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Understanding fpn.py with picture

picture1

I recently got into machine learning study. Your code, fpn.py, helped me understand feature pyramid networks. When I studyed fpn.py, I was drawing network structure. Since I also want to help others figure out the code, I share my note.

像素维度问题

每一层的维度都不一样,那么最后是如何进行相加的呢?

fpn structure doubt

in fpn.py file.

# Top-down
        p5 = self.toplayer(c5)
        p4 = self._upsample_add(p5, self.latlayer1(c4))
        p3 = self._upsample_add(p4, self.latlayer2(c3))
        p2 = self._upsample_add(p3, self.latlayer3(c2))
        # Smooth
        p4 = self.smooth1(p4)
        p3 = self.smooth2(p3)
        p2 = self.smooth3(p2)

should be

# Top-down
        p5 = self.toplayer(c5)
        p4 = self._upsample_add(p5, self.latlayer1(c4))
        p4 = self.smooth1(p4)
        p3 = self._upsample_add(p4, self.latlayer2(c3))
        p3 = self.smooth2(p3)
        p2 = self._upsample_add(p3, self.latlayer3(c2))
        p2 = self.smooth3(p2)
        # Smooth

?

About evaluate

Hey, so lucky to find someone doing the same thing as me! Have u trained and evaluated the network?

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