AI Engineering/Phase 04/Lesson 20

Image Retrieval & Metric Learning

A retrieval system ranks candidates by a distance in embedding space. Metric learning is the discipline of shaping that space so the distances mean what you want.

Build 45 min Python

Prerequisites: Phase 4 Lesson 14 (ViT), Phase 4 Lesson 18 (CLIP)

#Learning Objectives

Explain triplet, contrastive, and proxy-based metric learning losses and pick the right one for a given dataset
Implement L2-normalisation and cosine similarity correctly and audit the difference between "same item" and "same class" retrieval
Build a FAISS index, query it by text and by image, and report recall@K for a held-out query set
Use DINOv2, CLIP, and SigLIP as off-the-shelf embedding backbones and know when each wins

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