oscarknagg/few-shot

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1284 stars · 244 forks observed · 2026-08-28

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

A PyTorch repository with clean, tested implementations that reproduce few-shot learning and meta-learning research papers such as Prototypical Networks and MAML. It includes dataset preparation scripts for Omniglot and miniImageNet and experiment scripts matching published benchmark results.

Use cases

• reproduce few-shot learning paper results in pytorch
• learn how prototypical networks work
• implement MAML meta-learning
• run experiments on omniglot and miniimagenet
• study clean implementations of meta-learning algorithms
• benchmark n-way k-shot classification models

When to choose

• you want readable, tested reference implementations of few-shot learning papers
• you need reproducible Prototypical Networks or MAML baselines
• you are learning meta-learning concepts with working code

When to avoid

• you need production-ready few-shot learning pipelines
• you want the latest few-shot learning methods or active maintenance
• you need a high-level framework rather than research code

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python few-shot-learning meta-learning pytorch maml prototypical-networks omniglot miniimagenet research-reproduction research gpu linux macos

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