sail-sg/poolformer

Health v2 · maintenance only

Adoption not part of the score

1362 stars · 117 forks observed · 2026-08-28

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

PoolFormer is a PyTorch implementation of the CVPR 2022 paper 'MetaFormer Is Actually What You Need for Vision', which replaces attention in Transformers with simple pooling to show the MetaFormer architecture itself drives performance. It provides pretrained image-classification models achieving 82.1% top-1 accuracy on ImageNet-1K.

Use cases

• classify images with a lightweight pooling-based vision model
• study whether attention is necessary in vision transformers
• benchmark vision backbones against DeiT and ResMLP
• use a MetaFormer backbone for downstream vision tasks
• reproduce CVPR 2022 PoolFormer results on ImageNet

When to choose

• you need an efficient vision backbone with fewer parameters and MACs than ViT/MLP models
• you want to experiment with the MetaFormer architecture concept
• you need a simple non-attention token mixer for research

When to avoid

• you need state-of-the-art attention-based models for production vision tasks
• you need a maintained general-purpose vision framework rather than research code
• you require non-PyTorch frameworks

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision deep-learning machine-learning python vision-transformer pooling image-classification metaformer pytorch research-code cvpr-2022 gpu

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