yolov7的v5 metric

关于yolov7的v5 metric，根据提供的引用内容中没有具体描述yolov7的v5 metric的信息。在给出确切答案之前，还需要提供关于yolov7的v5 metric的更多信息。<em>1</em><em>2</em><em>3</em>
#### 引用[.reference_title]
- *1* *2* *3* [Yolov7训练自己的数据-水果检测](https://blog.csdn.net/m0_67089871/article/details/128686711)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}} ] [.reference_item]
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相关问题

yolov7 thop is not satisfied

As an AI language model, I cannot provide a personal opinion on whether YOLOv7 THOP is satisfactory or not. However, I can provide information on YOLOv7 THOP.

YOLOv7 THOP is a version of the popular object detection algorithm, YOLO (You Only Look Once), that is optimized for deployment on mobile devices and embedded systems. THOP stands for Total Hang Overhead of Parameters, which is a metric used to measure the computational efficiency of a neural network.

While YOLOv7 THOP has been shown to have good performance on certain benchmarks, its suitability for a particular application depends on various factors such as the hardware platform, the size of the input images, and the specific use case. Therefore, it is necessary to evaluate the performance of YOLOv7 THOP in the context of the application it is intended for.

Traceback (most recent call last): File "test.py", line 345, in <module> v5_metric=opt.v5_metric File "test.py", line 107, in test for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): File "C:\Users\Dell\.conda\envs\sparsercnn\lib\site-packages\tqdm\std.py", line 1195, in __iter__ for obj in iterable: File "F:\git\yolov7-main\yolov7-main\utils\datasets.py", line 109, in __iter__ yield next(self.iterator) File "C:\Users\Dell\.conda\envs\sparsercnn\lib\site-packages\torch\utils\data\dataloader.py", line 628, in __next__ data = self._next_data() File "C:\Users\Dell\.conda\envs\sparsercnn\lib\site-packages\torch\utils\data\dataloader.py", line 1333, in _next_data return self._process_data(data) File "C:\Users\Dell\.conda\envs\sparsercnn\lib\site-packages\torch\utils\data\dataloader.py", line 1359, in _process_data data.reraise() File "C:\Users\Dell\.conda\envs\sparsercnn\lib\site-packages\torch\_utils.py", line 543, in reraise raise exception RuntimeError: Caught RuntimeError in DataLoader worker process 1. Original Traceback (most recent call last): File "C:\Users\Dell\.conda\envs\sparsercnn\lib\site-packages\torch\utils\data\_utils\worker.py", line 302, in _worker_loop data = fetcher.fetch(index) File "C:\Users\Dell\.conda\envs\sparsercnn\lib\site-packages\torch\utils\data\_utils\fetch.py", line 61, in fetch return self.collate_fn(data) File "F:\git\yolov7-main\yolov7-main\utils\datasets.py", line 434, in collate_fn return torch.stack(img, 0), torch.cat(label, 0), path, shapes RuntimeError: stack expects each tensor to be equal size, but got [1539, 448, 672] at entry 0 and [12, 448, 672] at entry 1

这个错误的原因是在数据加载过程中，有些数据的尺寸大小不一致，而在使用 `torch.stack` 进行堆叠操作时，要求每个 tensor 的大小必须相同，因此出现了这个错误。你需要检查一下数据集中的数据，找出这些尺寸大小不一致的数据，并将其进行处理，使其大小与其他数据一致。可以使用 OpenCV 或者 Pillow 这样的库来调整图像的大小。