Multi-Crop Training is a data augmentation strategy in self-supervised learning where multiple crops of different sizes are extracted from each image — typically 2 large global crops (covering 50-100% of the image) and several small local crops (covering 5-20%), both processing through the network.

How Does Multi-Crop Work?

• Global Crops (2): 224×224, covering most of the image. Processed by both student and teacher networks.
• Local Crops (6-8): 96×96, small patches. Processed only by the student network.
• Training Signal: Student must match teacher's representation of global crops using both local and global crops.
• Introduced By: SwAV, later adopted by DINO and DINOv2.

Why It Matters

• Local-Global Correspondence: Forces the model to learn that local patches contain information about the whole image.
• Efficiency: Small crops are cheap to process, adding many training signals with little compute overhead.
• Performance: Multi-crop consistently provides 1-2% accuracy improvement over standard 2-crop training.

Multi-Crop Training is seeing the forest from the trees — training models to understand global image semantics from small local patches.