The background and object can be anything. For example, assume I have 1000 pictures with a (fixed) forest background and a (fixed) cat image at different positions on each of these 1000 images. I want a NN able to output 2 numbers that are related to the real x and y of the cat position. By this I mean applying a (possibly highly non-linear) transformation on the 2 numbers output for each image, I can get the actual x and y of the cat. Of course I need the same transformation for each image and also that transformation function might be highly complicated, but what I want is an output that somehow stores the actual position of the cat. For example if the NN outputs for each image instead of (x,y), (log(sqrt(x)),atan(exp(y)) it’s fine as the functions are invertible. However I can’t hardcode this by hand, as later I might have another dataset (say dog on a bedroom background). So a good NN shouldn’t care too much about the object and background (assuming they are reasonable). Of course you would need to retrain it for each case, but for the purpose of my project, I don’t need a validation set. I just want the NN to discover the x and y on it’s own for the training data and that’s it (so basically highly overfitting would be ideal in this case). Thank you!