Hi ,
I have a binary segmentation problem. Where the label/target tensor is a simple binary mask where the background is represented by 0 and the foreground (object I want to segment) by 1.
I read that for such problems people have gotten great results using a single channel output, so the output from my U-Net network is of the shape [1,1,30,256,256]. Similarly, the target tensor has the same shape.
The objects I want to segment are very small so in every sample I have a lot of background but only a few foreground pixels. And I was wondering if applying weights to this would help.
I know that when I have multiple objects to segment such as if I had three classes [0,1,2] then I could apply weights like: [0.2,0.2,0.6]. But how to do the same when I have only 1 class?
Is the network considering the background as a class even though I chose to output a single channel? or is the background being ignored. If the background is not being ignored then how can I know how to set the weights? Would it be [0.2, 0.8] or [0.8. 0.2]?
Thank you