houqb/CoordAttention

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1152 stars · 125 forks observed · 2026-08-28

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

A PyTorch implementation of Coordinate Attention (CoordAttention), an efficient attention mechanism for mobile networks introduced in a CVPR 2021 paper. It provides a pluggable attention block that can be inserted into classic building blocks like MobileNetV2 inverted residuals and MobileNeXt sandglass blocks to improve feature representation.

Use cases

• add coordinate attention to a MobileNetV2 model
• improve ImageNet classification accuracy on lightweight networks
• plug an attention block into custom CNN backbones
• train object detection models with coordinate attention
• boost semantic segmentation performance on mobile networks
• compare SE, CBAM, and coordinate attention blocks
• download a pretrained MobileNetV2 with CA weights

When to choose

• you need an efficient attention mechanism for mobile or lightweight vision models
• you want to plug attention into existing CNN building blocks with minimal code
• you are reproducing CVPR 2021 coordinate attention results on ImageNet, COCO, or segmentation benchmarks

When to avoid

• you need transformer-style self-attention for large vision models
• you require a maintained library with frequent updates and broad model zoo support
• you work outside PyTorch

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing deep-learning python attention-mechanism pytorch cvpr2021 mobilenet lightweight-networks pretrained-models

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markdown · JSON · MCP: product_card(name="houqb/CoordAttention")