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Training AP is always 0 using coco128 dataset #1760
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@Jessica-hub did you resolve this. I am also getting the same. |
me tooo. I mean I get this error with custom dataset but still it would help to know why its like this with coco |
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I used the coco128 and unzip into the datasets folder. The command is python tools/train.py -f exps/example/custom/yolox_s.py -d 1 -b 32 --fp16 -o -c C:\Users\olivi\YOLOX\pretrained\yolox_s.pth However, the APs have a lot of nan and 0. The log details are below.
2024-03-03 15:05:28 | INFO | yolox.core.trainer:130 - args: Namespace(experiment_name='yolox_s', name=None, dist_backend='nccl', dist_url=None, batch_size=32, devices=1, exp_file='exps/example/custom/yolox_s.py', resume=False, ckpt='C:\Users\olivi\YOLOX\pretrained\yolox_s.pth', start_epoch=None, num_machines=1, machine_rank=0, fp16=True, cache=None, occupy=True, logger='tensorboard', opts=[])
2024-03-03 15:05:28 | INFO | yolox.core.trainer:131 - exp value:
╒═══════════════════╤════════════════════════════╕
│ keys │ values │
╞═══════════════════╪════════════════════════════╡
│ seed │ None │
├───────────────────┼────────────────────────────┤
│ output_dir │ './YOLOX_outputs' │
├───────────────────┼────────────────────────────┤
│ print_interval │ 10 │
├───────────────────┼────────────────────────────┤
│ eval_interval │ 1 │
├───────────────────┼────────────────────────────┤
│ dataset │ None │
├───────────────────┼────────────────────────────┤
│ num_classes │ 71 │
├───────────────────┼────────────────────────────┤
│ depth │ 0.33 │
├───────────────────┼────────────────────────────┤
│ width │ 0.5 │
├───────────────────┼────────────────────────────┤
│ act │ 'silu' │
├───────────────────┼────────────────────────────┤
│ data_num_workers │ 4 │
├───────────────────┼────────────────────────────┤
│ input_size │ (640, 640) │
├───────────────────┼────────────────────────────┤
│ multiscale_range │ 5 │
├───────────────────┼────────────────────────────┤
│ data_dir │ 'datasets/coco128' │
├───────────────────┼────────────────────────────┤
│ train_ann │ 'instances_train2017.json' │
├───────────────────┼────────────────────────────┤
│ val_ann │ 'instances_val2017.json' │
├───────────────────┼────────────────────────────┤
│ test_ann │ 'instances_test2017.json' │
├───────────────────┼────────────────────────────┤
│ mosaic_prob │ 1.0 │
├───────────────────┼────────────────────────────┤
│ mixup_prob │ 1.0 │
├───────────────────┼────────────────────────────┤
│ hsv_prob │ 1.0 │
├───────────────────┼────────────────────────────┤
│ flip_prob │ 0.5 │
├───────────────────┼────────────────────────────┤
│ degrees │ 10.0 │
├───────────────────┼────────────────────────────┤
│ translate │ 0.1 │
├───────────────────┼────────────────────────────┤
│ mosaic_scale │ (0.1, 2) │
├───────────────────┼────────────────────────────┤
│ enable_mixup │ True │
├───────────────────┼────────────────────────────┤
│ mixup_scale │ (0.5, 1.5) │
├───────────────────┼────────────────────────────┤
│ shear │ 2.0 │
├───────────────────┼────────────────────────────┤
│ warmup_epochs │ 5 │
├───────────────────┼────────────────────────────┤
│ max_epoch │ 300 │
├───────────────────┼────────────────────────────┤
│ warmup_lr │ 0 │
├───────────────────┼────────────────────────────┤
│ min_lr_ratio │ 0.05 │
├───────────────────┼────────────────────────────┤
│ basic_lr_per_img │ 0.00015625 │
├───────────────────┼────────────────────────────┤
│ scheduler │ 'yoloxwarmcos' │
├───────────────────┼────────────────────────────┤
│ no_aug_epochs │ 15 │
├───────────────────┼────────────────────────────┤
│ ema │ True │
├───────────────────┼────────────────────────────┤
│ weight_decay │ 0.0005 │
├───────────────────┼────────────────────────────┤
│ momentum │ 0.9 │
├───────────────────┼────────────────────────────┤
│ save_history_ckpt │ True │
├───────────────────┼────────────────────────────┤
│ exp_name │ 'yolox_s' │
├───────────────────┼────────────────────────────┤
│ test_size │ (640, 640) │
├───────────────────┼────────────────────────────┤
│ test_conf │ 0.01 │
├───────────────────┼────────────────────────────┤
│ nmsthre │ 0.65 │
╘═══════════════════╧════════════════════════════╛
qt.qpa.fonts: Unable to open default EUDC font: "EUDC.TTE"
2024-03-03 15:05:34 | INFO | yolox.core.trainer:136 - Model Summary: Params: 8.96M, Gflops: 26.91
2024-03-03 15:05:34 | INFO | yolox.core.trainer:319 - loading checkpoint for fine tuning
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.0.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.0.weight in model is torch.Size([71, 128, 1, 1]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.0.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.0.bias in model is torch.Size([71]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.1.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.1.weight in model is torch.Size([71, 128, 1, 1]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.1.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.1.bias in model is torch.Size([71]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.2.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.2.weight in model is torch.Size([71, 128, 1, 1]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.2.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.2.bias in model is torch.Size([71]).
2024-03-03 15:05:35 | INFO | yolox.data.datasets.coco:63 - loading annotations into memory...
2024-03-03 15:05:35 | INFO | yolox.data.datasets.coco:63 - Done (t=0.00s)
2024-03-03 15:05:35 | INFO | pycocotools.coco:86 - creating index...
2024-03-03 15:05:35 | INFO | pycocotools.coco:86 - index created!
2024-03-03 15:05:35 | INFO | yolox.core.trainer:155 - init prefetcher, this might take one minute or less...
C:\Users\olivi\yolox\yolox\utils\metric.py:43: UserWarning: The torch.cuda.DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=, device='cuda') to create tensors. (Triggered internally at ..\torch\csrc\tensor\python_tensor.cpp:85.)
x = torch.cuda.FloatTensor(256, 1024, block_mem)
2024-03-03 15:05:52 | INFO | yolox.data.datasets.coco:63 - loading annotations into memory...
2024-03-03 15:05:52 | INFO | yolox.data.datasets.coco:63 - Done (t=0.01s)
2024-03-03 15:05:52 | INFO | pycocotools.coco:86 - creating index...
2024-03-03 15:05:52 | INFO | pycocotools.coco:86 - index created!
2024-03-03 15:05:52 | INFO | yolox.core.trainer:191 - Training start...
2024-03-03 15:05:52 | INFO | yolox.core.trainer:192 -
YOLOX(
(backbone): YOLOPAFPN(
(backbone): CSPDarknet(
(stem): Focus(
(conv): BaseConv(
(conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(dark2): Sequential(
(0): BaseConv(
(conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(dark3): Sequential(
(0): BaseConv(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(dark4): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(dark5): Sequential(
(0): BaseConv(
(conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): SPPBottleneck(
(conv1): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): ModuleList(
(0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
(1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
(2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)
)
(conv2): BaseConv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
)
(upsample): Upsample(scale_factor=2.0, mode='nearest')
(lateral_conv0): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_p4): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(reduce_conv1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_p3): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(bu_conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_n3): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(bu_conv1): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_n4): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(head): YOLOXHead(
(cls_convs): ModuleList(
(0-2): 3 x Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
(reg_convs): ModuleList(
(0-2): 3 x Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
(cls_preds): ModuleList(
(0-2): 3 x Conv2d(128, 71, kernel_size=(1, 1), stride=(1, 1))
)
(reg_preds): ModuleList(
(0-2): 3 x Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
)
(obj_preds): ModuleList(
(0-2): 3 x Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
)
(stems): ModuleList(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(2): BaseConv(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(l1_loss): L1Loss()
(bcewithlog_loss): BCEWithLogitsLoss()
(iou_loss): IOUloss()
)
)
2024-03-03 15:05:52 | INFO | yolox.core.trainer:203 - ---> start train epoch1
2024-03-03 15:05:59 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.11s/it]
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:04 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:04 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:04 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.092
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.103
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.103
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.060
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.069
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.138
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.180
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
per class AP:
2024-03-03 15:06:04 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:05 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:05 | INFO | yolox.core.trainer:203 - ---> start train epoch2
2024-03-03 15:06:07 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.63s/it]
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:13 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.154
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
per class AP:
2024-03-03 15:06:13 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:14 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:14 | INFO | yolox.core.trainer:203 - ---> start train epoch3
2024-03-03 15:06:20 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:04<00:00, 4.98s/it]
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s).
2024-03-03 15:06:25 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.031
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.044
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.089
per class AP:
2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:25 | INFO | yolox.core.trainer:203 - ---> start train epoch4
2024-03-03 15:06:28 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.17s/it]
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s).
2024-03-03 15:06:33 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
per class AP:
2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:33 | INFO | yolox.core.trainer:203 - ---> start train epoch5
2024-03-03 15:06:36 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.09s/it]
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.02s).
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:41 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
per class AP:
2024-03-03 15:06:41 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:42 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:42 | INFO | yolox.core.trainer:203 - ---> start train epoch6
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