Multi-crop at test time is a form of data augmentation that a model uses during test time, as opposed to most data augmentation techniques that run during training time.
Broadly, the technique involves:
- cropping a test image in multiple ways
- using the model to classify these cropped variants of the test image
- averaging the results of the model’s many predictions
Multi-crop at test time is a technique that some machine learning researchers use to improve accuracy at test time. The technique found popularity among some competitors in the ImageNet Large Scale Visual Recognition Competition after the famous AlexNet paper, titled ImageNet Classification with Deep Convolutional Neural Networks, used the technique.