Traceback (most recent call last): File "E:\yolov5-master\train.py", line 642, in <module> main(opt) File "E:\yolov5-master\train.py", line 531, in main train(opt.hyp, opt, device, callbacks) File "E:\yolov5-master\train.py", line 312, in train pred = model(imgs) # forward File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\yolo.py", line 209, in forward return self._forward_once(x, profile, visualize) # single-scale inference, train File "E:\yolov5-master\models\yolo.py", line 121, in _forward_once x = m(x) # run File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\common.py", line 167, in forward return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\container.py", line 217, in forward input = module(input) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\common.py", line 120, in forward return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\common.py", line 56, in forward return self.act(self.bn(self.conv(x))) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\activation.py", line 396, in forward return F.silu(input, inplace=self.inplace) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\functional.py", line 2058, in silu return torch._C._nn.silu_(input) torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 6.00 GiB total capacity; 2.92 GiB already allocated; 951.00 MiB free; 3.01 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

这个错误提示是CUDA显存不足导致的。可能是模型过于复杂，超出了显存的限制。解决这个问题的方法是优化模型，减少显存使用，或者增加显存资源。你可以尝试以下解决方案：
1. 减小batch size，可以减少显存占用。
2. 将模型转移到CPU上运行。
3. 减小模型尺寸，可以减少显存占用。
4. 增加显存，可以更换显卡或增加显存条。
5. 优化代码，减少显存占用，例如使用inplace操作或减小tensor的维度。

相关问题

Traceback (most recent call last): File "E:\yolov5-master\train.py", line 642, in <module> main(opt) File "E:\yolov5-master\train.py", line 517, in main device = select_device(opt.device, batch_size=opt.batch_size) File "E:\yolov5-master\utils\torch_utils.py", line 118, in select_device assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ AssertionError: Invalid CUDA '--device 3' requested, use '--device cpu' or pass valid CUDA device(s)

这是一个错误信息，出现在使用 YOLOv5 进行训练时。错误的原因是选择了一个无效的 CUDA 设备，可能是因为设备号错误或者没有可用的 GPU。建议检查一下 CUDA 设备的设置，确保设备号正确并且该设备可用。如果您没有可用的 GPU，可以选择使用 CPU 进行训练，通过设置 '--device cpu' 来实现。

Transferred 738/745 items from yolov5x.pt AMP: checks passed optimizer: SGD(lr=0.01) with parameter groups 123 weight(decay=0.0), 126 weight(decay=0.0005), 126 bias Traceback (most recent call last): File "C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\utils\dataloaders.py", line 481, in __init__ assert self.im_files, f'{prefix}No images found' AssertionError: train: No images found The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\train.py", line 648, in <module> main(opt) File "C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\train.py", line 537, in main train(opt.hyp, opt, device, callbacks) File "C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\train.py", line 196, in train train_loader, dataset = create_dataloader(train_path, File "C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\utils\dataloaders.py", line 124, in create_dataloader dataset = LoadImagesAndLabels( File "C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\utils\dataloaders.py", line 483, in __init__ raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e Exception: train: Error loading data from C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\wzry\datasets\labels\train: train: No images found See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data

根据错误信息显示，训练过程中出现了找不到图像的问题。请确保在指定的路径下存在训练图像。您可以检查以下几点：

1. 检查训练图像的路径是否正确：C:\Users\admin\Desktop\yolov5-master (1)\yolov5-master\wzry\datasets\labels\train
2. 确保在该路径下存在图像文件。您可以检查一下该路径下是否有图像文件。

如果路径和文件都正确，但仍然出现问题，请确保图像文件格式正确，并且每个图像都有对应的标签文件。此外，还可以检查一下图像和标签文件的命名是否匹配。

如果问题仍然存在，请提供更多的信息和代码上下文，以便更好地帮助您解决问题。