microsoft/Semi-supervised-learning

Health v2 · maintenance only

Adoption not part of the score

1588 stars · 220 forks observed · 2026-08-28

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

USB (Unified Semi-supervised learning Benchmark) is a PyTorch-based codebase from Microsoft for semi-supervised learning across computer vision, NLP, and audio classification. It provides implementations of popular SSL algorithms with a unified framework and benchmark results.

Use cases

• train semi-supervised image classifiers with limited labels
• apply SSL algorithms to text classification
• run audio classification with few labeled examples
• benchmark FixMatch, FlexMatch, and other SSL methods
• experiment with transformers in low-resource settings

When to choose

• you need a unified framework to compare SSL algorithms
• you want reproducible benchmarks across CV, NLP, and audio
• you work in PyTorch and need ready-made SSL implementations

When to avoid

• you need fully supervised training pipelines only
• you require production deployment tooling rather than research code
• you work outside PyTorch

Facets

library · maturity active

machine-learning deep-learning machine-learning computer-vision python semi-supervised-learning pytorch classification benchmark low-resource natural-language-processing audio

Member repositories

For agents

markdown · JSON · MCP: product_card(name="microsoft/Semi-supervised-learning")