AI Engineering/Phase 19/Lesson 58

Vision Encoder Patches

A vision model that reads pixels needs a tokenizer for pixels. Patch embedding is that tokenizer. Cut the image into a grid of squares, flatten each square, project it through one linear layer, then add a 2D position signal so the transformer knows where each square sat in the original image.

Build 1.5 hr Python

Prerequisites: Phase 19 lessons 30-37 (Track B foundations)

#Learning Objectives

Tokenize an image into a fixed-length sequence of patch embeddings.
Implement a Conv2d-based patch projection that matches the math of unfold-then-linear.
Build a deterministic 2D sinusoidal position embedding so token order encodes spatial position.
Verify patch count, embedding shape, and Conv2d/unfold equivalence on a synthetic fixture.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.