Megvii-BaseDetection / Megvii-BaseDetection/YOLOX

Training AP is always 0 using coco128 dataset

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Description

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:

class AP class AP class AP
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 30.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 90.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000
per class AR:
class AR class AR class AR
:-------- :------ :-------- :------- :-------- :-------
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 90.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 90.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000

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:

class AP class AP class AP
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 90.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 60.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000
per class AR:
class AR class AR class AR
:-------- :------ :-------- :------- :-------- :------
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 90.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 60.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000

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:

class AP class AP class AP
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 40.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000
per class AR:
class AR class AR class AR
:-------- :------ :-------- :------- :-------- :------
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 80.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000

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:

class AP class AP class AP
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 0.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000
per class AR:
class AR class AR class AR
:-------- :------ :-------- :------ :-------- :------
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 0.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000

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:

class AP class AP class AP
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 0.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000
per class AR:
class AR class AR class AR
:-------- :------ :-------- :------ :-------- :------
0 nan 1 nan 2 nan
3 nan 4 nan 5 nan
6 nan 7 0.000 8 nan
9 nan 10 nan 11 nan
12 nan 13 nan 14 nan
15 nan 16 nan 17 nan
18 0.000 19 nan 20 nan
21 nan 22 nan 23 nan
24 nan 25 nan 26 0.000
27 nan 28 nan 29 nan
30 nan 31 nan 32 0.000
33 nan 34 0.000 35 nan
36 nan 37 0.000 38 nan
39 nan 40 nan 41 nan
42 nan 43 nan 44 0.000
45 nan 46 nan 47 nan
48 nan 49 0.000 50 nan
51 nan 52 nan 53 nan
54 nan 55 nan 56 0.000
57 0.000 58 nan 59 nan
60 nan 61 0.000 62 nan
63 nan 64 nan 65 nan
66 nan 67 nan 68 nan
69 0.000 70 0.000

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

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with tools/train.py and exps/example/custom/yolox_s.py, then inspect datasets/coco128 and the checkpoint-loading warnings in the log. Reproduce the command and compare the dataset annotations, configured num_classes, and evaluation output; done means identifying the cause of the zero/NaN AP or confirming a concrete correction with a successful evaluation.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
computer-vision, machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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