05-20 19:56:49.229 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5350/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 5.447e-03, size: 704, ETA: 9 days, 17:58:59 2022-05-20 19:56:54.894 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5360/7393, mem: 8935Mb, iter_time: 0.566s, data_time: 0.001s, total_loss: 8.8, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.450e-03, size: 768, ETA: 9 days, 18:02:15 2022-05-20 19:56:58.315 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5370/7393, mem: 8935Mb, iter_time: 0.341s, data_time: 0.001s, total_loss: 8.4, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.453e-03, size: 320, ETA: 9 days, 18:01:25 2022-05-20 19:57:01.650 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5380/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.003s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 5.455e-03, size: 576, ETA: 9 days, 18:00:26 2022-05-20 19:57:04.503 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5390/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.458e-03, size: 352, ETA: 9 days, 17:58:35 2022-05-20 19:57:07.437 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5400/7393, mem: 8935Mb, iter_time: 0.290s, data_time: 0.005s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.3, lr: 5.461e-03, size: 448, ETA: 9 days, 17:56:49 2022-05-20 19:57:12.075 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5410/7393, mem: 8935Mb, iter_time: 0.463s, data_time: 0.004s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 5.464e-03, size: 736, ETA: 9 days, 17:58:12 2022-05-20 19:57:15.660 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5420/7393, mem: 8935Mb, iter_time: 0.358s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, 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5460/7393, mem: 8935Mb, iter_time: 0.466s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 5.477e-03, size: 736, ETA: 9 days, 17:52:43 2022-05-20 19:57:33.144 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5470/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.001s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.480e-03, size: 608, ETA: 9 days, 17:54:13 2022-05-20 19:57:35.928 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5480/7393, mem: 8935Mb, iter_time: 0.278s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.482e-03, size: 320, ETA: 9 days, 17:52:15 2022-05-20 19:57:39.913 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5490/7393, mem: 8935Mb, iter_time: 0.397s, data_time: 0.004s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 5.485e-03, size: 640, ETA: 9 days, 17:52:27 2022-05-20 19:57:43.267 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5500/7393, mem: 8935Mb, iter_time: 0.335s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.488e-03, size: 448, ETA: 9 days, 17:51:30 2022-05-20 19:57:45.734 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5510/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.491e-03, size: 384, ETA: 9 days, 17:48:58 2022-05-20 19:57:48.749 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5520/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.005s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.493e-03, size: 512, ETA: 9 days, 17:47:25 2022-05-20 19:57:51.658 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5530/7393, mem: 8935Mb, iter_time: 0.288s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.496e-03, size: 448, ETA: 9 days, 17:45:38 2022-05-20 19:57:54.625 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5540/7393, mem: 8935Mb, iter_time: 0.296s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.499e-03, size: 512, ETA: 9 days, 17:44:00 2022-05-20 19:57:58.954 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5550/7393, mem: 8935Mb, iter_time: 0.432s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.501e-03, size: 672, ETA: 9 days, 17:44:50 2022-05-20 19:58:02.356 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5560/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.504e-03, size: 416, ETA: 9 days, 17:43:59 2022-05-20 19:58:04.759 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5570/7393, mem: 8935Mb, iter_time: 0.239s, data_time: 0.006s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.507e-03, size: 384, ETA: 9 days, 17:41:20 2022-05-20 19:58:07.173 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5580/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.005s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.510e-03, size: 320, ETA: 9 days, 17:38:43 2022-05-20 19:58:09.809 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5590/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.005s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.512e-03, size: 320, ETA: 9 days, 17:36:29 2022-05-20 19:58:12.086 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5600/7393, mem: 8935Mb, iter_time: 0.226s, data_time: 0.004s, total_loss: 8.8, loss_cls: 6.9, loss_iou: 0.5, loss_dfl: 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8935Mb, iter_time: 0.411s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.526e-03, size: 480, ETA: 9 days, 17:32:56 2022-05-20 19:58:31.029 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5650/7393, mem: 8935Mb, iter_time: 0.384s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.528e-03, size: 640, ETA: 9 days, 17:32:53 2022-05-20 19:58:35.869 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5660/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.7, lr: 5.531e-03, size: 704, ETA: 9 days, 17:34:38 2022-05-20 19:58:41.136 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5670/7393, mem: 8935Mb, iter_time: 0.526s, data_time: 0.001s, total_loss: 10.8, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.534e-03, size: 704, ETA: 9 days, 17:37:08 2022-05-20 19:58:44.408 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5680/7393, mem: 8935Mb, iter_time: 0.327s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.537e-03, size: 352, ETA: 9 days, 17:36:04 2022-05-20 19:58:47.167 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5690/7393, mem: 8935Mb, iter_time: 0.275s, data_time: 0.008s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.539e-03, size: 480, ETA: 9 days, 17:34:04 2022-05-20 19:58:51.170 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5700/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.003s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.542e-03, size: 640, ETA: 9 days, 17:34:19 2022-05-20 19:58:54.380 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5710/7393, mem: 8935Mb, iter_time: 0.320s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 5.545e-03, size: 384, ETA: 9 days, 17:33:08 2022-05-20 19:58:57.714 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5720/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.006s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.547e-03, size: 544, ETA: 9 days, 17:32:10 2022-05-20 19:59:01.723 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5730/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.550e-03, size: 608, ETA: 9 days, 17:32:25 2022-05-20 19:59:04.469 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5740/7393, mem: 8935Mb, iter_time: 0.274s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.553e-03, size: 352, ETA: 9 days, 17:30:25 2022-05-20 19:59:07.092 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5750/7393, mem: 8935Mb, iter_time: 0.261s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.556e-03, size: 448, ETA: 9 days, 17:28:11 2022-05-20 19:59:11.929 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5760/7393, mem: 8935Mb, iter_time: 0.483s, data_time: 0.002s, total_loss: 11.3, loss_cls: 9.9, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.558e-03, size: 768, ETA: 9 days, 17:29:55 2022-05-20 19:59:17.004 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5770/7393, mem: 8935Mb, iter_time: 0.507s, data_time: 0.002s, total_loss: 11.4, loss_cls: 9.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.561e-03, size: 640, ETA: 9 days, 17:32:04 2022-05-20 19:59:19.813 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5780/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.002s, total_loss: 8.7, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.564e-03, size: 320, ETA: 9 days, 17:30:11 2022-05-20 19:59:23.332 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5790/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.004s, total_loss: 9.2, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.566e-03, size: 608, ETA: 9 days, 17:29:34 2022-05-20 19:59:27.465 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5800/7393, mem: 8935Mb, iter_time: 0.413s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 5.569e-03, size: 576, ETA: 9 days, 17:30:02 2022-05-20 19:59:32.671 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5810/7393, mem: 8935Mb, iter_time: 0.520s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.1, lr: 5.572e-03, size: 768, ETA: 9 days, 17:32:25 2022-05-20 19:59:37.279 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5820/7393, mem: 8935Mb, iter_time: 0.460s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 5.574e-03, size: 576, ETA: 9 days, 17:33:44 2022-05-20 19:59:42.124 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5830/7393, mem: 8935Mb, iter_time: 0.484s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.577e-03, size: 736, ETA: 9 days, 17:35:28 2022-05-20 19:59:47.699 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5840/7393, mem: 8935Mb, iter_time: 0.557s, data_time: 0.001s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.580e-03, size: 736, ETA: 9 days, 17:38:30 2022-05-20 19:59:51.152 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5850/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.001s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.583e-03, size: 384, ETA: 9 days, 17:37:45 2022-05-20 19:59:53.821 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5860/7393, mem: 8935Mb, iter_time: 0.266s, data_time: 0.005s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 5.585e-03, size: 448, ETA: 9 days, 17:35:37 2022-05-20 19:59:56.919 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5870/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.008s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 5.588e-03, size: 448, ETA: 9 days, 17:34:14 2022-05-20 20:00:01.531 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5880/7393, mem: 8935Mb, iter_time: 0.460s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.591e-03, size: 736, ETA: 9 days, 17:35:33 2022-05-20 20:00:06.685 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5890/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, 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5930/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.604e-03, size: 384, ETA: 9 days, 17:35:45 2022-05-20 20:00:24.147 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5940/7393, mem: 8935Mb, iter_time: 0.315s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.9, lr: 5.607e-03, size: 544, ETA: 9 days, 17:34:29 2022-05-20 20:00:26.743 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5950/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.610e-03, size: 352, ETA: 9 days, 17:32:14 2022-05-20 20:00:31.286 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5960/7393, mem: 8935Mb, iter_time: 0.454s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 5.612e-03, size: 768, ETA: 9 days, 17:33:25 2022-05-20 20:00:35.497 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5970/7393, mem: 8935Mb, iter_time: 0.421s, data_time: 0.001s, total_loss: 11.0, loss_cls: 9.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.615e-03, size: 512, ETA: 9 days, 17:34:02 2022-05-20 20:00:38.053 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5980/7393, mem: 8935Mb, iter_time: 0.255s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.618e-03, size: 320, ETA: 9 days, 17:31:42 2022-05-20 20:00:42.168 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 5990/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.004s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.7, lr: 5.620e-03, size: 672, ETA: 9 days, 17:32:08 2022-05-20 20:00:45.458 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6000/7393, mem: 8935Mb, iter_time: 0.328s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.3, loss_l1: 1.4, lr: 5.623e-03, size: 416, ETA: 9 days, 17:31:07 2022-05-20 20:00:48.219 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6010/7393, mem: 8935Mb, iter_time: 0.275s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.626e-03, size: 480, ETA: 9 days, 17:29:09 2022-05-20 20:00:52.204 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6020/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.629e-03, size: 640, ETA: 9 days, 17:29:22 2022-05-20 20:00:56.536 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6030/7393, mem: 8935Mb, iter_time: 0.433s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.631e-03, size: 608, ETA: 9 days, 17:30:11 2022-05-20 20:01:01.714 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6040/7393, mem: 8935Mb, iter_time: 0.517s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 5.634e-03, size: 768, ETA: 9 days, 17:32:29 2022-05-20 20:01:06.101 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6050/7393, mem: 8935Mb, iter_time: 0.438s, data_time: 0.001s, total_loss: 10.7, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.637e-03, size: 544, ETA: 9 days, 17:33:24 2022-05-20 20:01:08.527 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6060/7393, mem: 8935Mb, iter_time: 0.242s, data_time: 0.002s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.639e-03, size: 320, ETA: 9 days, 17:30:51 2022-05-20 20:01:11.586 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6070/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.005s, total_loss: 9.7, loss_cls: 8.2, loss_iou: 0.3, 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days, 17:25:05 2022-05-20 20:01:42.183 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6150/7393, mem: 8935Mb, iter_time: 0.597s, data_time: 0.001s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.664e-03, size: 768, ETA: 9 days, 17:28:46 2022-05-20 20:01:47.207 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6160/7393, mem: 8935Mb, iter_time: 0.502s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 5.666e-03, size: 640, ETA: 9 days, 17:30:48 2022-05-20 20:01:50.664 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6170/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 5.669e-03, size: 480, ETA: 9 days, 17:30:04 2022-05-20 20:01:52.882 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6180/7393, mem: 8935Mb, iter_time: 0.221s, data_time: 0.005s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 5.672e-03, size: 320, ETA: 9 days, 17:27:11 2022-05-20 20:01:56.900 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6190/7393, mem: 8935Mb, iter_time: 0.401s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 5.675e-03, size: 672, ETA: 9 days, 17:27:26 2022-05-20 20:02:02.246 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6200/7393, mem: 8935Mb, iter_time: 0.534s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.677e-03, size: 736, ETA: 9 days, 17:30:01 2022-05-20 20:02:06.709 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6210/7393, mem: 8935Mb, iter_time: 0.446s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 5.680e-03, size: 576, ETA: 9 days, 17:31:03 2022-05-20 20:02:11.156 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6220/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.683e-03, size: 672, ETA: 9 days, 17:32:04 2022-05-20 20:02:14.237 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6230/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.685e-03, size: 320, ETA: 9 days, 17:30:41 2022-05-20 20:02:17.018 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6240/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.688e-03, size: 448, ETA: 9 days, 17:28:46 2022-05-20 20:02:19.721 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6250/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.004s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 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data_time: 0.002s, total_loss: 8.2, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.750e-03, size: 480, ETA: 9 days, 17:28:20 2022-05-20 20:03:48.884 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6480/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.753e-03, size: 512, ETA: 9 days, 17:27:00 2022-05-20 20:03:53.781 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6490/7393, mem: 8935Mb, iter_time: 0.489s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.756e-03, size: 768, ETA: 9 days, 17:28:46 2022-05-20 20:03:59.801 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6500/7393, mem: 8935Mb, iter_time: 0.602s, data_time: 0.001s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.758e-03, size: 768, ETA: 9 days, 17:32:28 2022-05-20 20:04:03.727 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6510/7393, mem: 8935Mb, iter_time: 0.392s, data_time: 0.001s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.761e-03, size: 448, ETA: 9 days, 17:32:34 2022-05-20 20:04:06.520 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6520/7393, mem: 8935Mb, iter_time: 0.278s, data_time: 0.003s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.764e-03, size: 416, ETA: 9 days, 17:30:42 2022-05-20 20:04:11.304 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6530/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.004s, total_loss: 11.1, loss_cls: 9.6, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.6, lr: 5.767e-03, size: 768, ETA: 9 days, 17:32:16 2022-05-20 20:04:15.904 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6540/7393, mem: 8935Mb, iter_time: 0.460s, data_time: 0.001s, total_loss: 10.6, loss_cls: 9.1, loss_iou: 0.4, 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days, 17:27:49 2022-05-20 20:04:42.935 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6620/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.791e-03, size: 416, ETA: 9 days, 17:26:46 2022-05-20 20:04:46.133 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6630/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.004s, total_loss: 9.2, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 5.794e-03, size: 544, ETA: 9 days, 17:25:37 2022-05-20 20:04:48.876 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6640/7393, mem: 8935Mb, iter_time: 0.273s, data_time: 0.003s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.796e-03, size: 352, ETA: 9 days, 17:23:41 2022-05-20 20:04:51.177 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6650/7393, mem: 8935Mb, iter_time: 0.229s, data_time: 0.004s, total_loss: 7.8, loss_cls: 6.