I’m working on a project about fractures. I trained a model for fracture types. The classes I want to look for are offset blunt fracture (pink box) and displaced fracture (blue box). The problem with the displaced fracture is that there are few images with that fracture, and I got low stats for the validation and test set . But both can be interpreted as displaced blunt fractures; there’s also a class that is a displaced point fracture. You can see in the images that the patterns for the displaced fracture vary a lot. So, would the detection probability increase if I put everything on the offset blunt fracture, since there are more images available? Because from what I see, to detect an object, the models have to find similar patterns in the pixels, so I think putting everything in one label won’t change much.
