The authors of the original paper of Faster R-CNN when they refer to the positive anchors, they are labeled as 1. I guess they refer in binary classification. What happens in the case in a task we have more than two classes?I mean the purpose of the RPN is to generate just region proposals (there is an object or not, regardless the label) or it generates region proposals and the respective label as well? I guess it is the second case, since the positive anchors that participate in the training process need to have a ground truth label as well, i.e. (cat, dog, e.t.c).However, I might misunderstand things and interpret things in a wrong way.
Faster R-CNN: Labels regarding the positive anchors when there are many classes
machine learning;deep learning;image recognition;object recognition