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.799e-03, size: 320, ETA: 9 days, 17:21:00 2022-05-20 20:04:55.799 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6660/7393, mem: 8935Mb, iter_time: 0.461s, data_time: 0.003s, total_loss: 10.5, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 2.0, lr: 5.802e-03, size: 768, ETA: 9 days, 17:22:17 2022-05-20 20:04:59.696 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6670/7393, mem: 8935Mb, iter_time: 0.389s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.804e-03, size: 448, ETA: 9 days, 17:22:20 2022-05-20 20:05:01.992 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6680/7393, mem: 8935Mb, iter_time: 0.229s, data_time: 0.004s, total_loss: 9.1, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 5.807e-03, size: 352, ETA: 9 days, 17:19:39 2022-05-20 20:05:04.904 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6690/7393, mem: 8935Mb, iter_time: 0.288s, data_time: 0.009s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.810e-03, size: 448, ETA: 9 days, 17:17:58 2022-05-20 20:05:08.978 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6700/7393, mem: 8935Mb, iter_time: 0.406s, data_time: 0.004s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.813e-03, size: 640, ETA: 9 days, 17:18:19 2022-05-20 20:05:12.833 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6710/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.815e-03, size: 544, ETA: 9 days, 17:18:18 2022-05-20 20:05:15.613 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6720/7393, mem: 8935Mb, iter_time: 0.276s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.345s, data_time: 0.005s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 5.829e-03, size: 576, ETA: 9 days, 17:11:18 2022-05-20 20:05:30.922 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6770/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.831e-03, size: 320, ETA: 9 days, 17:09:31 2022-05-20 20:05:33.824 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6780/7393, mem: 8935Mb, iter_time: 0.289s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 5.834e-03, size: 512, ETA: 9 days, 17:07:52 2022-05-20 20:05:36.737 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6790/7393, mem: 8935Mb, iter_time: 0.290s, data_time: 0.003s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.1, loss_l1: 0.6, lr: 5.837e-03, size: 416, ETA: 9 days, 17:06:15 2022-05-20 20:05:39.159 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6800/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.004s, total_loss: 9.2, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 5.840e-03, size: 320, ETA: 9 days, 17:03:48 2022-05-20 20:05:41.914 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6810/7393, mem: 8935Mb, iter_time: 0.274s, data_time: 0.003s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 5.842e-03, size: 448, ETA: 9 days, 17:01:55 2022-05-20 20:05:45.646 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6820/7393, mem: 8935Mb, iter_time: 0.372s, data_time: 0.004s, total_loss: 8.2, loss_cls: 6.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 5.845e-03, size: 576, ETA: 9 days, 17:01:41 2022-05-20 20:05:50.753 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6830/7393, mem: 8935Mb, iter_time: 0.510s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.6, lr: 5.848e-03, size: 768, ETA: 9 days, 17:03:47 2022-05-20 20:05:56.071 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6840/7393, mem: 8935Mb, iter_time: 0.531s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 5.850e-03, size: 672, ETA: 9 days, 17:06:15 2022-05-20 20:06:00.290 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6850/7393, mem: 8935Mb, iter_time: 0.421s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 5.853e-03, size: 576, ETA: 9 days, 17:06:51 2022-05-20 20:06:05.329 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6860/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.856e-03, size: 768, ETA: 9 days, 17:08:50 2022-05-20 20:06:09.710 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6870/7393, mem: 8935Mb, iter_time: 0.438s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.859e-03, size: 544, ETA: 9 days, 17:09:43 2022-05-20 20:06:12.487 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6880/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.7, lr: 5.861e-03, size: 416, ETA: 9 days, 17:07:52 2022-05-20 20:06:16.981 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6890/7393, mem: 8935Mb, iter_time: 0.449s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 5.864e-03, size: 736, ETA: 9 days, 17:08:56 2022-05-20 20:06:21.083 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6900/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 5.867e-03, size: 512, ETA: 9 days, 17:09:20 2022-05-20 20:06:24.146 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6910/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.002s, total_loss: 10.7, loss_cls: 9.3, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.6, lr: 5.869e-03, size: 512, ETA: 9 days, 17:07:58 2022-05-20 20:06:26.486 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6920/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.004s, total_loss: 8.9, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 5.872e-03, size: 320, ETA: 9 days, 17:05:24 2022-05-20 20:06:30.178 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6930/7393, mem: 8935Mb, iter_time: 0.368s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.875e-03, size: 640, ETA: 9 days, 17:05:06 2022-05-20 20:06:35.604 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6940/7393, mem: 8935Mb, iter_time: 0.542s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.877e-03, size: 768, ETA: 9 days, 17:07:44 2022-05-20 20:06:40.512 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6950/7393, mem: 8935Mb, iter_time: 0.490s, data_time: 0.001s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 5.880e-03, size: 608, ETA: 9 days, 17:09:29 2022-05-20 20:06:44.522 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6960/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.883e-03, size: 576, ETA: 9 days, 17:09:44 2022-05-20 20:06:48.470 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6970/7393, mem: 8935Mb, iter_time: 0.394s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.886e-03, size: 608, ETA: 9 days, 17:09:52 2022-05-20 20:06:53.168 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6980/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 5.888e-03, size: 704, ETA: 9 days, 17:11:16 2022-05-20 20:06:56.892 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 6990/7393, mem: 8935Mb, iter_time: 0.372s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.9, lr: 5.891e-03, size: 480, ETA: 9 days, 17:11:02 2022-05-20 20:06:59.677 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7000/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 1.7, lr: 5.894e-03, size: 448, ETA: 9 days, 17:09:12 2022-05-20 20:07:02.139 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7010/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.005s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, 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days, 17:00:33 2022-05-20 20:07:29.994 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7090/7393, mem: 8935Mb, iter_time: 0.452s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.6, lr: 5.918e-03, size: 704, ETA: 9 days, 17:01:40 2022-05-20 20:07:33.706 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7100/7393, mem: 8935Mb, iter_time: 0.370s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 5.921e-03, size: 480, ETA: 9 days, 17:01:24 2022-05-20 20:07:36.214 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7110/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.923e-03, size: 384, ETA: 9 days, 16:59:08 2022-05-20 20:07:39.363 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7120/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 5.926e-03, size: 544, ETA: 9 days, 16:57:56 2022-05-20 20:07:43.275 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7130/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 5.929e-03, size: 576, ETA: 9 days, 16:58:00 2022-05-20 20:07:45.922 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7140/7393, mem: 8935Mb, iter_time: 0.264s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.932e-03, size: 320, ETA: 9 days, 16:55:58 2022-05-20 20:07:49.888 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7150/7393, mem: 8935Mb, iter_time: 0.395s, data_time: 0.007s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.934e-03, size: 672, ETA: 9 days, 16:56:08 2022-05-20 20:07:53.874 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7160/7393, mem: 8935Mb, iter_time: 0.398s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.5, lr: 5.937e-03, size: 544, ETA: 9 days, 16:56:20 2022-05-20 20:07:56.358 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7170/7393, mem: 8935Mb, iter_time: 0.248s, data_time: 0.003s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 5.940e-03, size: 320, ETA: 9 days, 16:54:02 2022-05-20 20:07:58.946 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7180/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.004s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.942e-03, size: 448, ETA: 9 days, 16:51:52 2022-05-20 20:08:03.596 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7190/7393, mem: 8935Mb, iter_time: 0.464s, data_time: 0.004s, total_loss: 10.5, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.945e-03, size: 736, ETA: 9 days, 16:53:11 2022-05-20 20:08:08.292 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7200/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.001s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.948e-03, size: 608, ETA: 9 days, 16:54:34 2022-05-20 20:08:13.080 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7210/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.950e-03, size: 704, ETA: 9 days, 16:56:07 2022-05-20 20:08:16.338 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7220/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.953e-03, size: 384, ETA: 9 days, 16:55:07 2022-05-20 20:08:20.666 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7230/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.003s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.956e-03, size: 736, ETA: 9 days, 16:55:52 2022-05-20 20:08:25.419 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7240/7393, mem: 8935Mb, iter_time: 0.475s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.959e-03, size: 608, ETA: 9 days, 16:57:21 2022-05-20 20:08:29.237 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7250/7393, mem: 8935Mb, iter_time: 0.381s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 5.961e-03, size: 544, ETA: 9 days, 16:57:16 2022-05-20 20:08:32.065 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7260/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.964e-03, size: 416, ETA: 9 days, 16:55:33 2022-05-20 20:08:35.702 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7270/7393, mem: 8935Mb, iter_time: 0.363s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 5.967e-03, size: 608, ETA: 9 days, 16:55:10 2022-05-20 20:08:40.364 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7280/7393, mem: 8935Mb, iter_time: 0.466s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.6, lr: 5.969e-03, size: 672, ETA: 9 days, 16:56:29 2022-05-20 20:08:44.901 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7290/7393, mem: 8935Mb, iter_time: 0.453s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.972e-03, size: 640, ETA: 9 days, 16:57:36 2022-05-20 20:08:48.653 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7300/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 5.975e-03, size: 544, ETA: 9 days, 16:57:25 2022-05-20 20:08:51.146 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7310/7393, mem: 8935Mb, iter_time: 0.249s, data_time: 0.003s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 5.978e-03, size: 320, ETA: 9 days, 16:55:09 2022-05-20 20:08:55.873 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7320/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.007s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.980e-03, size: 768, ETA: 9 days, 16:56:34 2022-05-20 20:09:00.911 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7330/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.001s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 5.983e-03, size: 640, ETA: 9 days, 16:58:31 2022-05-20 20:09:04.363 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7340/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 5.986e-03, size: 480, ETA: 9 days, 16:57:50 2022-05-20 20:09:07.692 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7350/7393, mem: 8935Mb, iter_time: 0.332s, data_time: 0.002s, total_loss: 11.1, loss_cls: 9.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 5.988e-03, size: 544, ETA: 9 days, 16:56:57 2022-05-20 20:09:12.637 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7360/7393, mem: 8935Mb, iter_time: 0.494s, data_time: 0.002s, total_loss: 11.0, loss_cls: 9.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.9, lr: 5.991e-03, size: 736, ETA: 9 days, 16:58:44 2022-05-20 20:09:15.953 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7370/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 5.994e-03, size: 352, ETA: 9 days, 16:57:50 2022-05-20 20:09:19.452 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7380/7393, mem: 8935Mb, iter_time: 0.349s, data_time: 0.034s, total_loss: 9.4, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 5.996e-03, size: 576, ETA: 9 days, 16:57:13 2022-05-20 20:09:22.491 | INFO | yolox.core.trainer:after_iter:273 - epoch: 3/300, iter: 7390/7393, mem: 8935Mb, iter_time: 0.303s, data_time: 0.002s, total_loss: 8.7, loss_cls: 6.9, loss_iou: 0.5, loss_dfl: 1.3, loss_l1: 0.9, lr: 5.999e-03, size: 448, ETA: 9 days, 16:55:52 2022-05-20 20:09:23.332 | INFO | yolox.core.trainer:save_ckpt:364 - Save weights to ./YOLOX_outputs/ppyoloe_s_sigmoid 2022-05-20 20:09:23.566 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch4 2022-05-20 20:09:23.567 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 20:09:23.567 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 20:09:26.033 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 10/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.009s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 6.003e-03, size: 352, ETA: 9 days, 16:52:56 2022-05-20 20:09:28.905 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 20/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.009s, total_loss: 8.6, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.005e-03, size: 384, ETA: 9 days, 16:51:17 2022-05-20 20:09:32.600 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 30/7393, mem: 8935Mb, iter_time: 0.369s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.008e-03, size: 576, ETA: 9 days, 16:51:01 2022-05-20 20:09:38.314 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 40/7393, mem: 8935Mb, iter_time: 0.571s, data_time: 0.001s, total_loss: 11.2, loss_cls: 9.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 6.011e-03, size: 768, ETA: 9 days, 16:54:04 2022-05-20 20:09:41.448 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 50/7393, mem: 8935Mb, iter_time: 0.313s, data_time: 0.002s, total_loss: 11.0, loss_cls: 9.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.014e-03, size: 480, ETA: 9 days, 16:52:52 2022-05-20 20:09:43.797 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 60/7393, mem: 8935Mb, iter_time: 0.234s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 6.016e-03, size: 384, ETA: 9 days, 16:50:22 2022-05-20 20:09:47.226 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 70/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.006s, total_loss: 10.1, loss_cls: 8.6, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.019e-03, size: 512, ETA: 9 days, 16:49:39 2022-05-20 20:09:50.062 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 80/7393, mem: 8935Mb, iter_time: 0.283s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 6.022e-03, size: 448, ETA: 9 days, 16:47:57 2022-05-20 20:09:53.490 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 90/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 6.024e-03, size: 544, ETA: 9 days, 16:47:14 2022-05-20 20:09:58.171 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 100/7393, mem: 8935Mb, iter_time: 0.468s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.027e-03, size: 672, ETA: 9 days, 16:48:35 2022-05-20 20:10:01.199 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 110/7393, mem: 8935Mb, iter_time: 0.302s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.030e-03, size: 480, ETA: 9 days, 16:47:13 2022-05-20 20:10:06.960 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 120/7393, mem: 8935Mb, iter_time: 0.576s, data_time: 0.001s, total_loss: 10.8, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.032e-03, size: 768, ETA: 9 days, 16:50:20 2022-05-20 20:10:11.854 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 130/7393, mem: 8935Mb, iter_time: 0.489s, data_time: 0.001s, total_loss: 10.5, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 6.035e-03, size: 672, ETA: 9 days, 16:52:02 2022-05-20 20:10:16.019 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 140/7393, mem: 8935Mb, iter_time: 0.416s, data_time: 0.001s, total_loss: 11.2, loss_cls: 9.