Pete Warden shares his rule of thumb for how many images you need to train a neural network:
In the early days I would reply with the technically most correct, but also useless answer of “it depends”, but over the last couple of years I’ve realized that just having a very approximate rule of thumb is useful, so here it is for posterity:
You need 1,000 representative images for each class.
Like all models, this rule is wrong but sometimes useful. In the rest of this post I’ll cover where it came from, why it’s wrong, and what it’s still good for.
Read on to learn where the number 1000 came from and get some good hints, like flipping and rescaling images.