Top-1 accuracy is the conventional accuracy: the model answer (the one with highest probability) must be exactly the expected answer.
Top-5 accuracy means that any of your model 5 highest probability answers must match the expected answer.
For instance, let’s say you’re applying machine learning to object recognition using a neural network. A picture of a cat is shown, and these are the outputs of your neural network:
- Tiger: 0.4
- Dog: 0.3
- Cat: 0.1
- Lynx: 0.09
- Lion: 0.08
- Bird: 0.02
- Bear: 0.01
Using top-1 accuracy, you count this output as wrong, because it predicted a tiger.
Using top-5 accuracy, you count this output as correct, because cat is among the top-5 guesses.