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I don't understand about the bin size of the pyramid pooling module (11, 22, 33, 66) in the paper. Does it mean that, for instance of bin size 3*3, the width and height of each feature map after pooling are both 3? If yes, each feature map is square? Thx.
The text was updated successfully, but these errors were encountered:
Yes, for the original design is trained with a square input(like 473*473), so in the ppm the pooled ones are all squared maps.
Let's say your crop size of the input data is c, then it should be a number that can fit equation c = 8x+1;
2 Then your size in conv5_3 denotes as w = x + 1;
In each pool level L(1,2,3,6), assume the kernel size is k, and stride is s, and k>=s, say k = s+a;
In level 1, w = s+a;
In level 2, w = 2s+a;
In level 3, w = 3s+a;
In level 6, w = 6s+a;
So your s and k in level L should be s=[w/L], k=s+w%L. Also, you can modify the pool layer and interp layer to do automatic calculation.
Hi,
I don't understand about the bin size of the pyramid pooling module (11, 22, 33, 66) in the paper. Does it mean that, for instance of bin size 3*3, the width and height of each feature map after pooling are both 3? If yes, each feature map is square? Thx.
The text was updated successfully, but these errors were encountered: