# dragen1860/MAML-Pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

Repository: https://github.com/dragen1860/MAML-Pytorch
Canonical: https://ross.abutalabs.com/products/maml-pytorch
Language: Python
License: MIT
License Family: permissive
Last push: 2019-05-16T12:39:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3135, "days_push": 2666, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2483, forks 437 (observed 2026-08-28T04:06:55.445047+00:00)

## What it is
A PyTorch implementation of Model-Agnostic Meta-Learning (MAML) for few-shot supervised learning, supporting MiniImagenet and Omniglot benchmarks. It serves as a starting point for meta-learning research rather than a production library.

## Use cases
- reproduce MAML few-shot learning experiments in PyTorch
- run 5-way 1-shot classification on MiniImagenet
- train meta-learner on Omniglot dataset
- learn how meta-learning inner/outer loops are implemented
- baseline code for few-shot learning research

## When to choose
- you want a readable PyTorch reference implementation of MAML
- you need a starting point for few-shot meta-learning research
- you want to benchmark against MiniImagenet or Omniglot

## When to avoid
- you need a maintained production library with active support
- you want first-order methods like Reptile (use the author's Reptile repo)
- you need TensorFlow instead of PyTorch

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, artificial-intelligence
- platform: python
- tags: meta-learning, few-shot-learning, pytorch, maml, research-code, miniimagenet, omniglot, gpu

## Member repositories
- dragen1860/MAML-Pytorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:55.445047+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:28:06.215178+00:00, confidence not recorded.
  - readme: https://github.com/dragen1860/MAML-Pytorch (fetched 2026-08-28T04:06:55.445047+00:00, sha 192dfdce4ed4)
- Data as of 2026-08-30T08:39:29.467469+00:00.