pengzhiliang/MAE-pytorch

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2689 stars · 341 forks observed · 2026-08-28

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

An unofficial PyTorch implementation of the MAE (Masked Autoencoders Are Scalable Vision Learners) paper, providing pretraining and finetuning scripts for vision transformers. It implements masked token reconstruction with distributed training on ImageNet.

Use cases

• pretrain a vision transformer with masked autoencoding
• reproduce MAE self-supervised pretraining in PyTorch
• finetune a masked autoencoder model on ImageNet
• learn how MAE masking and reconstruction works
• visualize MAE image reconstructions

When to choose

• you want a PyTorch codebase for MAE-style self-supervised pretraining
• you want to study or modify the MAE masking and decoder implementation
• you need pretrain and finetune pipelines built on a BEiT-style codebase

When to avoid

• you need guaranteed reproduction of the paper's reported results
• you want the official implementation with full feature parity (cls token, linear prob, knn eval are incomplete)
• you need a commercially licensed or maintained project (no license is provided)

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision deep-learning machine-learning python masked-autoencoder self-supervised-learning vision-transformer pytorch unofficial-implementation pretraining gpu

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markdown · JSON · MCP: product_card(name="pengzhiliang/MAE-pytorch")