Multi_label will cause problem in non_max_suppression_face (Now have one solution)

[ChangenicRM]

In general.py, when calculating the non_max_suppression_face

Detections matrix nx6 (xyxy, conf, landmarks, cls)

if multi_label: i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15], j[:, None].float()), 1) else: # best class only conf, j = x[:, 15:].max(1, keepdim=True) x = torch.cat((box, conf, x[:, 5:15], j.float()), 1)[conf.view(-1) > conf_thres]

if nc>1, then multi_label is true. So when I trained more than 20 epochs then begin to test the model (Need to calculate the NMS), at "x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15] ,j[:, None].float()), 1)", to concatenate those tensor, the shape of x[:, 5:15] will not satisfy the limit of torch.cat() I guess that because of "i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T" (To select some tensors which are higher than the conf_thres) lead this condition different from the one-class case. So my solution is to change x[:, 5:15] to x[i, 5:15] to shape the dimensions of those tensors to the same. (maybe this is a little mistake of the author) But I am not sure if it is wrong.

[iamdoubleawesome]

I did the same

[litao-zhx]

你好，请问多目标检测时，需要修改那些地方呢？我在改为检测3类目标时，出现如下错误 不知道各位遇到过没？

[gatzf]

Hello, I ran into some problems when modifying the category number of the program. The program could not carry backward, lcls += BCEcls(ps[:, 15:], t), there is something wrong with this statement

Do you have any good suggestions, thank you very much

[yychentw]

你好，请问多目标检测时，需要修改那些地方呢？我在改为检测3类目标时，出现如下错误 不知道各位遇到过没？

Hi @litao-zhx , I faced the same issue while training YOLOv5-face for multi-class object detection. The problem arises because the optimizer loads the state_dict from a previous training session on a single-class detector, causing a mismatch in tensor sizes between the optimizer state tensors and the model's parameter tensors.

You can check if you are experiencing a similar situation.