Simple question about prediction classes of item in question vs not item in question 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?
It depends if the percentages are required to sum to 100%. Typically when training on only 2 classes, the model will make predicts that sum to 100% for 2 classes. There will be no chance for out-of-class predictions.