I have an idea but I am not certain that it can be modeled in a DL architecture.
Let's say we have images of different qualities based on color patterns and their assessment as labels in a range from 0-1. E.g. Image 1 has 0.25 quality, Image 2 has 0.5 quality and so on.
Could this be implemented in a standard Resnet50 architecture with 1 output and sigmoid? Is there anything in literature that you could point me up to? I wasn't able to find anything, maybe I am searching it wrong.
EDIT: I have found this https://github.com/idealo/image-quality-assessment, but the implementation is different that I suggest. I know that this can work if I add five different quality classes.
I want to know if I can train this with 1 class output with a rank, in order to let the model understand how those qualities are linked. For instance quality 2 is the next best quality than quality 1.