ViT, read beside the paper

The paper and its explanation side by side.

• Part 1: The Big Idea: An Image Is Worth 16×16 Words

  The title, the abstract and the introduction of the Vision Transformer paper, line by line: what it means to cut a picture into 16×16 patches and read them like words, why convolutional networks ruled computer vision, what inductive bias, locality and translation equivariance are, and the claim that enough data beats built-in assumptions. With a real run of ViT-B/16 and ResNet-50 on the same picture and the quadratic cost of attention worked out by hand.

• Part 2: Inside ViT: Patches, Embeddings and the Encoder

  Section 2, Figure 1, Section 3.1 and Appendix A, line by line: the earlier attempts to put attention on images, then every step from a 224×224 picture to a class label, with Equations 1 to 8 recomputed by hand on the released ViT-B/16 and matched against the library at every stage, the 86,567,656 parameters counted exactly, and a four-token attention example small enough to check with a pencil.