KevinMusgrave/pytorch-metric-learning

Health v2 · maintenance only

Adoption not part of the score

6342 stars · 660 forks observed · 2026-10-19

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch library providing modular losses, miners, samplers, and testers for deep metric learning. It supports building complete train/test workflows for learning embeddings, with composable components usable independently.

Use cases

• train a model to learn image embeddings with triplet loss
• implement contrastive learning in PyTorch
• mine hard negative pairs for metric learning
• build an image retrieval system with deep embeddings
• train face recognition embeddings with ArcFace loss
• run self-supervised metric learning experiments
• evaluate embedding quality with retrieval metrics

When to choose

• you need metric learning losses and miners in a PyTorch codebase
• you want a complete, modular train/test workflow for embedding learning
• you need well-tested implementations of losses like ArcFace, TripletMarginLoss, or MultiSimilarityLoss

When to avoid

• you use TensorFlow, JAX, or another framework instead of PyTorch
• you only need off-the-shelf pretrained embeddings without training
• your task is standard classification rather than embedding learning

Facets

library · maturity active

machine-learning deep-learning computer-vision machine-learning deep-learning computer-vision image-processing python cross-platform metric-learning pytorch contrastive-learning embeddings triplet-loss image-retrieval self-supervised-learning loss-functions hard-negative-mining gpu

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