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. 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. Retrieval is everywhere in production vision: duplicate detection, reverse image search, visual search ("find similar products"), face re-identification, person re-ID for surveillance, instance-level matching for e-commerce. The product question is always the same: "given this query image, rank my catalogue." Two design decisions shape the whole system. The embedding — what model produces the vectors. The index — how to find nearest neighbours at scale. Both are commodity in 2026 (DINOv2 for the embedding, FAISS for the index), which raises the bar: the hard part is defining what counts as similar for your application, then shaping the embedding space so the distances match. That shaping is metric learning. It is a small but high-leverage discipline. For most production use cases, start with a pretrained backbone and…
Image Retrieval & Metric Learning: A retrieval system ranks candidates by a distance in embedding space. Metric learning is the discipline of shaping that…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.