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 6.038e-03, size: 608, ETA: 9 days, 16:52:32 2022-05-20 20:10:19.487 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 150/7393, mem: 8935Mb, iter_time: 0.346s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.041e-03, size: 544, ETA: 9 days, 16:51:53 2022-05-20 20:10:25.167 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 160/7393, mem: 8935Mb, iter_time: 0.568s, data_time: 0.001s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.043e-03, size: 768, ETA: 9 days, 16:54:52 2022-05-20 20:10:30.426 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 170/7393, mem: 8935Mb, iter_time: 0.525s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.046e-03, size: 704, ETA: 9 days, 16:57:09 2022-05-20 20:10:35.946 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 180/7393, mem: 8935Mb, iter_time: 0.552s, data_time: 0.001s, total_loss: 11.7, loss_cls: 10.2, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.8, lr: 6.049e-03, size: 736, ETA: 9 days, 16:59:52 2022-05-20 20:10:40.372 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 190/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 6.051e-03, size: 640, ETA: 9 days, 17:00:47 2022-05-20 20:10:42.607 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 200/7393, mem: 8935Mb, iter_time: 0.223s, data_time: 0.004s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.054e-03, size: 352, ETA: 9 days, 16:58:06 2022-05-20 20:10:48.034 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 210/7393, mem: 8935Mb, iter_time: 0.542s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.057e-03, size: 736, ETA: 9 days, 17:00:40 2022-05-20 20:10:50.714 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 220/7393, mem: 8935Mb, iter_time: 0.267s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.060e-03, size: 416, ETA: 9 days, 16:58:43 2022-05-20 20:10:54.681 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 230/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.062e-03, size: 608, ETA: 9 days, 16:58:53 2022-05-20 20:10:58.991 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 240/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.001s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.065e-03, size: 640, ETA: 9 days, 16:59:37 2022-05-20 20:11:03.772 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 250/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 6.068e-03, size: 672, ETA: 9 days, 17:01:06 2022-05-20 20:11:08.134 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 260/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 6.070e-03, size: 640, ETA: 9 days, 17:01:55 2022-05-20 20:11:10.698 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 270/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.002s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.073e-03, size: 416, ETA: 9 days, 16:59:48 2022-05-20 20:11:13.791 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 280/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.004s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.076e-03, size: 480, ETA: 9 days, 16:58:32 2022-05-20 20:11:19.533 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 290/7393, mem: 8935Mb, iter_time: 0.574s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.078e-03, size: 768, ETA: 9 days, 17:01:35 2022-05-20 20:11:22.253 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 300/7393, mem: 8935Mb, iter_time: 0.271s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 6.081e-03, size: 416, ETA: 9 days, 16:59:43 2022-05-20 20:11:25.506 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 310/7393, mem: 8935Mb, iter_time: 0.324s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.8, lr: 6.084e-03, size: 512, ETA: 9 days, 16:58:43 2022-05-20 20:11:29.110 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 320/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.7, lr: 6.087e-03, size: 544, ETA: 9 days, 16:58:17 2022-05-20 20:11:34.527 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 330/7393, mem: 8935Mb, iter_time: 0.541s, data_time: 0.001s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.089e-03, size: 736, ETA: 9 days, 17:00:48 2022-05-20 20:11:37.222 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 340/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.092e-03, size: 384, ETA: 9 days, 16:58:54 2022-05-20 20:11:42.475 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 350/7393, mem: 8935Mb, iter_time: 0.525s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.3, lr: 6.095e-03, size: 704, ETA: 9 days, 17:01:09 2022-05-20 20:11:45.735 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 360/7393, mem: 8935Mb, iter_time: 0.325s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 0.9, lr: 6.097e-03, size: 512, ETA: 9 days, 17:00:10 2022-05-20 20:11:51.471 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 370/7393, mem: 8935Mb, iter_time: 0.573s, data_time: 0.001s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.100e-03, size: 768, ETA: 9 days, 17:03:12 2022-05-20 20:11:54.614 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 380/7393, mem: 8935Mb, iter_time: 0.312s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.103e-03, size: 480, ETA: 9 days, 17:02:00 2022-05-20 20:12:00.068 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 390/7393, mem: 8935Mb, iter_time: 0.545s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.106e-03, size: 736, ETA: 9 days, 17:04:35 2022-05-20 20:12:05.320 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 400/7393, mem: 8935Mb, iter_time: 0.525s, data_time: 0.001s, total_loss: 10.8, loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.108e-03, size: 704, ETA: 9 days, 17:06:50 2022-05-20 20:12:07.738 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 410/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.4, loss_iou: 0.5, loss_dfl: 1.6, loss_l1: 1.8, lr: 6.111e-03, size: 384, ETA: 9 days, 17:04:29 2022-05-20 20:12:11.368 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 420/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.004s, total_loss: 11.1, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 6.114e-03, size: 512, ETA: 9 days, 17:04:02 2022-05-20 20:12:13.475 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 430/7393, mem: 8935Mb, iter_time: 0.210s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 6.116e-03, size: 320, ETA: 9 days, 17:01:10 2022-05-20 20:12:18.557 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 440/7393, mem: 8935Mb, iter_time: 0.507s, data_time: 0.005s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.7, lr: 6.119e-03, size: 704, ETA: 9 days, 17:03:08 2022-05-20 20:12:25.600 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 450/7393, mem: 8935Mb, iter_time: 0.704s, data_time: 0.001s, total_loss: 11.2, loss_cls: 9.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.122e-03, size: 768, ETA: 9 days, 17:08:17 2022-05-20 20:12:29.509 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 460/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.8, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.5, lr: 6.124e-03, size: 576, ETA: 9 days, 17:08:20 2022-05-20 20:12:34.187 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 470/7393, mem: 8935Mb, iter_time: 0.467s, data_time: 0.001s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.127e-03, size: 672, ETA: 9 days, 17:09:39 2022-05-20 20:12:36.623 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 480/7393, mem: 8935Mb, iter_time: 0.243s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.130e-03, size: 384, ETA: 9 days, 17:07:20 2022-05-20 20:12:39.815 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 490/7393, mem: 8935Mb, iter_time: 0.318s, data_time: 0.004s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 6.133e-03, size: 448, ETA: 9 days, 17:06:14 2022-05-20 20:12:42.138 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 500/7393, mem: 8935Mb, iter_time: 0.231s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 6.135e-03, size: 352, ETA: 9 days, 17:03:44 2022-05-20 20:12:44.901 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 510/7393, mem: 8935Mb, iter_time: 0.275s, data_time: 0.008s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.6, lr: 6.138e-03, size: 384, ETA: 9 days, 17:01:57 2022-05-20 20:12:47.336 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 520/7393, mem: 8935Mb, iter_time: 0.242s, data_time: 0.007s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.141e-03, size: 320, ETA: 9 days, 16:59:37 2022-05-20 20:12:51.594 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 530/7393, mem: 8935Mb, iter_time: 0.425s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 6.143e-03, size: 608, ETA: 9 days, 17:00:15 2022-05-20 20:12:53.807 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 540/7393, mem: 8935Mb, iter_time: 0.220s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.146e-03, size: 352, ETA: 9 days, 16:57:35 2022-05-20 20:12:57.869 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 550/7393, mem: 8935Mb, iter_time: 0.405s, data_time: 0.003s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 6.149e-03, size: 608, ETA: 9 days, 16:57:53 2022-05-20 20:13:02.047 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 560/7393, mem: 8935Mb, iter_time: 0.417s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.151e-03, size: 608, ETA: 9 days, 16:58:23 2022-05-20 20:13:04.574 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 570/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 6.154e-03, size: 384, ETA: 9 days, 16:56:14 2022-05-20 20:13:08.645 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 580/7393, mem: 8935Mb, iter_time: 0.406s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 6.157e-03, size: 576, ETA: 9 days, 16:56:33 2022-05-20 20:13:12.276 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 590/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.160e-03, size: 544, ETA: 9 days, 16:56:11 2022-05-20 20:13:15.238 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 600/7393, mem: 8935Mb, iter_time: 0.296s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.7, lr: 6.162e-03, size: 480, ETA: 9 days, 16:54:44 2022-05-20 20:13:18.976 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 610/7393, mem: 8935Mb, iter_time: 0.373s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.165e-03, size: 576, ETA: 9 days, 16:54:31 2022-05-20 20:13:24.158 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 620/7393, mem: 8935Mb, iter_time: 0.518s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.168e-03, size: 704, ETA: 9 days, 16:56:38 2022-05-20 20:13:28.601 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 630/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 11.1, loss_cls: 9.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.5, lr: 6.170e-03, size: 640, ETA: 9 days, 16:57:34 2022-05-20 20:13:34.394 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 640/7393, mem: 8935Mb, iter_time: 0.579s, data_time: 0.001s, total_loss: 8.4, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.173e-03, size: 768, ETA: 9 days, 17:00:39 2022-05-20 20:13:37.797 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 650/7393, mem: 8935Mb, iter_time: 0.340s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.176e-03, size: 512, ETA: 9 days, 16:59:55 2022-05-20 20:13:40.581 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 660/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.6, lr: 6.179e-03, size: 416, ETA: 9 days, 16:58:10 2022-05-20 20:13:43.182 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 670/7393, mem: 8935Mb, iter_time: 0.259s, data_time: 0.008s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.181e-03, size: 352, ETA: 9 days, 16:56:08 2022-05-20 20:13:47.050 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 680/7393, mem: 8935Mb, iter_time: 0.386s, data_time: 0.004s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.184e-03, size: 576, ETA: 9 days, 16:56:08 2022-05-20 20:13:52.195 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 690/7393, mem: 8935Mb, iter_time: 0.514s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 0.8, lr: 6.187e-03, size: 704, ETA: 9 days, 16:58:11 2022-05-20 20:13:54.534 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 700/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.004s, total_loss: 8.7, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.189e-03, size: 320, ETA: 9 days, 16:55:44 2022-05-20 20:13:59.320 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 710/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 6.192e-03, size: 672, ETA: 9 days, 16:57:12 2022-05-20 20:14:01.470 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 720/7393, mem: 8935Mb, iter_time: 0.213s, data_time: 0.002s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.195e-03, size: 320, ETA: 9 days, 16:54:25 2022-05-20 20:14:04.380 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 730/7393, mem: 8935Mb, iter_time: 0.289s, data_time: 0.009s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.197e-03, size: 416, ETA: 9 days, 16:52:52 2022-05-20 20:14:08.927 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 740/7393, mem: 8935Mb, iter_time: 0.454s, data_time: 0.005s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 6.200e-03, size: 640, ETA: 9 days, 16:53:57 2022-05-20 20:14:12.703 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 750/7393, mem: 8935Mb, iter_time: 0.377s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.6, lr: 6.203e-03, size: 576, ETA: 9 days, 16:53:49 2022-05-20 20:14:15.697 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 760/7393, mem: 8935Mb, iter_time: 0.299s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 6.206e-03, size: 480, ETA: 9 days, 16:52:25 2022-05-20 20:14:18.130 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 770/7393, mem: 8935Mb, iter_time: 0.242s, data_time: 0.004s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.208e-03, size: 384, ETA: 9 days, 16:50:08 2022-05-20 20:14:21.264 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 780/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.004s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 6.211e-03, size: 480, ETA: 9 days, 16:48:56 2022-05-20 20:14:26.977 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 790/7393, mem: 8935Mb, iter_time: 0.571s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 6.214e-03, size: 768, ETA: 9 days, 16:51:52 2022-05-20 20:14:30.632 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 800/7393, mem: 8935Mb, iter_time: 0.365s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.216e-03, size: 544, ETA: 9 days, 16:51:32 2022-05-20 20:14:32.956 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 810/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.004s, total_loss: 9.8, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.219e-03, size: 320, ETA: 9 days, 16:49:05 2022-05-20 20:14:36.135 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 820/7393, mem: 8935Mb, iter_time: 0.315s, data_time: 0.005s, total_loss: 8.6, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.222e-03, size: 448, ETA: 9 days, 16:47:57 2022-05-20 20:14:40.140 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 830/7393, mem: 8935Mb, iter_time: 0.400s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.225e-03, size: 608, ETA: 9 days, 16:48:10 2022-05-20 20:14:42.522 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 840/7393, mem: 8935Mb, iter_time: 0.237s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 6.227e-03, size: 384, ETA: 9 days, 16:45:49 2022-05-20 20:14:45.537 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 850/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.005s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 6.230e-03, size: 416, ETA: 9 days, 16:44:27 2022-05-20 20:14:48.226 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 860/7393, mem: 8935Mb, iter_time: 0.268s, data_time: 0.005s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.233e-03, size: 384, ETA: 9 days, 16:42:35 2022-05-20 20:14:52.273 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 870/7393, mem: 8935Mb, iter_time: 0.404s, data_time: 0.004s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.235e-03, size: 608, ETA: 9 days, 16:42:52 2022-05-20 20:14:54.620 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 880/7393, mem: 8935Mb, iter_time: 0.234s, data_time: 0.003s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 6.238e-03, size: 384, ETA: 9 days, 16:40:27 2022-05-20 20:14:57.594 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 890/7393, mem: 8935Mb, iter_time: 0.296s, data_time: 0.008s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.241e-03, size: 448, ETA: 9 days, 16:39:02 2022-05-20 20:15:03.037 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 900/7393, mem: 8935Mb, iter_time: 0.543s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 6.243e-03, size: 736, ETA: 9 days, 16:41:32 2022-05-20 20:15:05.318 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 910/7393, mem: 8935Mb, iter_time: 0.227s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.246e-03, size: 320, ETA: 9 days, 16:39:02 2022-05-20 20:15:10.073 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 920/7393, mem: 8935Mb, iter_time: 0.475s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.249e-03, size: 672, ETA: 9 days, 16:40:26 2022-05-20 20:15:13.181 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 930/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.252e-03, size: 480, ETA: 9 days, 16:39:15 2022-05-20 20:15:15.461 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 940/7393, mem: 8935Mb, iter_time: 0.227s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 6.254e-03, size: 352, ETA: 9 days, 16:36:44 2022-05-20 20:15:19.913 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 950/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.007s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.9, lr: 6.257e-03, size: 640, ETA: 9 days, 16:37:39 2022-05-20 20:15:22.346 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 960/7393, mem: 8935Mb, iter_time: 0.243s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 6.260e-03, size: 384, ETA: 9 days, 16:35:24 2022-05-20 20:15:25.091 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 970/7393, mem: 8935Mb, iter_time: 0.274s, data_time: 0.003s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 6.262e-03, size: 416, ETA: 9 days, 16:33:38 2022-05-20 20:15:29.805 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 980/7393, mem: 8935Mb, iter_time: 0.471s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 6.265e-03, size: 672, ETA: 9 days, 16:34:58 2022-05-20 20:15:32.441 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 990/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.268e-03, size: 416, ETA: 9 days, 16:33:01 2022-05-20 20:15:38.100 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1000/7393, mem: 8935Mb, iter_time: 0.565s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 6.271e-03, size: 768, ETA: 9 days, 16:35:51 2022-05-20 20:15:42.334 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1010/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.001s, total_loss: 9.3, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 6.273e-03, size: 608, ETA: 9 days, 16:36:27 2022-05-20 20:15:47.844 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1020/7393, mem: 8935Mb, iter_time: 0.550s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 2.0, lr: 6.276e-03, size: 736, ETA: 9 days, 16:39:03 2022-05-20 20:15:52.324 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1030/7393, mem: 8935Mb, iter_time: 0.448s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.279e-03, size: 640, ETA: 9 days, 16:40:01 2022-05-20 20:15:57.437 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1040/7393, mem: 8935Mb, iter_time: 0.511s, data_time: 0.001s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.281e-03, size: 704, ETA: 9 days, 16:41:59 2022-05-20 20:16:02.648 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1050/7393, mem: 8935Mb, iter_time: 0.521s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 6.284e-03, size: 704, ETA: 9 days, 16:44:07 2022-05-20 20:16:04.913 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1060/7393, mem: 8935Mb, iter_time: 0.226s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.287e-03, size: 320, ETA: 9 days, 16:41:36 2022-05-20 20:16:08.721 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1070/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.005s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.289e-03, size: 576, ETA: 9 days, 16:41:30 2022-05-20 20:16:11.703 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1080/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.292e-03, size: 480, ETA: 9 days, 16:40:07 2022-05-20 20:16:15.883 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1090/7393, mem: 8935Mb, iter_time: 0.417s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.265s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 6.306e-03, size: 416, ETA: 9 days, 16:42:25 2022-05-20 20:16:35.113 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1140/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.005s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.308e-03, size: 416, ETA: 9 days, 16:40:28 2022-05-20 20:16:37.935 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1150/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.006s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.311e-03, size: 448, ETA: 9 days, 16:38:50 2022-05-20 20:16:40.012 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1160/7393, mem: 8935Mb, iter_time: 0.207s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.314e-03, size: 320, ETA: 9 days, 16:36:02 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loss_cls: 9.