I have 3 questions in mind about the neural network
- For the best model performance, is it better to train a model only on high resolution images or does it not matter whether the training data includes high-resolution and low-resolution images.
- Let us say I would like a model that can detect cats and there are 10 cats I am interested, again, in the best performance. Would it be better if I had 10 classes for each cat or just one class for cats (like just cat) or does it not matter?
- Why might a model trained on 2D images not work well when validated by 360 images?