YOLOV5安装步骤（一）

1.首先在github上下载模型

Release v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference · ultralytics/yolov5 · GitHubYOLOv5 in PyTorch > ONNX > CoreML > TFLite. Contribute to ultralytics/yolov5 development by creating an account on GitHub.https://github.com/ultralytics/yolov5/releases/tag/v6.1

2.下载好后打开然后安装依赖

pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com

默默等待安装完成

3.运行下detect.py文件观察是否缺少某个依赖

你可能发现安装了和没安装一样这时候你就需要做的下一步

4.安装好后一定要更换解释器，选择configure配置后，更换有yolov5-6.1的python解释器，最后

5.执行YOLOv5时报错，解决：AttributeError: ‘Upsample‘ object has no attribute ‘recompute_scale_factor

参考执行YOLOv5时报错，解决：AttributeError: ‘Upsample‘ object has no attribute ‘recompute_scale_factor‘_C++有手就行的博客-CSDN博客6.runtimeerror

解决方法：找到5.0版报错的loss.py中最后那段for函数，将其整体替换为yolov5-master版中loss.py最后一段for函数即可正常运行

        for i in range(self.nl):
            anchors, shape = self.anchors[i], p[i].shape
            gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]]  # xyxy gain
 
            # Match targets to anchors
            t = targets * gain  # shape(3,n,7)
            if nt:
                # Matches
                r = t[..., 4:6] / anchors[:, None]  # wh ratio
                j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']  # compare
                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                t = t[j]  # filter
 
                # Offsets
                gxy = t[:, 2:4]  # grid xy
                gxi = gain[[2, 3]] - gxy  # inverse
                j, k = ((gxy % 1 < g) & (gxy > 1)).T
                l, m = ((gxi % 1 < g) & (gxi > 1)).T
                j = torch.stack((torch.ones_like(j), j, k, l, m))
                t = t.repeat((5, 1, 1))[j]
                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
            else:
                t = targets[0]
                offsets = 0
 
            # Define
            bc, gxy, gwh, a = t.chunk(4, 1)  # (image, class), grid xy, grid wh, anchors
            a, (b, c) = a.long().view(-1), bc.long().T  # anchors, image, class
            gij = (gxy - offsets).long()
            gi, gj = gij.T  # grid indices
 
            # Append
            indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1)))  # image, anchor, grid
            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
            anch.append(anchors[a])  # anchors
            tcls.append(c)  # class

记住，不要把return也弄没了