facebookresearch/mixup-cifar10

Health v2 · maintenance only

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1197 stars · 230 forks observed · 2026-08-28

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

Official PyTorch implementation of mixup, a data augmentation technique that trains neural networks on convex combinations of image pairs and their labels. This repository reproduces the CIFAR-10 results from the ICLR 2018 paper 'mixup: Beyond Empirical Risk Minimization'.

Use cases

• implement mixup data augmentation in pytorch
• reproduce mixup paper results on cifar10
• improve neural network regularization with data augmentation
• train image classifiers with mixup
• learn how mixup works from reference code

When to choose

• you want the reference implementation of mixup from the original authors
• you are reproducing the paper's CIFAR-10 experiments
• you need a simple example of mixup in PyTorch

When to avoid

• you need a maintained, production-ready augmentation library
• you need a permissive license (this is CC-BY-NC)
• you work on Windows or need modern Python versions
• you want mixup support for frameworks other than PyTorch

Facets

library · maturity maintenance

machine-learning deep-learning data-generation machine-learning deep-learning computer-vision python data-augmentation pytorch cifar10 research-code regularization linux macos gpu

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