优秀个人网页设计案例分析seo知识是什么意思

  小白刚开始学习YOLOv5，跟随老哥的步骤走了一遍目标检测--手把手教你搭建自己的YOLOv5目标检测平台

  最后训练最后一步出现RuntimeError: result type Float can‘t be cast to the desired output type __int64报错

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].shapegain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]]  # xyxy gain# Match targets to anchorst = targets * gain  # shape(3,n,7)if nt:# Matchesr = t[..., 4:6] / anchors[:, None]  # wh ratioj = 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# Offsetsgxy = t[:, 2:4]  # grid xygxi = gain[[2, 3]] - gxy  # inversej, k = ((gxy % 1 < g) & (gxy > 1)).Tl, m = ((gxi % 1 < g) & (gxi > 1)).Tj = 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# Definebc, gxy, gwh, a = t.chunk(4, 1)  # (image, class), grid xy, grid wh, anchorsa, (b, c) = a.long().view(-1), bc.long().T  # anchors, image, classgij = (gxy - offsets).long()gi, gj = gij.T  # grid indices# Appendindices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1)))  # image, anchor, gridtbox.append(torch.cat((gxy - gij, gwh), 1))  # boxanch.append(anchors[a])  # anchorstcls.append(c)  # class