Suppose I have a dataset that contains ten kinds of animals, such as dog , cat , etc, each with 1000 pictures. It is a goot dataset and each class has the same number of samles. However, I am not going to do a ten-class animals classification task. But to do a two-class classfification task. For example, I want to train a model to classify DOG and Not DOG. I put the pictures of DOG into one class, and the pictures of Not DOG  into another class. This becomes a data imbalance problem. My question is， is it necessary for me to up-sample the DOG pictures to make the samples balanced?