openai/supervised-reptile

Health v2 · maintenance only

Flags: no_releases archived

Adoption not part of the score

1044 stars · 209 forks observed · 2026-08-28

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

Official research code for the Reptile meta-learning algorithm from the paper 'On First-Order Meta-Learning Algorithms'. It trains and evaluates few-shot classification models on Omniglot and Mini-ImageNet benchmarks.

Use cases

• reproduce Reptile few-shot classification results
• run meta-learning experiments on Omniglot
• train 1-shot and 5-way Mini-ImageNet models
• learn how first-order meta-learning works
• compare MAML-style algorithms on few-shot benchmarks
• download and prepare Omniglot and Mini-ImageNet datasets

When to choose

• you want to reproduce or study the Reptile paper's experiments
• you need a reference implementation of first-order meta-learning
• you're doing research on few-shot classification benchmarks

When to avoid

• you need a maintained, production-ready meta-learning library
• you want modern framework support or GPU ecosystem updates
• you need anything beyond Omniglot or Mini-ImageNet benchmarks

Facets

library · maturity abandoned

machine-learning deep-learning machine-learning artificial-intelligence python meta-learning reptile few-shot-learning research-code omniglot mini-imagenet tensorflow archived research linux macos

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For agents

markdown · JSON · MCP: product_card(name="openai/supervised-reptile")