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.
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.