jakesnell/prototypical-networks

Health v2 · maintenance only

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1235 stars · 268 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference PyTorch implementation of Prototypical Networks for few-shot classification from the NeurIPS 2017 paper. It includes training and evaluation scripts for the Omniglot dataset.

Use cases

• reproduce prototypical networks results from the NeurIPS 2017 paper
• train a few-shot image classification model on Omniglot
• learn how metric-based few-shot learning works in PyTorch
• benchmark few-shot learning algorithms
• adapt prototypical networks to a custom few-shot dataset

When to choose

• you need the official reference implementation of Prototypical Networks
• you want a simple, well-cited baseline for few-shot learning research
• you are working with Omniglot-style few-shot classification tasks

When to avoid

• you need a maintained library with recent PyTorch support - the code targets PyTorch 0.4 and Python 3.6
• you want production-ready few-shot learning tooling rather than research code
• you need few-shot learning for NLP or other modalities out of the box

Facets

library · maturity maintenance

deep-learning machine-learning nlp machine-learning deep-learning artificial-intelligence python few-shot-learning metric-learning pytorch research-code omniglot prototypical-networks linux gpu

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