CrossEntropyLoss after Sigmoid on class predictions about pytorch-yolov3 HOT 3 CLOSED

Optimizing the network to minimize the crossentropy loss means minimizing the distance between ground truth labels (1 for index of class and 0 for the others) and the prediction of the network. In the case where a sigmoid activation function is applied the output will be between 0 and 1, but the network is not constrained so that the outputs sum to one, as is the case when a softmax activation function is used. The softmax activation function is normally applied together with the crossentropy loss as this essentially means that the network will be trained to maximize the output where the ground truth label is 1 and minimize all others. The fact that the difference is only one in this case is not really an issue. However, in this case the authors argued that it makes more sense to use a sigmoid activation function (instead of softmax) since the class labels for an object does not necessarily need to be mutually exclusive, e.g. a labrador should be able to be labeled as both a dog and a labrador. In this case it makes more sense to use a binary crossentropy loss, since this loss does not assume that the outputs should sum to one.

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Right now, I just came across the commit where you have changed the CrossEntropyLoss, sorry for not looking before opening the issue.

Nevertheless, was this the reason for the change?

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Thanks for the awesome repo. For my dataset all the objects are exclusive so I think using softmax will be more efficient. Can you suggest where should i make changes to use softmax classifier instead of sigmoid for multi-class classifier.

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