BangguWu/ECANet

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1416 stars · 205 forks observed · 2026-08-28

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

Official PyTorch implementation of ECA-Net, an efficient channel attention module for deep convolutional neural networks published at CVPR 2020. It provides ECA-enhanced ResNet and MobileNetV2 backbones plus pretrained models for image classification, object detection, and instance segmentation.

Use cases

• add channel attention to a CNN with minimal parameter overhead
• improve image classification accuracy over SENet without extra complexity
• plug an efficient attention module into ResNet or MobileNetV2 backbones
• boost object detection and instance segmentation with attention-enhanced backbones
• reproduce CVPR 2020 ECA-Net paper results
• compare channel attention module efficiency and accuracy

When to choose

• you need channel attention with near-zero parameter and FLOPs overhead
• you want pretrained ECA-ResNet models for classification or detection
• you are researching or benchmarking attention mechanisms in CNNs

When to avoid

• you need transformer or vision-transformer attention architectures
• you need a maintained library with frequent updates and broad model zoo
• you work outside PyTorch or need production-grade support

Facets

library · maturity maintenance

machine-learning deep-learning image-processing deep-learning computer-vision image-processing python channel-attention pytorch cnn cvpr2020 attention-mechanism computer-vision

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markdown · JSON · MCP: product_card(name="BangguWu/ECANet")