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.
Hiring signal: Understanding of vision transformers (vit) internals
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: Phase 7 Lesson 02 (Self-Attention), Phase 4 Lesson 04 (Image Classification) Time: ~45 minutes
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 It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
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