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 6.325e-03, size: 640, ETA: 9 days, 16:39:40 2022-05-20 20:17:00.359 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1210/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 6.327e-03, size: 416, ETA: 9 days, 16:37:38 2022-05-20 20:17:03.529 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1220/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.330e-03, size: 512, ETA: 9 days, 16:36:33 2022-05-20 20:17:06.332 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1230/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.004s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 6.333e-03, size: 416, ETA: 9 days, 16:34:53 2022-05-20 20:17:08.913 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1240/7393, mem: 8935Mb, iter_time: 0.255s, data_time: 0.005s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 6.335e-03, size: 320, ETA: 9 days, 16:32:50 2022-05-20 20:17:14.119 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1250/7393, mem: 8935Mb, iter_time: 0.520s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 6.338e-03, size: 704, ETA: 9 days, 16:34:56 2022-05-20 20:17:17.979 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1260/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.341e-03, size: 576, ETA: 9 days, 16:34:56 2022-05-20 20:17:23.369 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1270/7393, mem: 8935Mb, iter_time: 0.539s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.344e-03, size: 736, ETA: 9 days, 16:37:19 2022-05-20 20:17:26.767 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1280/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.346e-03, size: 512, ETA: 9 days, 16:36:35 2022-05-20 20:17:29.184 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1290/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.349e-03, size: 384, ETA: 9 days, 16:34:20 2022-05-20 20:17:31.982 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1300/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.352e-03, size: 416, ETA: 9 days, 16:32:40 2022-05-20 20:17:36.125 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1310/7393, mem: 8935Mb, iter_time: 0.414s, data_time: 0.004s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 6.354e-03, size: 608, ETA: 9 days, 16:33:06 2022-05-20 20:17:38.852 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1320/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.003s, total_loss: 8.1, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 6.357e-03, size: 416, ETA: 9 days, 16:31:20 2022-05-20 20:17:42.241 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1330/7393, mem: 8935Mb, iter_time: 0.338s, data_time: 0.004s, total_loss: 8.4, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.360e-03, size: 480, ETA: 9 days, 16:30:35 2022-05-20 20:17:47.746 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1340/7393, mem: 8935Mb, iter_time: 0.550s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 6.363e-03, size: 736, ETA: 9 days, 16:33:08 2022-05-20 20:17:51.939 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1350/7393, mem: 8935Mb, iter_time: 0.419s, data_time: 0.002s, total_loss: 8.7, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.365e-03, size: 608, ETA: 9 days, 16:33:39 2022-05-20 20:17:54.375 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1360/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.368e-03, size: 384, ETA: 9 days, 16:31:25 2022-05-20 20:17:59.235 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1370/7393, mem: 8935Mb, iter_time: 0.485s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.371e-03, size: 672, ETA: 9 days, 16:32:58 2022-05-20 20:18:02.480 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1380/7393, mem: 8935Mb, iter_time: 0.324s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.3, loss_iou: 0.5, loss_dfl: 1.5, loss_l1: 2.1, lr: 6.373e-03, size: 512, ETA: 9 days, 16:32:00 2022-05-20 20:18:04.619 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1390/7393, mem: 8935Mb, iter_time: 0.211s, data_time: 0.003s, total_loss: 8.8, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 6.376e-03, size: 320, ETA: 9 days, 16:29:17 2022-05-20 20:18:08.521 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1400/7393, mem: 8935Mb, iter_time: 0.388s, data_time: 0.009s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.379e-03, size: 576, ETA: 9 days, 16:29:20 2022-05-20 20:18:12.944 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1410/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.381e-03, size: 640, ETA: 9 days, 16:30:12 2022-05-20 20:18:15.018 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1420/7393, mem: 8935Mb, iter_time: 0.207s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.384e-03, size: 320, ETA: 9 days, 16:27:26 2022-05-20 20:18:18.417 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1430/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.004s, total_loss: 10.3, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.5, lr: 6.387e-03, size: 512, ETA: 9 days, 16:26:42 2022-05-20 20:18:22.248 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1440/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.390e-03, size: 576, ETA: 9 days, 16:26:39 2022-05-20 20:18:24.759 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1450/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.003s, total_loss: 8.3, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.392e-03, size: 384, ETA: 9 days, 16:24:34 2022-05-20 20:18:28.277 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1460/7393, mem: 8935Mb, iter_time: 0.351s, data_time: 0.004s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.395e-03, size: 512, ETA: 9 days, 16:24:01 2022-05-20 20:18:33.978 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1470/7393, mem: 8935Mb, iter_time: 0.570s, data_time: 0.001s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.398e-03, size: 768, ETA: 9 days, 16:26:52 2022-05-20 20:18:39.233 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1480/7393, mem: 8935Mb, iter_time: 0.525s, data_time: 0.001s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.400e-03, size: 704, ETA: 9 days, 16:29:02 2022-05-20 20:18:42.213 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1490/7393, mem: 8935Mb, iter_time: 0.297s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.403e-03, size: 448, ETA: 9 days, 16:27:40 2022-05-20 20:18:45.585 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1500/7393, mem: 8935Mb, iter_time: 0.336s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.3, lr: 6.406e-03, size: 512, ETA: 9 days, 16:26:55 2022-05-20 20:18:48.707 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1510/7393, mem: 8935Mb, iter_time: 0.311s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.408e-03, size: 512, ETA: 9 days, 16:25:46 2022-05-20 20:18:54.352 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1520/7393, mem: 8935Mb, iter_time: 0.564s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 6.411e-03, size: 768, ETA: 9 days, 16:28:31 2022-05-20 20:18:57.053 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1530/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.3, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.4, lr: 6.414e-03, size: 416, ETA: 9 days, 16:26:44 2022-05-20 20:18:59.519 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1540/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.007s, total_loss: 10.6, loss_cls: 9.2, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 1.5, lr: 6.417e-03, size: 320, ETA: 9 days, 16:24:34 2022-05-20 20:19:04.447 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1550/7393, mem: 8935Mb, iter_time: 0.492s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 6.419e-03, size: 672, ETA: 9 days, 16:26:13 2022-05-20 20:19:10.261 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1560/7393, mem: 8935Mb, iter_time: 0.581s, data_time: 0.001s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 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loss_cls: 8.0, loss_iou: 0.5, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.452e-03, size: 384, ETA: 9 days, 16:31:16 2022-05-20 20:19:59.828 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1680/7393, mem: 8935Mb, iter_time: 0.574s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.454e-03, size: 768, ETA: 9 days, 16:34:09 2022-05-20 20:20:02.032 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1690/7393, mem: 8935Mb, iter_time: 0.220s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.457e-03, size: 320, ETA: 9 days, 16:31:36 2022-05-20 20:20:05.253 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1700/7393, mem: 8935Mb, iter_time: 0.321s, data_time: 0.007s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.460e-03, size: 480, ETA: 9 days, 16:30:37 2022-05-20 20:20:10.028 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1710/7393, mem: 8935Mb, iter_time: 0.477s, data_time: 0.003s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 6.463e-03, size: 672, ETA: 9 days, 16:32:00 2022-05-20 20:20:14.507 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1720/7393, mem: 8935Mb, iter_time: 0.447s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 6.465e-03, size: 640, ETA: 9 days, 16:32:57 2022-05-20 20:20:17.074 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1730/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.9, lr: 6.468e-03, size: 416, ETA: 9 days, 16:30:58 2022-05-20 20:20:22.392 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1740/7393, mem: 8935Mb, iter_time: 0.531s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.2, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.471e-03, 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data_time: 0.003s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.482e-03, size: 576, ETA: 9 days, 16:30:09 2022-05-20 20:20:38.081 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1790/7393, mem: 8935Mb, iter_time: 0.221s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.484e-03, size: 352, ETA: 9 days, 16:27:38 2022-05-20 20:20:43.510 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1800/7393, mem: 8935Mb, iter_time: 0.542s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 6.487e-03, size: 736, ETA: 9 days, 16:30:01 2022-05-20 20:20:48.760 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1810/7393, mem: 8935Mb, iter_time: 0.524s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 6.490e-03, size: 704, ETA: 9 days, 16:32:08 2022-05-20 20:20:51.599 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1820/7393, mem: 8935Mb, iter_time: 0.283s, data_time: 0.004s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.492e-03, size: 448, ETA: 9 days, 16:30:35 2022-05-20 20:20:54.279 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1830/7393, mem: 8935Mb, iter_time: 0.267s, data_time: 0.005s, total_loss: 8.5, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.495e-03, size: 416, ETA: 9 days, 16:28:46 2022-05-20 20:20:56.949 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1840/7393, mem: 8935Mb, iter_time: 0.266s, data_time: 0.004s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.498e-03, size: 416, ETA: 9 days, 16:26:57 2022-05-20 20:21:02.353 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1850/7393, mem: 8935Mb, iter_time: 0.540s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.4, loss_iou: 0.4, 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1890/7393, mem: 8935Mb, iter_time: 0.289s, data_time: 0.010s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.511e-03, size: 384, ETA: 9 days, 16:29:55 2022-05-20 20:21:20.747 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1900/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.2, lr: 6.514e-03, size: 320, ETA: 9 days, 16:27:52 2022-05-20 20:21:23.671 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1910/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.011s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.517e-03, size: 416, ETA: 9 days, 16:26:26 2022-05-20 20:21:26.753 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1920/7393, mem: 8935Mb, iter_time: 0.307s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.519e-03, size: 512, ETA: 9 days, 16:25:15 2022-05-20 20:21:30.896 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1930/7393, mem: 8935Mb, iter_time: 0.414s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 6.522e-03, size: 608, ETA: 9 days, 16:25:40 2022-05-20 20:21:36.487 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1940/7393, mem: 8935Mb, iter_time: 0.559s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.525e-03, size: 736, ETA: 9 days, 16:28:18 2022-05-20 20:21:41.726 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1950/7393, mem: 8935Mb, iter_time: 0.523s, data_time: 0.001s, total_loss: 8.1, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.528e-03, size: 704, ETA: 9 days, 16:30:23 2022-05-20 20:21:45.287 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1960/7393, mem: 8935Mb, iter_time: 0.356s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 6.530e-03, size: 544, ETA: 9 days, 16:29:55 2022-05-20 20:21:47.475 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1970/7393, mem: 8935Mb, iter_time: 0.218s, data_time: 0.003s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.533e-03, size: 352, ETA: 9 days, 16:27:23 2022-05-20 20:21:50.313 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1980/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.007s, total_loss: 8.4, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.536e-03, size: 320, ETA: 9 days, 16:25:48 2022-05-20 20:21:56.205 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 1990/7393, mem: 8935Mb, iter_time: 0.589s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 6.538e-03, size: 768, ETA: 9 days, 16:28:52 2022-05-20 20:21:58.543 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2000/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 6.541e-03, size: 352, ETA: 9 days, 16:26:34 2022-05-20 20:22:01.768 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2010/7393, mem: 8935Mb, iter_time: 0.321s, data_time: 0.005s, total_loss: 11.1, loss_cls: 9.4, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 6.544e-03, size: 512, ETA: 9 days, 16:25:35 2022-05-20 20:22:05.275 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2020/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.546e-03, size: 544, ETA: 9 days, 16:25:03 2022-05-20 20:22:11.071 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2030/7393, mem: 8935Mb, iter_time: 0.579s, data_time: 0.001s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, 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days, 16:25:26 2022-05-20 20:22:38.606 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2110/7393, mem: 8935Mb, iter_time: 0.216s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.571e-03, size: 320, ETA: 9 days, 16:22:53 2022-05-20 20:22:41.276 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2120/7393, mem: 8935Mb, iter_time: 0.266s, data_time: 0.006s, total_loss: 8.7, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 6.574e-03, size: 320, ETA: 9 days, 16:21:05 2022-05-20 20:22:44.133 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2130/7393, mem: 8935Mb, iter_time: 0.284s, data_time: 0.009s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 6.576e-03, size: 416, ETA: 9 days, 16:19:34 2022-05-20 20:22:46.583 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2140/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.009s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.579e-03, size: 320, ETA: 9 days, 16:17:26 2022-05-20 20:22:49.655 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2150/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.005s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.582e-03, size: 480, ETA: 9 days, 16:16:14 