luuuyi/CBAM.PyTorch

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1500 stars · 290 forks observed · 2026-08-28

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

A non-official PyTorch re-implementation of the CBAM (Convolutional Block Attention Module) paper from ECCV 2018. It provides channel and spatial attention modules that can be inserted into convolutional networks like ResNet to improve image classification accuracy.

Use cases

• add attention modules to a resnet in pytorch
• reproduce CBAM paper results on ImageNet
• improve image classification accuracy with attention
• learn how channel and spatial attention works
• plug CBAM blocks into my own CNN architecture

When to choose

• you want a simple PyTorch implementation of CBAM to drop into your models
• you are studying or reproducing the ECCV 2018 CBAM paper
• you need verified ImageNet gains from adding attention to ResNet

When to avoid

• you need an officially maintained library with a license
• you want a broad toolkit of many attention mechanisms
• you need support for modern PyTorch versions out of the box

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision deep-learning image-processing python attention-module cbam pytorch image-classification resnet paper-implementation

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markdown · JSON · MCP: product_card(name="luuuyi/CBAM.PyTorch")