Phase 2: Object Detection & Segmentation · ~45 minutes · Python

Vision Transformers (ViT)

Cut the image into patches, treat each patch as a word, run a standard transformer. Don't look back.

What you will learn

Implement patch embedding, learned positional embedding, class token, and transformer encoder blocks from scratch to build a minimal ViT
Explain why ViT was thought to need massive pretraining data until DeiT and MAE proved otherwise
Compare ViT, Swin, and ConvNeXt on their architectural priors (none, local window attention, conv backbone)
Fine-tune a pretrained ViT on a small dataset using `timm` and the standard linear-probe / fine-tune recipe

Introduction

Type: Build
Languages: Python
Prerequisites: the Transformers & LLMs from Scratch course, Image Classification
Time: ~45 minutes

Objective

Learning objectives

Implement patch embedding, learned positional embedding, class token, and transformer encoder blocks from scratch to build a minimal ViT
Explain why ViT was thought to need massive pretraining data until DeiT and MAE proved otherwise
Compare ViT, Swin, and ConvNeXt on their architectural priors (none, local window attention, conv backbone)
Fine-tune a pretrained ViT on a small dataset using timm and the standard linear-probe / fine-tune recipe

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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