2022-05-20 20:22:53.462 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2160/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.584e-03, size: 576, ETA: 9 days, 16:16:09 2022-05-20 20:22:56.923 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2170/7393, mem: 8935Mb, iter_time: 0.345s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.6, lr: 6.587e-03, size: 544, ETA: 9 days, 16:15:33 2022-05-20 20:22:59.112 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2180/7393, mem: 8935Mb, iter_time: 0.218s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.590e-03, size: 352, ETA: 9 days, 16:13:03 2022-05-20 20:23:02.561 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2190/7393, mem: 8935Mb, iter_time: 0.344s, data_time: 0.006s, total_loss: 10.6, loss_cls: 9.0, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 1.4, lr: 6.592e-03, size: 512, ETA: 9 days, 16:12:25 2022-05-20 20:23:08.021 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2200/7393, mem: 8935Mb, iter_time: 0.545s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.2, lr: 6.595e-03, size: 736, ETA: 9 days, 16:14:49 2022-05-20 20:23:13.028 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2210/7393, mem: 8935Mb, iter_time: 0.500s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.277s, data_time: 0.003s, total_loss: 8.9, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.609e-03, size: 448, ETA: 9 days, 16:14:34 2022-05-20 20:23:31.248 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2260/7393, mem: 8935Mb, iter_time: 0.406s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.611e-03, size: 608, ETA: 9 days, 16:14:53 2022-05-20 20:23:33.618 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2270/7393, mem: 8935Mb, iter_time: 0.236s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 6.614e-03, size: 384, ETA: 9 days, 16:12:39 2022-05-20 20:23:36.635 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2280/7393, mem: 8935Mb, iter_time: 0.301s, data_time: 0.005s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 6.617e-03, size: 480, ETA: 9 days, 16:11:23 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loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.628e-03, size: 704, ETA: 9 days, 16:10:28 2022-05-20 20:23:54.775 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2330/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.630e-03, size: 512, ETA: 9 days, 16:09:37 2022-05-20 20:23:59.497 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2340/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 6.633e-03, size: 672, ETA: 9 days, 16:10:55 2022-05-20 20:24:05.416 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2350/7393, mem: 8935Mb, iter_time: 0.591s, data_time: 0.001s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.636e-03, size: 768, ETA: 9 days, 16:13:59 2022-05-20 20:24:08.766 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2360/7393, mem: 8935Mb, iter_time: 0.334s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 6.638e-03, size: 512, ETA: 9 days, 16:13:13 2022-05-20 20:24:11.777 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2370/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.641e-03, size: 480, ETA: 9 days, 16:11:57 2022-05-20 20:24:14.704 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2380/7393, mem: 8935Mb, iter_time: 0.292s, data_time: 0.005s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.644e-03, size: 416, ETA: 9 days, 16:10:34 2022-05-20 20:24:17.449 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2390/7393, mem: 8935Mb, iter_time: 0.273s, data_time: 0.008s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.1, lr: 6.647e-03, size: 384, ETA: 9 days, 16:08:54 2022-05-20 20:24:19.909 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2400/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.005s, total_loss: 8.2, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.649e-03, size: 352, ETA: 9 days, 16:06:48 2022-05-20 20:24:23.303 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2410/7393, mem: 8935Mb, iter_time: 0.337s, data_time: 0.004s, total_loss: 9.6, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.652e-03, size: 512, ETA: 9 days, 16:06:05 2022-05-20 20:24:26.349 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2420/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.655e-03, size: 480, ETA: 9 days, 16:04:53 2022-05-20 20:24:30.710 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2430/7393, mem: 8935Mb, iter_time: 0.435s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 6.657e-03, size: 640, ETA: 9 days, 16:05:38 2022-05-20 20:24:32.991 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2440/7393, mem: 8935Mb, iter_time: 0.227s, data_time: 0.006s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.660e-03, size: 352, ETA: 9 days, 16:03:17 2022-05-20 20:24:35.860 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2450/7393, mem: 8935Mb, iter_time: 0.286s, data_time: 0.005s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.663e-03, size: 416, ETA: 9 days, 16:01:48 2022-05-20 20:24:38.310 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2460/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.005s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.665e-03, size: 384, ETA: 9 days, 15:59:43 2022-05-20 20:24:42.133 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2470/7393, mem: 8935Mb, iter_time: 0.381s, data_time: 0.005s, total_loss: 10.0, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.668e-03, size: 576, ETA: 9 days, 15:59:39 2022-05-20 20:24:44.616 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2480/7393, mem: 8935Mb, iter_time: 0.247s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.4, lr: 6.671e-03, size: 352, ETA: 9 days, 15:57:37 2022-05-20 20:24:48.243 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2490/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.006s, total_loss: 9.0, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.674e-03, size: 544, ETA: 9 days, 15:57:16 2022-05-20 20:24:51.023 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2500/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.002s, total_loss: 8.5, loss_cls: 6.9, loss_iou: 0.4, 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days, 15:52:18 2022-05-20 20:25:20.715 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2580/7393, mem: 8935Mb, iter_time: 0.494s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.4, lr: 6.698e-03, size: 704, ETA: 9 days, 15:53:55 2022-05-20 20:25:24.473 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2590/7393, mem: 8935Mb, iter_time: 0.375s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.701e-03, size: 544, ETA: 9 days, 15:53:47 2022-05-20 20:25:29.182 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2600/7393, mem: 8935Mb, iter_time: 0.470s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.703e-03, size: 672, ETA: 9 days, 15:55:02 2022-05-20 20:25:33.641 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2610/7393, mem: 8935Mb, iter_time: 0.445s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 6.706e-03, size: 640, ETA: 9 days, 15:55:56 2022-05-20 20:25:36.732 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2620/7393, mem: 8935Mb, iter_time: 0.308s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 6.709e-03, size: 480, ETA: 9 days, 15:54:48 2022-05-20 20:25:40.159 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2630/7393, mem: 8935Mb, iter_time: 0.342s, data_time: 0.003s, total_loss: 10.2, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.711e-03, size: 544, ETA: 9 days, 15:54:10 2022-05-20 20:25:43.311 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2640/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.003s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.714e-03, size: 512, ETA: 9 days, 15:53:07 2022-05-20 20:25:45.453 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2650/7393, mem: 8935Mb, iter_time: 0.213s, data_time: 0.004s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.717e-03, size: 320, ETA: 9 days, 15:50:36 2022-05-20 20:25:48.720 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2660/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.005s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.9, lr: 6.720e-03, size: 448, ETA: 9 days, 15:49:43 2022-05-20 20:25:51.437 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2670/7393, mem: 8935Mb, iter_time: 0.270s, data_time: 0.007s, total_loss: 8.2, loss_cls: 6.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 6.722e-03, size: 416, ETA: 9 days, 15:48:03 2022-05-20 20:25:54.642 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2680/7393, mem: 8935Mb, iter_time: 0.319s, data_time: 0.004s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 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loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.755e-03, size: 640, ETA: 9 days, 15:45:00 2022-05-20 20:26:41.507 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2800/7393, mem: 8935Mb, iter_time: 0.587s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 6.757e-03, size: 768, ETA: 9 days, 15:47:58 2022-05-20 20:26:43.989 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2810/7393, mem: 8935Mb, iter_time: 0.248s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 6.760e-03, size: 384, ETA: 9 days, 15:45:57 2022-05-20 20:26:46.763 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2820/7393, mem: 8935Mb, iter_time: 0.277s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.9, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 6.763e-03, size: 448, ETA: 9 days, 15:44:22 2022-05-20 20:26:50.103 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2830/7393, mem: 8935Mb, iter_time: 0.333s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.0, lr: 6.766e-03, size: 512, ETA: 9 days, 15:43:37 2022-05-20 20:26:54.332 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2840/7393, mem: 8935Mb, iter_time: 0.422s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 6.768e-03, size: 608, ETA: 9 days, 15:44:10 2022-05-20 20:26:59.790 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2850/7393, mem: 8935Mb, iter_time: 0.545s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 6.771e-03, size: 736, ETA: 9 days, 15:46:30 2022-05-20 20:27:02.923 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2860/7393, mem: 8935Mb, iter_time: 0.313s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 2.1, lr: 6.774e-03, size: 480, ETA: 9 days, 15:45:27 2022-05-20 20:27:06.476 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2870/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 6.776e-03, size: 544, ETA: 9 days, 15:45:00 2022-05-20 20:27:09.195 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2880/7393, mem: 8935Mb, iter_time: 0.271s, data_time: 0.003s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.779e-03, size: 416, ETA: 9 days, 15:43:21 2022-05-20 20:27:11.879 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2890/7393, mem: 8935Mb, iter_time: 0.264s, data_time: 0.010s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.782e-03, size: 352, ETA: 9 days, 15:41:35 2022-05-20 20:27:14.407 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2900/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.013s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.785e-03, size: 352, ETA: 9 days, 15:39:37 2022-05-20 20:27:17.245 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2910/7393, mem: 8935Mb, iter_time: 0.283s, data_time: 0.007s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.787e-03, size: 416, ETA: 9 days, 15:38:08 2022-05-20 20:27:19.667 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2920/7393, mem: 8935Mb, iter_time: 0.241s, data_time: 0.009s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 6.790e-03, size: 320, ETA: 9 days, 15:36:02 2022-05-20 20:27:22.936 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2930/7393, mem: 8935Mb, iter_time: 0.326s, data_time: 0.011s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.793e-03, size: 448, ETA: 9 days, 15:35:11 2022-05-20 20:27:25.579 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2940/7393, mem: 8935Mb, iter_time: 0.263s, data_time: 0.008s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.795e-03, size: 352, ETA: 9 days, 15:33:25 2022-05-20 20:27:28.982 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2950/7393, mem: 8935Mb, iter_time: 0.339s, data_time: 0.004s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 6.798e-03, size: 512, ETA: 9 days, 15:32:46 2022-05-20 20:27:31.251 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2960/7393, mem: 8935Mb, iter_time: 0.226s, data_time: 0.005s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.801e-03, size: 320, ETA: 9 days, 15:30:27 2022-05-20 20:27:34.711 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 2970/7393, mem: 8935Mb, iter_time: 0.344s, data_time: 0.007s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.273s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 6.814e-03, size: 416, ETA: 9 days, 15:33:22 2022-05-20 20:27:55.636 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3020/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.817e-03, size: 512, ETA: 9 days, 15:32:17 2022-05-20 20:27:58.886 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3030/7393, mem: 8935Mb, iter_time: 0.324s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 6.820e-03, size: 512, ETA: 9 days, 15:31:25 2022-05-20 20:28:04.624 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3040/7393, mem: 8935Mb, iter_time: 0.573s, data_time: 0.001s, total_loss: 9.8, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.7, lr: 6.822e-03, size: 768, ETA: 9 days, 15:34:09 2022-05-20 20:28:09.539 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3050/7393, mem: 8935Mb, iter_time: 0.491s, data_time: 0.001s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 6.825e-03, size: 672, ETA: 9 days, 15:35:41 2022-05-20 20:28:15.103 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3060/7393, mem: 8935Mb, iter_time: 0.556s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 6.828e-03, size: 736, ETA: 9 days, 15:38:10 2022-05-20 20:28:17.873 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3070/7393, mem: 8935Mb, iter_time: 0.276s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.831e-03, size: 416, ETA: 9 days, 15:36:36 2022-05-20 20:28:20.148 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3080/7393, mem: 8935Mb, iter_time: 0.226s, data_time: 0.011s, total_loss: 8.6, 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yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3230/7393, mem: 8935Mb, iter_time: 0.384s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.874e-03, size: 576, ETA: 9 days, 15:24:25 2022-05-20 20:29:15.790 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3240/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.3, lr: 6.877e-03, size: 672, ETA: 9 days, 15:25:38 2022-05-20 20:29:19.344 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3250/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.003s, total_loss: 10.4, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 2.1, lr: 6.879e-03, size: 544, ETA: 9 days, 15:25:12 2022-05-20 20:29:22.140 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3260/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.4, 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days, 15:18:03 2022-05-20 20:29:47.907 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3340/7393, mem: 8935Mb, iter_time: 0.271s, data_time: 0.003s, total_loss: 8.7, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 6.904e-03, size: 448, ETA: 9 days, 15:16:25 2022-05-20 20:29:50.864 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3350/7393, mem: 8935Mb, iter_time: 0.295s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 6.906e-03, size: 480, ETA: 9 days, 15:15:08 2022-05-20 20:29:56.279 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3360/7393, mem: 8935Mb, iter_time: 0.541s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.9, lr: 6.909e-03, size: 736, ETA: 9 days, 15:17:23 2022-05-20 20:29:58.946 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3370/7393, mem: 8935Mb, iter_time: 0.266s, data_time: 0.002s, total_loss: 10.6, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 6.912e-03, size: 416, ETA: 9 days, 15:15:41 2022-05-20 20:30:01.878 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3380/7393, mem: 8935Mb, iter_time: 0.292s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 6.914e-03, size: 480, ETA: 9 days, 15:14:22 2022-05-20 20:30:05.311 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3390/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 0.003s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.917e-03, size: 544, ETA: 9 days, 15:13:47 2022-05-20 20:30:10.860 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3400/7393, mem: 8935Mb, iter_time: 0.554s, data_time: 0.002s, total_loss: 11.1, loss_cls: 9.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.5, lr: 6.920e-03, size: 736, ETA: 9 days, 15:16:13 2022-05-20 20:30:13.434 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3410/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 6.922e-03, size: 384, ETA: 9 days, 15:14:23 2022-05-20 20:30:15.783 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3420/7393, mem: 8935Mb, iter_time: 0.233s, data_time: 0.008s, total_loss: 8.2, loss_cls: 6.