I'm working on a research problem where I need to perform classification for coarse prediction in a feature space and then fine grained regression for getting more precise values. I know that this way of regression should work. I also will essentially deal with feature maps.
I am thinking of using a 'stacked hourglass network'. Do I need to identify this by sheer experimentation or can someone intuitively remove some possibilities saying a particular architecture may not be suitable for my problem.
I found stacked hourglass network to upscale and downscale essentially the heatmaps but now am confused with changing the model for sequential classification and regression task. Any clues would be welcomed.