HumanRefiner
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About BCEloss with abnormal score
I have know that we can calculate the abnormal scores by the number of abnormal blocks. But I notice that a sigmoid layer and binary cross-entropy been used in the model. Is there any dismatch between the linear score and a binary loss?
Thanks for your reply!
The paper states that "Images assigned with abnormal labels are positive samples while normal ones are negative samples.", and the score is in range of [0, 1]. So, it's a binary classification task in fact. But the author said that the number of abnormal bounding boxes as the score, I feel confused ...