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I was working on segmentation using unet, its a multiclass segmentation problem with 21 classes.

Thus Ideally we go with softmax as activation in the last layer, which contains 21 kernels so that output depth will be 21 which will match the number of classes.

But my question is if we use 'Softmax' as activation in this layer how will it work? I mean since softmax will be applied to each feature map and by the nature of 'softmax' it will give probabilities that sum to 1. But we need 1's in all places where the corresponding class is present in the feature map.

Or is the 'softmax' applied depth wise like taking all 21 class pixels in depth and applied on top of it?

Hope I have explained the problem properly

I have tried with sigmoid as activation, and the result is not good.

JAGADEESHA R G
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If I understand correctly, you have 21 kernels that are of some shape m*n. So if you reshape your final layer to have a shape of (batch_size, 21, (m*n)), then you can apply softmax long the first dimension (21). Then every value within a single kernel should be the same, and you can take the kernel with the max value.

In this case, you'll find the feature map that has the best overall overlap with the region of interest, rather than finding which part of every feature map overlaps with the ROI if any.

Sean
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