In my problem, there are about 5,000 training images and there are about 50~100 objects of identical type (or class) on average, per image. And for each training images, there is a partial mask information that denotes the polygon vertices of objects, but the problem is there are only 3 ~ 5 objects per image with mask/annotation information.
So in summary there is 1 class, 5,000 * 50 ~ 5,000 * 100 instances of the class, and 5,000 * 3 ~ 5,000 * 5 instances with masking information.
So not a single training data image has a full masking information, and yet all the training data images have partial masking information. My job is to make instance segmentation model.
I did some search on semi-supervised segmentation, and to my understanding it seems like the papers are tackling problems where some training images have all the objects annotated while the other training images have 0 objects with annotation. That isn't exactly my situation. How should I approach this problem? Any tips are appreciated.
