@ptrblck  Thank you so much for your reply! it is very helpful since it answers the main question I had in terms of normalization and its bounds.
I’ll try to describe my project better to see if you have any additional professional advise about which methods to use for this problem from your experience.
I have 30,000+ images (256x256) of circular particles in space. I have already collected their ground truth using conventional computer vision segmentation which also outputs radius, perimeter, and x-y locations of each particle; it is very slow, therefore machine learning could be an attractive approach. My initial task which I have already done successfully was to implement a simpler version of U-Net to generate the same segmented (ground truth) figure. The results were great, nearly a perfect segmentation as well as improving the speed of conventional computer vision methods by a substantial amount.
Now I am trying to do the second portion which is to predict the radius, and x-y locations as well. I have tried using convolutional neural nets, even using transfer learning from pre-trained models but the results are quite undesirable.
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My first thought as to what is causing this is that since some images have more particles than others, in order to make the output vector of x-y locations and radius a fixed size (101 each x, y and radius so 303 total), some outputs will contain values of zeros. In other words I made the output vector size fixed at 101 for x,y,radius but some images might only have 51 particles which means 50 (50*3 = 150 out of 303) will be 0. My second thought regarding the source of error is the sparsity on the x-y locations; being scattered all over the image.
Is there an approach you would advise on using that could be better for this specific problem? I am thinking of trying the YOLO network today by creating bounding boxes, however, can the YOLO network work with only 1 class (particle or no particle, where some even overlap). Please, if you think there is a different route I should take I would really appreciate your advise