我希望用YOLOv5训练自己的数据集，我的数据集是游泳池中的人，我希望识别出他们的头部
YOLOv5 train.py运行后结果如下：
Using CPU
Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='models/yolov5s.yaml', data='data/swimmer.yaml', device='', epochs=300, evolve=False, exist_ok=False, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=4, world_size=1)
Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/
Hyperparameters {'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mixup': 0.0, 'lrf': 0.2, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'mosaic': 1.0}
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
D:\Python3.8.5\lib\site-packages\torch\nn\functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ..\c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Model Summary: 191 layers, 7.25509e+06 parameters, 7.25509e+06 gradients
Optimizer groups: 62 .bias, 70 conv.weight, 59 other
Transferred 362/370 items from yolov5s.pt
Scanning images: 100%|██████████| 1/1 [00:00<00:00, 40.11it/s]
Scanning labels D:\Python3.8.5\YOLO\yolov5\data\labels.cache (1 found, 0 missing, 0 empty, 0 duplicate, for 1 images): 100%|██████████| 1/1 [00:00<00:00, 501.35it/s]
Scanning images: 100%|██████████| 26/26 [00:00<00:00, 75.35it/s]
Scanning labels D:\Python3.8.5\YOLO\yolov5\data\labels.cache (26 found, 0 missing, 0 empty, 0 duplicate, for 26 images): 100%|██████████| 26/26 [00:00<00:00, 8690.78it/s]
Analyzing anchors... Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 test
Using 0 dataloader workers
Starting training for 300 epochs...
Epoch gpu_mem GIoU obj cls total targets img_size
0%| | 0/1 [00:01<?, ?it/s]
Traceback (most recent call last):
File "D:/Python3.8.5/YOLO/yolov5/train.py", line 496, in
train(hyp, opt, device, tb_writer)
File "D:/Python3.8.5/YOLO/yolov5/train.py", line 286, in train
loss, loss_items = compute_loss(pred, targets.to(device), model) # scaled by batch_size
File "D:\Python3.8.5\YOLO\yolov5\utils\utils.py", line 480, in compute_loss
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype) # giou ratio
RuntimeError: shape mismatch: value tensor of shape [18] cannot be broadcast to indexing result of shape [0]
