Classification Loss: CE vs BCE

Created by: glenn-jocher

When developing the training code I found that replacing Binary Cross Entropy (BCE) loss with Cross Entropy (CE) loss significantly improves Precision, Recall and mAP. All show about 2X improvements using CE, though the YOLOv3 paper states these loss terms as BCE in darknet.

The two loss terms are on lines 162 and 163 of models.py. If anyone has any insight into this phenomenon I'd be very interested to hear it. For now you can swap the two back and forth. Note that SGD does not converge using either BCE or CE, so that issue appears independent of this one.