Let's say I wanted to use transfer learning to train a model to detect object A vs everything else. In this case, do I provide 2 types of input, images of object A and images of everything else, and then have the final layer of the model output either object A or not-object A?
What about in the case where I want object A vs object B vs everything else. Would it make sense in this case to provide images of A and B and then have only two output classes, but based on the confidence of the output, interpret it as 3 classes? Say that it's object A if the confidence in that is > 50%, object B if the confidence in that is > 50%, and anything else if neither of those two conditions are met?