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 6.925e-03, size: 352, ETA: 9 days, 15:12:14 2022-05-20 20:30:20.190 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3430/7393, mem: 8935Mb, iter_time: 0.440s, data_time: 0.004s, total_loss: 10.4, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.6, loss_l1: 1.5, lr: 6.928e-03, size: 640, ETA: 9 days, 15:13:02 2022-05-20 20:30:22.981 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3440/7393, mem: 8935Mb, iter_time: 0.278s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 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data_time: 0.008s, total_loss: 8.1, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.990e-03, size: 352, ETA: 9 days, 15:16:12 2022-05-20 20:31:53.776 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3670/7393, mem: 8935Mb, iter_time: 0.280s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 6.993e-03, size: 416, ETA: 9 days, 15:14:44 2022-05-20 20:31:56.248 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3680/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.008s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 6.996e-03, size: 320, ETA: 9 days, 15:12:46 2022-05-20 20:31:58.812 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3690/7393, mem: 8935Mb, iter_time: 0.255s, data_time: 0.006s, total_loss: 8.5, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 6.998e-03, size: 320, ETA: 9 days, 15:10:57 2022-05-20 20:32:04.691 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3700/7393, mem: 8935Mb, iter_time: 0.587s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.001e-03, size: 768, ETA: 9 days, 15:13:49 2022-05-20 20:32:07.562 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3710/7393, mem: 8935Mb, iter_time: 0.285s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.004e-03, size: 448, ETA: 9 days, 15:12:25 2022-05-20 20:32:11.597 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3720/7393, mem: 8935Mb, iter_time: 0.403s, data_time: 0.004s, total_loss: 8.9, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 7.006e-03, size: 608, ETA: 9 days, 15:12:41 2022-05-20 20:32:14.746 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3730/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.002s, total_loss: 10.7, loss_cls: 9.0, loss_iou: 0.4, loss_dfl: 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loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 7.039e-03, size: 480, ETA: 9 days, 15:11:50 2022-05-20 20:33:00.232 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3850/7393, mem: 8935Mb, iter_time: 0.303s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.4, lr: 7.042e-03, size: 512, ETA: 9 days, 15:10:42 2022-05-20 20:33:05.599 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3860/7393, mem: 8935Mb, iter_time: 0.536s, data_time: 0.002s, total_loss: 11.6, loss_cls: 10.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.044e-03, size: 736, ETA: 9 days, 15:12:49 2022-05-20 20:33:08.499 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3870/7393, mem: 8935Mb, iter_time: 0.289s, data_time: 0.003s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.047e-03, size: 448, ETA: 9 days, 15:11:30 2022-05-20 20:33:11.480 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3880/7393, mem: 8935Mb, iter_time: 0.297s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.050e-03, size: 448, ETA: 9 days, 15:10:16 2022-05-20 20:33:14.553 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3890/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.003s, total_loss: 8.5, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.052e-03, size: 480, ETA: 9 days, 15:09:11 2022-05-20 20:33:17.078 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3900/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.055e-03, size: 416, ETA: 9 days, 15:07:20 2022-05-20 20:33:22.740 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3910/7393, mem: 8935Mb, iter_time: 0.566s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 7.058e-03, size: 768, ETA: 9 days, 15:09:52 2022-05-20 20:33:28.754 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3920/7393, mem: 8935Mb, iter_time: 0.601s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.060e-03, size: 768, ETA: 9 days, 15:12:54 2022-05-20 20:33:32.662 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3930/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.063e-03, size: 576, ETA: 9 days, 15:12:59 2022-05-20 20:33:37.008 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3940/7393, mem: 8935Mb, iter_time: 0.434s, data_time: 0.001s, total_loss: 11.2, loss_cls: 9.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.066e-03, size: 640, ETA: 9 days, 15:13:41 2022-05-20 20:33:39.944 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3950/7393, mem: 8935Mb, iter_time: 0.293s, data_time: 0.003s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 2.0, lr: 7.069e-03, size: 448, ETA: 9 days, 15:12:24 2022-05-20 20:33:45.291 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3960/7393, mem: 8935Mb, iter_time: 0.534s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.3, lr: 7.071e-03, size: 736, ETA: 9 days, 15:14:30 2022-05-20 20:33:48.979 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3970/7393, mem: 8935Mb, iter_time: 0.368s, data_time: 0.001s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.074e-03, size: 544, ETA: 9 days, 15:14:16 2022-05-20 20:33:53.423 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3980/7393, mem: 8935Mb, iter_time: 0.444s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.077e-03, size: 640, ETA: 9 days, 15:15:06 2022-05-20 20:33:56.050 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 3990/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.079e-03, size: 416, ETA: 9 days, 15:13:24 2022-05-20 20:33:58.509 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4000/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.005s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.082e-03, size: 352, ETA: 9 days, 15:11:27 2022-05-20 20:34:02.704 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4010/7393, mem: 8935Mb, iter_time: 0.419s, data_time: 0.005s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 7.085e-03, size: 608, ETA: 9 days, 15:11:55 2022-05-20 20:34:06.279 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4020/7393, mem: 8935Mb, iter_time: 0.357s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 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yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4280/7393, mem: 8935Mb, iter_time: 0.349s, data_time: 0.003s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 7.158e-03, size: 544, ETA: 9 days, 15:17:11 2022-05-20 20:35:55.830 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4290/7393, mem: 8935Mb, iter_time: 0.536s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.161e-03, size: 736, ETA: 9 days, 15:19:16 2022-05-20 20:35:59.722 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4300/7393, mem: 8935Mb, iter_time: 0.389s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.163e-03, size: 576, ETA: 9 days, 15:19:19 2022-05-20 20:36:03.524 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4310/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 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loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.5, lr: 7.196e-03, size: 384, ETA: 9 days, 15:18:25 2022-05-20 20:36:49.093 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4430/7393, mem: 8935Mb, iter_time: 0.377s, data_time: 0.006s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.198e-03, size: 576, ETA: 9 days, 15:18:18 2022-05-20 20:36:52.424 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4440/7393, mem: 8935Mb, iter_time: 0.332s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.201e-03, size: 512, ETA: 9 days, 15:17:35 2022-05-20 20:36:55.197 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4450/7393, mem: 8935Mb, iter_time: 0.275s, data_time: 0.004s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.204e-03, size: 416, ETA: 9 days, 15:16:05 2022-05-20 20:36:57.704 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4460/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.005s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.207e-03, size: 352, ETA: 9 days, 15:14:14 2022-05-20 20:37:01.151 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4470/7393, mem: 8935Mb, iter_time: 0.343s, data_time: 0.007s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.209e-03, size: 512, ETA: 9 days, 15:13:40 2022-05-20 20:37:05.882 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4480/7393, mem: 8935Mb, iter_time: 0.473s, data_time: 0.002s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.212e-03, size: 672, ETA: 9 days, 15:14:53 2022-05-20 20:37:10.322 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4490/7393, mem: 8935Mb, iter_time: 0.443s, data_time: 0.001s, total_loss: 8.2, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.215e-03, size: 640, ETA: 9 days, 15:15:41 2022-05-20 20:37:13.053 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4500/7393, mem: 8935Mb, iter_time: 0.273s, data_time: 0.002s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.6, lr: 7.217e-03, size: 448, ETA: 9 days, 15:14:09 2022-05-20 20:37:15.621 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4510/7393, mem: 8935Mb, iter_time: 0.256s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 7.220e-03, size: 416, ETA: 9 days, 15:12:23 2022-05-20 20:37:18.206 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4520/7393, mem: 8935Mb, iter_time: 0.257s, data_time: 0.004s, total_loss: 8.9, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.223e-03, size: 384, ETA: 9 days, 15:10:39 2022-05-20 20:37:21.084 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4530/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 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yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4570/7393, mem: 8935Mb, iter_time: 0.414s, data_time: 0.001s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.236e-03, size: 608, ETA: 9 days, 15:08:53 2022-05-20 20:37:38.650 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4580/7393, mem: 8935Mb, iter_time: 0.246s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.239e-03, size: 352, ETA: 9 days, 15:07:00 2022-05-20 20:37:42.935 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4590/7393, mem: 8935Mb, iter_time: 0.428s, data_time: 0.004s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 7.242e-03, size: 608, ETA: 9 days, 15:07:35 2022-05-20 20:37:46.736 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4600/7393, mem: 8935Mb, iter_time: 0.380s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 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epoch: 4/300, iter: 4750/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.004s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.285e-03, size: 352, ETA: 9 days, 14:58:25 2022-05-20 20:38:40.445 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4760/7393, mem: 8935Mb, iter_time: 0.262s, data_time: 0.005s, total_loss: 8.6, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.288e-03, size: 352, ETA: 9 days, 14:56:46 2022-05-20 20:38:44.776 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4770/7393, mem: 8935Mb, iter_time: 0.431s, data_time: 0.004s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.290e-03, size: 608, ETA: 9 days, 14:57:24 2022-05-20 20:38:49.840 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4780/7393, mem: 8935Mb, iter_time: 0.506s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 7.293e-03, size: 704, ETA: 9 days, 14:59:03 2022-05-20 20:38:53.137 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4790/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 7.296e-03, size: 512, ETA: 9 days, 14:58:18 2022-05-20 20:38:58.904 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4800/7393, mem: 8935Mb, iter_time: 0.576s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 7.299e-03, size: 768, ETA: 9 days, 15:00:54 2022-05-20 20:39:04.537 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4810/7393, mem: 8935Mb, iter_time: 0.563s, data_time: 0.001s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.301e-03, size: 736, ETA: 9 days, 15:03:19 2022-05-20 20:39:07.678 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4820/7393, mem: 8935Mb, iter_time: 0.313s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.304e-03, size: 480, ETA: 9 days, 15:02:22 2022-05-20 20:39:11.253 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4830/7393, mem: 8935Mb, iter_time: 0.356s, data_time: 0.002s, total_loss: 9.7, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.307e-03, size: 544, ETA: 9 days, 15:01:59 2022-05-20 20:39:17.031 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4840/7393, mem: 8935Mb, iter_time: 0.577s, data_time: 0.001s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.309e-03, size: 768, ETA: 9 days, 15:04:35 2022-05-20 20:39:22.681 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4850/7393, mem: 8935Mb, iter_time: 0.564s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.312e-03, size: 736, ETA: 9 days, 15:07:01 2022-05-20 20:39:25.408 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4860/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.002s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.7, lr: 7.315e-03, size: 416, ETA: 9 days, 15:05:30 2022-05-20 20:39:29.239 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4870/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.003s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.317e-03, size: 576, ETA: 9 days, 15:05:29 2022-05-20 20:39:34.664 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4880/7393, mem: 8935Mb, iter_time: 0.542s, data_time: 0.001s, total_loss: 9.2, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.320e-03, size: 736, ETA: 9 days, 15:07:36 2022-05-20 20:39:36.967 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4890/7393, mem: 8935Mb, iter_time: 0.230s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.358s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.334e-03, size: 544, ETA: 9 days, 15:04:13 2022-05-20 20:39:53.868 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4940/7393, mem: 8935Mb, iter_time: 0.244s, data_time: 0.005s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.336e-03, size: 384, ETA: 9 days, 15:02:20 2022-05-20 20:39:57.774 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4950/7393, mem: 8935Mb, iter_time: 0.390s, data_time: 0.008s, total_loss: 10.7, loss_cls: 9.3, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.339e-03, size: 576, ETA: 9 days, 15:02:24 2022-05-20 20:40:01.001 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4960/7393, mem: 8935Mb, iter_time: 0.322s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 7.342e-03, size: 512, ETA: 9 days, 15:01:34 2022-05-20 20:40:04.950 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4970/7393, mem: 8935Mb, iter_time: 0.394s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.345e-03, size: 576, ETA: 9 days, 15:01:42 2022-05-20 20:40:08.577 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4980/7393, mem: 8935Mb, iter_time: 0.362s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.347e-03, size: 544, ETA: 9 days, 15:01:24 2022-05-20 20:40:14.323 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 4990/7393, mem: 8935Mb, iter_time: 0.574s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.350e-03, size: 768, ETA: 9 days, 15:03:57 2022-05-20 20:40:18.580 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5000/7393, mem: 8935Mb, iter_time: 0.425s, data_time: 0.001s, total_loss: 9.1, 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- epoch: 4/300, iter: 5040/7393, mem: 8935Mb, iter_time: 0.323s, data_time: 0.002s, total_loss: 10.8, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 7.363e-03, size: 512, ETA: 9 days, 15:05:07 2022-05-20 20:40:38.089 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5050/7393, mem: 8935Mb, iter_time: 0.364s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 7.366e-03, size: 544, ETA: 9 days, 15:04:50 2022-05-20 20:40:40.792 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5060/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.004s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.369e-03, size: 384, ETA: 9 days, 15:03:18 2022-05-20 20:40:44.840 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5070/7393, mem: 8935Mb, iter_time: 0.404s, data_time: 0.004s, total_loss: 8.3, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.372e-03, 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data_time: 0.005s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.382e-03, size: 320, ETA: 9 days, 15:02:01 2022-05-20 20:41:04.547 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5120/7393, mem: 8935Mb, iter_time: 0.545s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 7.385e-03, size: 736, ETA: 9 days, 15:04:09 2022-05-20 20:41:06.844 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5130/7393, mem: 8935Mb, iter_time: 0.229s, data_time: 0.004s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 7.388e-03, size: 352, ETA: 9 days, 15:02:05 2022-05-20 20:41:12.201 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5140/7393, mem: 8935Mb, iter_time: 0.535s, data_time: 0.002s, total_loss: 11.5, loss_cls: 9.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.391e-03, size: 736, ETA: 9 days, 15:04:06 2022-05-20 20:41:17.823 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5150/7393, mem: 8935Mb, iter_time: 0.562s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.393e-03, size: 736, ETA: 9 days, 15:06:28 2022-05-20 20:41:20.524 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5160/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.4, lr: 7.396e-03, size: 416, ETA: 9 days, 15:04:55 2022-05-20 20:41:25.682 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5170/7393, mem: 8935Mb, iter_time: 0.515s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.399e-03, size: 704, ETA: 9 days, 15:06:40 2022-05-20 20:41:30.103 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5180/7393, mem: 8935Mb, iter_time: 0.442s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.4, 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5220/7393, mem: 8935Mb, iter_time: 0.404s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.6, lr: 7.412e-03, size: 608, ETA: 9 days, 15:05:02 2022-05-20 20:41:47.323 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5230/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.415e-03, size: 544, ETA: 9 days, 15:04:42 2022-05-20 20:41:49.849 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5240/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.418e-03, size: 384, ETA: 9 days, 15:02:54 2022-05-20 20:41:52.957 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5250/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.006s, total_loss: 10.8, loss_cls: 9.2, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.420e-03, size: 416, ETA: 9 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total_loss: 9.9, loss_cls: 8.5, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.431e-03, size: 704, ETA: 9 days, 15:01:57 2022-05-20 20:42:10.687 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5300/7393, mem: 8935Mb, iter_time: 0.229s, data_time: 0.004s, total_loss: 8.8, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.434e-03, size: 320, ETA: 9 days, 14:59:53 2022-05-20 20:42:15.882 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5310/7393, mem: 8935Mb, iter_time: 0.519s, data_time: 0.004s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 7.436e-03, size: 704, ETA: 9 days, 15:01:40 2022-05-20 20:42:18.578 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5320/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.439e-03, size: 416, ETA: 9 days, 15:00:08 2022-05-20 20:42:23.273 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5330/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.442e-03, size: 672, ETA: 9 days, 15:01:15 2022-05-20 20:42:27.751 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5340/7393, mem: 8935Mb, iter_time: 0.447s, data_time: 0.001s, total_loss: 10.8, loss_cls: 9.3, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.445e-03, size: 640, ETA: 9 days, 15:02:05 2022-05-20 20:42:29.829 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5350/7393, mem: 8935Mb, iter_time: 0.207s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.447e-03, size: 320, ETA: 9 days, 14:59:44 2022-05-20 20:42:34.017 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5360/7393, mem: 8935Mb, iter_time: 0.418s, data_time: 0.004s, total_loss: 9.7, loss_cls: 8.4, loss_iou: 0.3, loss_dfl: 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yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5620/7393, mem: 8935Mb, iter_time: 0.318s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.520e-03, size: 480, ETA: 9 days, 14:41:06 2022-05-20 20:44:03.862 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5630/7393, mem: 8935Mb, iter_time: 0.410s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.9, lr: 7.523e-03, size: 608, ETA: 9 days, 14:41:27 2022-05-20 20:44:06.367 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5640/7393, mem: 8935Mb, iter_time: 0.250s, data_time: 0.002s, total_loss: 11.8, loss_cls: 10.