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I've just found that the code is not doing the same way as the paper said.
For instance, The Smooth l1 Loss of Key-point Locations is not the same. In the paper, only the m predicted labels contribute to the loss. In the code, since the proj_label are the set to be all zeros besides 9 * m locations around m key points, every location will contribute to the loss.
Can anyone explain this for me?
The text was updated successfully, but these errors were encountered:
@tensorboy No, I still don't quite understand the paper. I mean I don't quite understand how the labels are formed, how the losses are computed and how the proposals are generated. Maybe you've understood the paper?
Hi, thanks for the work.
I've just found that the code is not doing the same way as the paper said.
For instance, The Smooth l1 Loss of Key-point Locations is not the same. In the paper, only the m predicted labels contribute to the loss. In the code, since the proj_label are the set to be all zeros besides 9 * m locations around m key points, every location will contribute to the loss.
Can anyone explain this for me?
The text was updated successfully, but these errors were encountered: