Vision Transformers (ViT)

Summary

Applying the Transformer architecture to images by splitting them into patches treated like tokens — now competitive with or better than CNNs on many vision benchmarks.

• 1Read how ViT splits an image into patches and embeds them like sequence tokens.
• 2Fine-tune a pretrained ViT on a small image classification dataset.
• 3Compare ViT and CNN performance/data requirements at a conceptual level.