# google-research/mixmatch

Repository: https://github.com/google-research/mixmatch
Canonical: https://ross.abutalabs.com/products/mixmatch
Language: Python
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2023-03-24T22:14:44+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2667, "days_push": 1258, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1139, forks 161 (observed 2026-08-28T04:03:44.255725+00:00)

## What it is
Reference implementation of MixMatch, a holistic semi-supervised learning algorithm from a Google Research paper. It includes dataset preparation scripts and training code for CIFAR, SVHN, and STL-10 semi-supervised benchmarks.

## Use cases
- reproduce MixMatch semi-supervised learning results
- train image classifiers with few labeled examples
- compare semi-supervised learning methods on CIFAR-10
- study label-consistency regularization with mixup
- run semi-supervised experiments on SVHN and STL-10

## When to choose
- you want the official reference implementation of the MixMatch paper
- you need semi-supervised image classification benchmarks with prepared splits
- you are doing research on semi-supervised learning techniques

## When to avoid
- you need a production-ready or actively maintained ML library
- you want a framework-agnostic or PyTorch implementation
- you need semi-supervised learning for non-image modalities

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, artificial-intelligence
- platform: python
- tags: semi-supervised-learning, research-code, tensorflow, mixup, data-augmentation, paper-reproduction, gpu

## Member repositories
- google-research/mixmatch (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:44.255725+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 2026-08-30T06:35:52.286519+00:00, confidence not recorded.
  - readme: https://github.com/google-research/mixmatch (fetched 2026-08-28T04:03:44.255725+00:00, sha 4b1ff6bcca23)
- Data as of 2026-08-30T08:39:29.467469+00:00.