我希望用YOLOv5训练自己的数据集，我的数据集是不同背景下的飞鸟，瓢虫，蚂蚁，我希望在不同图片中识别出他们
YOLOv5 train.py运行后结果如下：
D:\YOLOv5\Anaconda3\envs\yolov5test\python.exe D:/Yolov5/yolov5/train.py
Using CUDA Apex device0 _CudaDeviceProperties(name='NVIDIA GeForce GTX 1650', total_memory=4095MB)
Namespace(batch_size=16, bucket='', cache_images=False, cfg='models/yolov5s.yaml', data='data/myvoc.yaml', device='', epochs=50, evolve=False, hyp='', img_size=[256, 256], local_rank=-1, multi_scale=False, name='', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='', world_size=1)
Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/
Hyperparameters {'optimizer': 'SGD', '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': 0.0, 'scale': 0.5, 'shear': 0.0}
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 21576 models.yolo.Detect [3, [[27, 31, 51, 56, 82, 76], [117, 137, 195, 167, 221, 269], [279, 441, 389, 316, 512, 524]], [128, 256, 512]]
Model Summary: 191 layers, 7.26049e+06 parameters, 7.26049e+06 gradients
Optimizer groups: 62 .bias, 70 conv.weight, 59 other
Traceback (most recent call last):
File "D:/Yolov5/yolov5/train.py", line 469, in
train(hyp, tb_writer, opt, device)
File "D:/Yolov5/yolov5/train.py", line 186, in train
world_size=opt.world_size)
File "D:\Yolov5\yolov5\utils\datasets.py", line 59, in create_dataloader
pad=pad)
File "D:\Yolov5\yolov5\utils\datasets.py", line 342, in init
labels, shapes = zip(*[cache[x] for x in self.img_files])
TypeError: zip argument #1 must support iteration
Process finished with exit code 1
走这个步骤，我毕业做的汽车识别的看的这个https://blog.csdn.net/ECHOSON/article/details/121939535