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.9, lr: 7.526e-03, size: 416, ETA: 9 days, 14:39:41 2022-05-20 20:44:11.137 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5650/7393, mem: 8935Mb, iter_time: 0.476s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, 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yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5800/7393, mem: 8935Mb, iter_time: 0.423s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.569e-03, size: 608, ETA: 9 days, 14:38:29 2022-05-20 20:45:09.294 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5810/7393, mem: 8935Mb, iter_time: 0.229s, data_time: 0.002s, total_loss: 8.5, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.572e-03, size: 384, ETA: 9 days, 14:36:27 2022-05-20 20:45:12.807 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5820/7393, mem: 8935Mb, iter_time: 0.350s, data_time: 0.006s, total_loss: 8.0, loss_cls: 6.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.574e-03, size: 512, ETA: 9 days, 14:36:01 2022-05-20 20:45:17.724 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5830/7393, mem: 8935Mb, iter_time: 0.491s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.410s, data_time: 0.003s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.588e-03, size: 608, ETA: 9 days, 14:34:27 2022-05-20 20:45:33.243 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5880/7393, mem: 8935Mb, iter_time: 0.239s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.591e-03, size: 352, ETA: 9 days, 14:32:34 2022-05-20 20:45:36.043 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5890/7393, mem: 8935Mb, iter_time: 0.279s, data_time: 0.004s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 7.593e-03, size: 416, ETA: 9 days, 14:31:12 2022-05-20 20:45:41.855 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5900/7393, mem: 8935Mb, iter_time: 0.581s, data_time: 0.003s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.8, lr: 7.596e-03, size: 768, ETA: 9 days, 14:33:46 2022-05-20 20:45:44.221 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5910/7393, mem: 8935Mb, iter_time: 0.236s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.599e-03, size: 320, ETA: 9 days, 14:31:50 2022-05-20 20:45:47.371 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5920/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.007s, total_loss: 10.1, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.602e-03, size: 448, ETA: 9 days, 14:30:56 2022-05-20 20:45:52.367 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5930/7393, mem: 8935Mb, iter_time: 0.499s, data_time: 0.002s, total_loss: 10.5, loss_cls: 8.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.604e-03, size: 704, ETA: 9 days, 14:32:25 2022-05-20 20:45:55.094 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5940/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.004s, total_loss: 8.5, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.607e-03, size: 416, ETA: 9 days, 14:30:58 2022-05-20 20:46:00.182 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5950/7393, mem: 8935Mb, iter_time: 0.508s, data_time: 0.004s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.610e-03, size: 704, ETA: 9 days, 14:32:35 2022-05-20 20:46:05.345 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5960/7393, mem: 8935Mb, iter_time: 0.516s, data_time: 0.001s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.612e-03, size: 704, ETA: 9 days, 14:34:17 2022-05-20 20:46:08.340 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5970/7393, mem: 8935Mb, iter_time: 0.299s, data_time: 0.003s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.1, lr: 7.615e-03, size: 448, ETA: 9 days, 14:33:11 2022-05-20 20:46:13.034 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5980/7393, mem: 8935Mb, iter_time: 0.469s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.6, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.618e-03, size: 672, ETA: 9 days, 14:34:17 2022-05-20 20:46:15.728 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 5990/7393, mem: 8935Mb, iter_time: 0.269s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.620e-03, size: 416, ETA: 9 days, 14:32:48 2022-05-20 20:46:20.063 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6000/7393, mem: 8935Mb, iter_time: 0.433s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.623e-03, size: 640, ETA: 9 days, 14:33:26 2022-05-20 20:46:25.861 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6010/7393, mem: 8935Mb, iter_time: 0.579s, data_time: 0.001s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.8, lr: 7.626e-03, size: 768, ETA: 9 days, 14:35:57 2022-05-20 20:46:28.345 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6020/7393, mem: 8935Mb, iter_time: 0.248s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.629e-03, size: 384, ETA: 9 days, 14:34:12 2022-05-20 20:46:31.116 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6030/7393, mem: 8935Mb, iter_time: 0.276s, data_time: 0.004s, total_loss: 9.6, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.631e-03, size: 416, ETA: 9 days, 14:32:48 2022-05-20 20:46:36.158 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6040/7393, mem: 8935Mb, iter_time: 0.503s, data_time: 0.002s, total_loss: 10.0, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.8, lr: 7.634e-03, size: 704, ETA: 9 days, 14:34:20 2022-05-20 20:46:40.015 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6050/7393, mem: 8935Mb, iter_time: 0.385s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.637e-03, size: 576, ETA: 9 days, 14:34:21 2022-05-20 20:46:43.235 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6060/7393, mem: 8935Mb, iter_time: 0.321s, data_time: 0.002s, total_loss: 10.1, loss_cls: 8.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.639e-03, size: 512, ETA: 9 days, 14:33:33 2022-05-20 20:46:47.351 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6070/7393, mem: 8935Mb, iter_time: 0.411s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.642e-03, size: 608, ETA: 9 days, 14:33:54 2022-05-20 20:46:52.079 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6080/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.001s, total_loss: 11.6, loss_cls: 10.2, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 1.6, lr: 7.645e-03, size: 672, ETA: 9 days, 14:35:02 2022-05-20 20:46:54.958 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6090/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.648e-03, size: 448, ETA: 9 days, 14:33:47 2022-05-20 20:46:59.738 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6100/7393, mem: 8935Mb, iter_time: 0.477s, data_time: 0.003s, total_loss: 9.3, loss_cls: 7.8, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.4, lr: 7.650e-03, size: 672, ETA: 9 days, 14:34:59 2022-05-20 20:47:02.020 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6110/7393, mem: 8935Mb, iter_time: 0.227s, data_time: 0.003s, total_loss: 8.9, loss_cls: 7.5, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.653e-03, size: 352, ETA: 9 days, 14:32:58 2022-05-20 20:47:05.559 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6120/7393, mem: 8935Mb, iter_time: 0.353s, data_time: 0.006s, total_loss: 9.4, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.656e-03, size: 544, ETA: 9 days, 14:32:34 2022-05-20 20:47:08.376 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6130/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.658e-03, size: 448, ETA: 9 days, 14:31:15 2022-05-20 20:47:13.721 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6140/7393, mem: 8935Mb, iter_time: 0.534s, data_time: 0.003s, total_loss: 9.7, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.2, lr: 7.661e-03, size: 736, ETA: 9 days, 14:33:11 2022-05-20 20:47:15.965 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6150/7393, mem: 8935Mb, iter_time: 0.223s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.664e-03, size: 352, ETA: 9 days, 14:31:07 2022-05-20 20:47:19.554 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6160/7393, mem: 8935Mb, iter_time: 0.358s, data_time: 0.004s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.6, lr: 7.666e-03, size: 544, ETA: 9 days, 14:30:46 2022-05-20 20:47:21.826 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6170/7393, mem: 8935Mb, iter_time: 0.226s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.669e-03, size: 352, ETA: 9 days, 14:28:44 2022-05-20 20:47:26.196 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6180/7393, mem: 8935Mb, iter_time: 0.436s, data_time: 0.006s, total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 2.0, lr: 7.672e-03, size: 608, ETA: 9 days, 14:29:25 2022-05-20 20:47:31.648 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6190/7393, mem: 8935Mb, iter_time: 0.545s, data_time: 0.002s, total_loss: 11.6, loss_cls: 10.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.3, lr: 7.675e-03, size: 736, ETA: 9 days, 14:31:29 2022-05-20 20:47:35.326 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6200/7393, mem: 8935Mb, iter_time: 0.367s, data_time: 0.004s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.677e-03, size: 544, ETA: 9 days, 14:31:16 2022-05-20 20:47:40.456 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6210/7393, mem: 8935Mb, iter_time: 0.512s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.680e-03, size: 704, ETA: 9 days, 14:32:55 2022-05-20 20:47:46.380 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6220/7393, mem: 8935Mb, iter_time: 0.592s, data_time: 0.001s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 7.683e-03, size: 768, ETA: 9 days, 14:35:35 2022-05-20 20:47:51.324 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6230/7393, mem: 8935Mb, iter_time: 0.494s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.685e-03, size: 672, ETA: 9 days, 14:37:00 2022-05-20 20:47:56.109 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6240/7393, mem: 8935Mb, iter_time: 0.478s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.688e-03, size: 672, ETA: 9 days, 14:38:12 2022-05-20 20:48:01.977 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6250/7393, mem: 8935Mb, iter_time: 0.586s, data_time: 0.001s, total_loss: 9.3, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.691e-03, size: 768, ETA: 9 days, 14:40:48 2022-05-20 20:48:05.161 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6260/7393, mem: 8935Mb, iter_time: 0.318s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.693e-03, size: 480, ETA: 9 days, 14:39:57 2022-05-20 20:48:07.589 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6270/7393, mem: 8935Mb, iter_time: 0.242s, data_time: 0.004s, total_loss: 9.3, loss_cls: 7.9, loss_iou: 0.3, loss_dfl: 1.1, loss_l1: 0.7, lr: 7.696e-03, size: 384, ETA: 9 days, 14:38:07 2022-05-20 20:48:10.433 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6280/7393, mem: 8935Mb, iter_time: 0.282s, data_time: 0.008s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.699e-03, size: 416, ETA: 9 days, 14:36:49 2022-05-20 20:48:13.537 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6290/7393, mem: 8935Mb, iter_time: 0.309s, data_time: 0.004s, total_loss: 9.5, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.702e-03, size: 448, ETA: 9 days, 14:35:51 2022-05-20 20:48:16.071 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6300/7393, mem: 8935Mb, iter_time: 0.252s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.704e-03, size: 416, ETA: 9 days, 14:34:10 2022-05-20 20:48:21.393 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6310/7393, mem: 8935Mb, iter_time: 0.532s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 2.0, lr: 7.707e-03, size: 736, ETA: 9 days, 14:36:03 2022-05-20 20:48:24.691 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6320/7393, mem: 8935Mb, iter_time: 0.329s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.710e-03, size: 512, ETA: 9 days, 14:35:21 2022-05-20 20:48:27.757 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6330/7393, mem: 8935Mb, iter_time: 0.306s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.712e-03, size: 480, ETA: 9 days, 14:34:21 2022-05-20 20:48:32.212 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6340/7393, mem: 8935Mb, iter_time: 0.445s, data_time: 0.002s, total_loss: 8.5, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.715e-03, size: 640, ETA: 9 days, 14:35:08 2022-05-20 20:48:36.034 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6350/7393, mem: 8935Mb, iter_time: 0.382s, data_time: 0.002s, total_loss: 9.5, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.1, lr: 7.718e-03, size: 576, ETA: 9 days, 14:35:06 2022-05-20 20:48:38.032 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6360/7393, mem: 8935Mb, iter_time: 0.199s, data_time: 0.002s, total_loss: 8.2, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.721e-03, size: 320, ETA: 9 days, 14:32:44 2022-05-20 20:48:40.914 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6370/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.005s, total_loss: 9.2, loss_cls: 7.7, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.723e-03, size: 416, ETA: 9 days, 14:31:29 2022-05-20 20:48:43.968 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6380/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.005s, total_loss: 8.7, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.726e-03, size: 416, ETA: 9 days, 14:30:28 2022-05-20 20:48:46.384 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6390/7393, mem: 8935Mb, iter_time: 0.240s, data_time: 0.004s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.729e-03, size: 320, ETA: 9 days, 14:28:38 2022-05-20 20:48:49.382 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6400/7393, mem: 8935Mb, iter_time: 0.298s, data_time: 0.012s, total_loss: 8.5, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.731e-03, size: 352, ETA: 9 days, 14:27:32 2022-05-20 20:48:53.355 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6410/7393, mem: 8935Mb, iter_time: 0.396s, data_time: 0.007s, total_loss: 9.5, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.734e-03, size: 544, ETA: 9 days, 14:27:42 2022-05-20 20:48:56.360 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6420/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.003s, total_loss: 9.1, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.737e-03, size: 480, ETA: 9 days, 14:26:37 2022-05-20 20:48:59.505 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6430/7393, mem: 8935Mb, iter_time: 0.314s, data_time: 0.002s, total_loss: 9.3, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.739e-03, size: 512, ETA: 9 days, 14:25:43 2022-05-20 20:49:02.427 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6440/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.742e-03, size: 480, ETA: 9 days, 14:24:33 2022-05-20 20:49:07.949 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6450/7393, mem: 8935Mb, iter_time: 0.551s, data_time: 0.004s, total_loss: 9.8, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.745e-03, size: 736, ETA: 9 days, 14:26:41 2022-05-20 20:49:11.054 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6460/7393, mem: 8935Mb, iter_time: 0.310s, data_time: 0.002s, total_loss: 8.5, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.748e-03, size: 480, ETA: 9 days, 14:25:44 2022-05-20 20:49:14.031 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6470/7393, mem: 8935Mb, iter_time: 0.297s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.750e-03, size: 480, ETA: 9 days, 14:24:38 2022-05-20 20:49:17.203 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6480/7393, mem: 8935Mb, iter_time: 0.316s, data_time: 0.002s, total_loss: 8.4, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.753e-03, size: 480, ETA: 9 days, 14:23:46 2022-05-20 20:49:19.933 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6490/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.005s, total_loss: 8.8, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.756e-03, size: 416, ETA: 9 days, 14:22:21 2022-05-20 20:49:22.680 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6500/7393, mem: 8935Mb, iter_time: 0.274s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.3, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 7.758e-03, size: 448, ETA: 9 days, 14:20:57 2022-05-20 20:49:26.166 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6510/7393, mem: 8935Mb, iter_time: 0.348s, data_time: 0.004s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.761e-03, size: 544, ETA: 9 days, 14:20:29 2022-05-20 20:49:29.222 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6520/7393, mem: 8935Mb, iter_time: 0.305s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.764e-03, size: 480, ETA: 9 days, 14:19:29 2022-05-20 20:49:32.138 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6530/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.002s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.767e-03, size: 448, ETA: 9 days, 14:18:18 2022-05-20 20:49:34.960 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6540/7393, mem: 8935Mb, iter_time: 0.281s, data_time: 0.003s, total_loss: 8.0, loss_cls: 6.