Update - Earlier when I worked on QAT, I was using wrong config. After correcting it, the QAT helps improve the accuracy. However I am still interested in knowing (in case of static quantization for qnnpack config)  why the output value for all pixels is positive for background channel and negative for foreground channel.
The model I am using is available here:
source - https://github.com/thuyngch/Human-Segmentation-PyTorch 15
model file - https://drive.google.com/file/d/17GZLCi_FHhWo4E4wPobbLAQdBZrlqVnF/view 3