While reading the semantic segmentation papers as well as their corresponding implementations, I found that some approaches use softmax while others use sigmoid for the pixel-level labeling.
For instance, with respect to u-net paper, the output is a feature map with two channels.
I have seen some implementations using softmax over these two channel outputs. I am not sure whether my following understanding is correct or not?
For illustration purposes, the masked portion belongs to class 1 and the other part belongs to class 2. I only assume two class: masked or non-masked.
xy to represent the output map with shape (1, image_row,image_col,2). Then,
xy[1,0,0,0] will represent the probability of pixel at (0,0) belonging to class 1 while
xy[1,0,0,1] will represent the probability of pixel (0,0) belonging to class 2. In other words,
Is my understanding correct?