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dangweili / pedestrian-attribute-recognition-pytorch

A simple baseline for pedestrian attribute recognition in surveillance scenarios
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output attribute meaning #9

Closed lynnw123 closed 5 years ago

lynnw123 commented 5 years ago

Hello, thanks for sharing your great work! Could you help me understand the following sentence in the paper, "the DeepMAR output a label vector which represents whether it has each attribute or not"? For example, in the case of the age prediction, PETA dataset which had four attributes 16-30, 31-45, 45-60 and >60 in your selected 35 attributes, If more than 1 output has a positive score, (eg. first twos) it means the image has both 16-30 and 31-45 attribute? or we need sort above four output scores and vote the biggest positive number as the age prediction? I noticed one more attribute <16 later in the PETA datasets. have you performed some experiments about this? thanks again!

lynnw123 commented 5 years ago

To my first question, I think to vote the max number as the age prediction is a better way. I am wondering is there a way that I could add some softmax above certain attributes, like group softmax in PyTorch?

dangweili commented 5 years ago

@YL-123 I treat the attribute classification as a multi-label classification problem, which is a simple way. As you said, different attributes may be conflict with each other, e.g. age16-30 and age13-45, thus using softmax on these mutually exclusive attributes would be better where the ambiguity would be decreased.