xmu-xiaoma666/External-Attention-pytorch

Health v2 · maintenance only

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Adoption not part of the score

12186 stars · 1934 forks observed · 2026-10-04

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch library (fightingcv-attention) providing clean, minimal implementations of numerous attention mechanisms, MLP variants, re-parameterization modules, and convolution blocks from research papers. It is designed to help researchers and learners understand paper core ideas without digging through complex task-specific codebases.

Use cases

• implement attention mechanisms in pytorch
• understand attention papers through code
• add cbam or squeeze-excitation to my model
• find minimal implementations of vision transformer attention modules
• learn how re-parameterization modules work
• use attention blocks as plug-and-play components in my network
• compare different attention mechanism implementations

When to choose

• you want readable, standalone implementations of attention/MLP/conv modules from papers
• you need plug-and-play PyTorch modules for research experiments
• you are learning deep learning architecture concepts from code

When to avoid

• you need production-tested, heavily optimized layers
• you need full model architectures for detection or segmentation rather than individual modules
• you need frameworks other than PyTorch

Facets

library · maturity active

deep-learning machine-learning image-processing deep-learning computer-vision machine-learning python attention-mechanisms pytorch paper-implementations mlp re-parameterization convolution cbam squeeze-excitation research educational

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