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.769e-03, size: 416, ETA: 9 days, 14:17:00 2022-05-20 20:49:37.475 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6550/7393, mem: 8935Mb, iter_time: 0.251s, data_time: 0.003s, total_loss: 10.8, loss_cls: 9.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.772e-03, size: 320, ETA: 9 days, 14:15:19 2022-05-20 20:49:42.004 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6560/7393, mem: 8935Mb, iter_time: 0.451s, data_time: 0.005s, total_loss: 8.9, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.775e-03, size: 608, ETA: 9 days, 14:16:10 2022-05-20 20:49:46.326 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6570/7393, mem: 8935Mb, iter_time: 0.432s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.777e-03, size: 640, ETA: 9 days, 14:16:46 2022-05-20 20:49:50.169 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6580/7393, mem: 8935Mb, iter_time: 0.384s, data_time: 0.003s, total_loss: 8.9, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.2, lr: 7.780e-03, size: 576, ETA: 9 days, 14:16:46 2022-05-20 20:49:52.610 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6590/7393, mem: 8935Mb, iter_time: 0.243s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, 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6630/7393, mem: 8935Mb, iter_time: 0.477s, data_time: 0.002s, total_loss: 8.8, loss_cls: 7.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.794e-03, size: 672, ETA: 9 days, 14:18:54 2022-05-20 20:50:14.116 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6640/7393, mem: 8935Mb, iter_time: 0.304s, data_time: 0.002s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 7.796e-03, size: 480, ETA: 9 days, 14:17:54 2022-05-20 20:50:16.832 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6650/7393, mem: 8935Mb, iter_time: 0.271s, data_time: 0.004s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.799e-03, size: 448, ETA: 9 days, 14:16:28 2022-05-20 20:50:22.320 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6660/7393, mem: 8935Mb, iter_time: 0.548s, data_time: 0.002s, total_loss: 10.3, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.802e-03, size: 736, ETA: 9 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total_loss: 10.1, loss_cls: 8.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.813e-03, size: 576, ETA: 9 days, 14:17:52 2022-05-20 20:50:42.974 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6710/7393, mem: 8935Mb, iter_time: 0.579s, data_time: 0.001s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.815e-03, size: 768, ETA: 9 days, 14:20:21 2022-05-20 20:50:48.317 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6720/7393, mem: 8935Mb, iter_time: 0.534s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.818e-03, size: 704, ETA: 9 days, 14:22:14 2022-05-20 20:50:52.232 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6730/7393, mem: 8935Mb, iter_time: 0.391s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 7.821e-03, size: 576, ETA: 9 days, 14:22:20 2022-05-20 20:50:56.962 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6740/7393, mem: 8935Mb, iter_time: 0.472s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.823e-03, size: 672, ETA: 9 days, 14:23:27 2022-05-20 20:51:02.806 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6750/7393, mem: 8935Mb, iter_time: 0.584s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.826e-03, size: 768, ETA: 9 days, 14:25:58 2022-05-20 20:51:06.511 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6760/7393, mem: 8935Mb, iter_time: 0.369s, data_time: 0.002s, total_loss: 8.3, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.829e-03, size: 544, ETA: 9 days, 14:25:46 2022-05-20 20:51:08.837 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6770/7393, mem: 8935Mb, iter_time: 0.232s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 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8935Mb, iter_time: 0.276s, data_time: 0.003s, total_loss: 9.0, loss_cls: 7.2, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.1, lr: 7.842e-03, size: 448, ETA: 9 days, 14:22:35 2022-05-20 20:51:28.310 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6820/7393, mem: 8935Mb, iter_time: 0.508s, data_time: 0.002s, total_loss: 10.2, loss_cls: 8.8, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.2, lr: 7.845e-03, size: 704, ETA: 9 days, 14:24:09 2022-05-20 20:51:30.583 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6830/7393, mem: 8935Mb, iter_time: 0.226s, data_time: 0.004s, total_loss: 10.0, loss_cls: 8.4, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.848e-03, size: 320, ETA: 9 days, 14:22:10 2022-05-20 20:51:33.504 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6840/7393, mem: 8935Mb, iter_time: 0.291s, data_time: 0.004s, total_loss: 7.9, loss_cls: 6.3, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.3, lr: 7.850e-03, size: 416, ETA: 9 days, 14:21:00 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loss_cls: 8.0, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.861e-03, size: 480, ETA: 9 days, 14:16:58 2022-05-20 20:51:49.261 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6890/7393, mem: 8935Mb, iter_time: 0.357s, data_time: 0.004s, total_loss: 8.7, loss_cls: 7.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.2, lr: 7.864e-03, size: 544, ETA: 9 days, 14:16:37 2022-05-20 20:51:51.755 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6900/7393, mem: 8935Mb, iter_time: 0.248s, data_time: 0.006s, total_loss: 9.3, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.7, lr: 7.867e-03, size: 384, ETA: 9 days, 14:14:55 2022-05-20 20:51:55.003 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6910/7393, mem: 8935Mb, iter_time: 0.324s, data_time: 0.005s, total_loss: 9.1, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 7.869e-03, size: 512, ETA: 9 days, 14:14:10 2022-05-20 20:51:58.561 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6920/7393, mem: 8935Mb, iter_time: 0.355s, data_time: 0.002s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.872e-03, size: 544, ETA: 9 days, 14:13:49 2022-05-20 20:52:03.579 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6930/7393, mem: 8935Mb, iter_time: 0.501s, data_time: 0.002s, total_loss: 8.1, loss_cls: 6.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.875e-03, size: 704, ETA: 9 days, 14:15:17 2022-05-20 20:52:07.153 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6940/7393, mem: 8935Mb, iter_time: 0.357s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.1, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.877e-03, size: 544, ETA: 9 days, 14:14:57 2022-05-20 20:52:10.704 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6950/7393, mem: 8935Mb, iter_time: 0.354s, data_time: 0.002s, total_loss: 9.7, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.5, loss_l1: 1.2, lr: 7.880e-03, size: 544, ETA: 9 days, 14:14:35 2022-05-20 20:52:14.661 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6960/7393, mem: 8935Mb, iter_time: 0.395s, data_time: 0.002s, total_loss: 7.8, loss_cls: 6.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.883e-03, size: 576, ETA: 9 days, 14:14:43 2022-05-20 20:52:20.370 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6970/7393, mem: 8935Mb, iter_time: 0.570s, data_time: 0.001s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.886e-03, size: 768, ETA: 9 days, 14:17:03 2022-05-20 20:52:24.284 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6980/7393, mem: 8935Mb, iter_time: 0.391s, data_time: 0.001s, total_loss: 8.8, loss_cls: 7.4, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.888e-03, size: 576, ETA: 9 days, 14:17:09 2022-05-20 20:52:27.875 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 6990/7393, mem: 8935Mb, iter_time: 0.359s, data_time: 0.002s, total_loss: 10.4, loss_cls: 8.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.5, lr: 7.891e-03, size: 544, ETA: 9 days, 14:16:50 2022-05-20 20:52:31.208 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7000/7393, mem: 8935Mb, iter_time: 0.331s, data_time: 0.002s, total_loss: 9.8, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.894e-03, size: 512, ETA: 9 days, 14:16:10 2022-05-20 20:52:34.449 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7010/7393, mem: 8935Mb, iter_time: 0.323s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.896e-03, size: 512, ETA: 9 days, 14:15:25 2022-05-20 20:52:37.181 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7020/7393, mem: 8935Mb, iter_time: 0.272s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.899e-03, size: 448, ETA: 9 days, 14:14:01 2022-05-20 20:52:42.884 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7030/7393, mem: 8935Mb, iter_time: 0.570s, data_time: 0.001s, total_loss: 9.1, loss_cls: 7.7, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 1.0, lr: 7.902e-03, size: 768, ETA: 9 days, 14:16:20 2022-05-20 20:52:48.487 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7040/7393, mem: 8935Mb, iter_time: 0.560s, data_time: 0.001s, total_loss: 9.5, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.905e-03, size: 736, ETA: 9 days, 14:18:32 2022-05-20 20:52:54.033 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7050/7393, mem: 8935Mb, iter_time: 0.554s, data_time: 0.001s, total_loss: 10.2, loss_cls: 8.6, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.4, lr: 7.907e-03, size: 736, ETA: 9 days, 14:20:40 2022-05-20 20:52:56.490 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7060/7393, mem: 8935Mb, iter_time: 0.245s, data_time: 0.002s, total_loss: 7.5, loss_cls: 6.3, loss_iou: 0.3, loss_dfl: 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- epoch: 4/300, iter: 7210/7393, mem: 8935Mb, iter_time: 0.517s, data_time: 0.002s, total_loss: 9.4, loss_cls: 7.9, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.950e-03, size: 704, ETA: 9 days, 14:21:21 2022-05-20 20:54:00.286 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7220/7393, mem: 8935Mb, iter_time: 0.415s, data_time: 0.001s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.9, lr: 7.953e-03, size: 608, ETA: 9 days, 14:21:44 2022-05-20 20:54:02.805 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7230/7393, mem: 8935Mb, iter_time: 0.251s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.4, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.956e-03, size: 416, ETA: 9 days, 14:20:05 2022-05-20 20:54:08.157 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7240/7393, mem: 8935Mb, iter_time: 0.534s, data_time: 0.002s, total_loss: 8.7, loss_cls: 7.2, loss_iou: 0.3, loss_dfl: 1.2, loss_l1: 0.7, lr: 7.959e-03, 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0.002s, total_loss: 8.2, loss_cls: 6.7, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.969e-03, size: 480, ETA: 9 days, 14:19:01 2022-05-20 20:54:24.083 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7290/7393, mem: 8935Mb, iter_time: 0.287s, data_time: 0.004s, total_loss: 9.6, loss_cls: 8.0, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.1, lr: 7.972e-03, size: 448, ETA: 9 days, 14:17:49 2022-05-20 20:54:27.590 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7300/7393, mem: 8935Mb, iter_time: 0.347s, data_time: 0.002s, total_loss: 9.9, loss_cls: 8.2, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.5, lr: 7.975e-03, size: 544, ETA: 9 days, 14:17:22 2022-05-20 20:54:30.797 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7310/7393, mem: 8935Mb, iter_time: 0.320s, data_time: 0.002s, total_loss: 9.0, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 0.8, lr: 7.978e-03, size: 512, ETA: 9 days, 14:16:34 2022-05-20 20:54:34.891 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7320/7393, mem: 8935Mb, iter_time: 0.409s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.5, loss_iou: 0.4, loss_dfl: 1.3, loss_l1: 1.2, lr: 7.980e-03, size: 608, ETA: 9 days, 14:16:53 2022-05-20 20:54:39.634 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7330/7393, mem: 8935Mb, iter_time: 0.474s, data_time: 0.001s, total_loss: 9.4, loss_cls: 7.8, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.983e-03, size: 672, ETA: 9 days, 14:17:59 2022-05-20 20:54:42.240 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7340/7393, mem: 8935Mb, iter_time: 0.260s, data_time: 0.002s, total_loss: 8.0, loss_cls: 6.4, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 1.2, lr: 7.986e-03, size: 416, ETA: 9 days, 14:16:27 2022-05-20 20:54:45.252 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7350/7393, mem: 8935Mb, iter_time: 0.300s, data_time: 0.004s, total_loss: 7.8, loss_cls: 6.2, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.8, lr: 7.988e-03, size: 480, ETA: 9 days, 14:15:25 2022-05-20 20:54:47.956 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7360/7393, mem: 8935Mb, iter_time: 0.270s, data_time: 0.002s, total_loss: 8.6, loss_cls: 6.8, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 0.9, lr: 7.991e-03, size: 448, ETA: 9 days, 14:14:01 2022-05-20 20:54:50.624 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7370/7393, mem: 8935Mb, iter_time: 0.266s, data_time: 0.002s, total_loss: 9.2, loss_cls: 7.6, loss_iou: 0.3, loss_dfl: 1.3, loss_l1: 1.0, lr: 7.994e-03, size: 416, ETA: 9 days, 14:12:33 2022-05-20 20:54:58.177 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7380/7393, mem: 8935Mb, iter_time: 0.755s, data_time: 0.189s, total_loss: 8.6, loss_cls: 6.9, loss_iou: 0.4, loss_dfl: 1.4, loss_l1: 1.0, lr: 7.996e-03, size: 768, ETA: 9 days, 14:17:08 2022-05-20 20:55:00.963 | INFO | yolox.core.trainer:after_iter:273 - epoch: 4/300, iter: 7390/7393, mem: 8935Mb, iter_time: 0.278s, data_time: 0.002s, total_loss: 8.6, loss_cls: 7.1, loss_iou: 0.4, loss_dfl: 1.2, loss_l1: 0.6, lr: 7.999e-03, size: 416, ETA: 9 days, 14:15:50 2022-05-20 20:55:02.310 | INFO | yolox.core.trainer:save_ckpt:364 - Save weights to ./YOLOX_outputs/ppyoloe_s_sigmoid 2022-05-20 20:55:02.521 | INFO | yolox.core.trainer:before_epoch:214 - ---> start train epoch5 2022-05-20 20:55:02.521 | INFO | yolox.core.trainer:before_epoch:217 - --->No mosaic aug now! 2022-05-20 20:55:02.522 | INFO | yolox.core.trainer:before_epoch:219 - --->Add additional L1 loss now! 2022-05-20 20:55:05.830 | INFO | yolox.core.trainer:after_train:207 - Training of experiment is done and the best AP is 0.00 2022-05-20 20:55:05.830 | ERROR | yolox.core.launch:launch:98 - An error has been caught in function 'launch', process 'MainProcess' (14977), thread 'MainThread' (139817742497536): Traceback (most recent call last): File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) └ ModuleSpec(name='yolox.tools.train', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7f296898a110>, origin='/... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'yolox.tools', '__loader__': <_frozen_importlib_external.SourceFileL... └ at 0x7f29d997e030, file "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 5> File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 139, in args=(exp, args), │ └ Namespace(batch_size=16, cache=False, ckpt='pretrained_weight/ppyoloe_s.pth', devices=1, dist_backend='nccl', dist_url=None, ... └ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════... > File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/launch.py", line 98, in launch main_func(*args) │ └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════... └ File "/home/ipcam-pc/PPYOLOE_pytorch/tools/train.py", line 117, in main trainer.train() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 77, in train self.train_in_epoch() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 86, in train_in_epoch self.train_in_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 92, in train_in_iter self.train_one_iter() │ └ └ File "/home/ipcam-pc/PPYOLOE_pytorch/yolox/core/trainer.py", line 110, in train_one_iter outputs = self.model(inps, targets, extra_info) │ │ │ │ └ {'epoch': 4} │ │ │ └ tensor([[[ 0.0000, 481.0000, 342.2500, 564.0000, 528.0000], │ │ │ [ 24.0000, 502.7500, 350.0000, 538.0000, 425.0000], │ │ │ ... │ │ └ tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540, ... │ └ PPYOLOE( │ (backbone): CSPResNet( │ (stem): Sequential( │ (0): ConvBNLayer( │ (conv): Conv2d(3, 16, kernel_size=(... └ File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ (tensor([[[[-0.3540, -0.3540, -0.3540, ..., -0.3540, -0.3540, -0.3540], │ │ [-0.3540, -0.3540, -0.3540, ..., -0.3540,... │ └ └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe.py", line 18, in forward yolo_losses = self.head(neck_feats, targets, extra_info) │ │ │ └ {'epoch': 4} │ │ └ tensor([[[ 0.0000, 481.0000, 342.2500, 564.0000, 528.0000], │ │ [ 24.0000, 502.7500, 350.0000, 538.0000, 425.0000], │ │ ... │ └ [tensor([[[[ 1.2988e-01, -1.3379e-01, -1.7078e-01, ..., -3.6072e-02, │ 3.3478e-02, 2.5854e-01], │ [ 4.414... └ PPYOLOE( (backbone): CSPResNet( (stem): Sequential( (0): ConvBNLayer( (conv): Conv2d(3, 16, kernel_size=(... File "/home/ipcam-pc/.conda/envs/yolox/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) │ │ │ └ {} │ │ └ ([tensor([[[[ 1.2988e-01, -1.3379e-01, -1.7078e-01, ..., -3.6072e-02, │ │ 3.3478e-02, 2.5854e-01], │ │ [ 4.41... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 219, in forward return self.forward_train(feats, targets, extra_info) │ │ │ │ └ {'epoch': 4} │ │ │ └ tensor([[[ 0.0000, 481.0000, 342.2500, 564.0000, 528.0000], │ │ │ [ 24.0000, 502.7500, 350.0000, 538.0000, 425.0000], │ │ │ ... │ │ └ [tensor([[[[ 1.2988e-01, -1.3379e-01, -1.7078e-01, ..., -3.6072e-02, │ │ 3.3478e-02, 2.5854e-01], │ │ [ 4.414... │ └ └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 145, in forward_train ], targets, extra_info) │ └ {'epoch': 4} └ tensor([[[ 0.0000, 481.0000, 342.2500, 564.0000, 528.0000], [ 24.0000, 502.7500, 350.0000, 538.0000, 425.0000], ... File "/home/ipcam-pc/PPYOLOE_pytorch/ppyoloe/models/ppyoloe_head.py", line 289, in get_loss self.loss_weight['iou'] * loss_iou + \ │ │ └ tensor([0.]) │ └ {'class': 1.0, 'iou': 2.5, 'dfl': 0.5} └ PPYOLOEHead( (varifocal_loss): VarifocalLoss() (focal_loss): FocalLoss() (bbox_loss): BboxLoss( (iou_loss): GIoULos... RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!