# BangguWu/ECANet

Code for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Repository: https://github.com/BangguWu/ECANet
Canonical: https://ross.abutalabs.com/products/ecanet
Language: Python
License: MIT
License Family: permissive
Last push: 2021-05-16T07:00:27+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2544, "days_push": 1935, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1416, forks 205 (observed 2026-08-28T04:04:39.946408+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, computer-vision, image-processing
- platform: python
- tags: channel-attention, pytorch, cnn, cvpr2020, attention-mechanism, computer-vision

## Member repositories
- BangguWu/ECANet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:39.946408+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:38:03.273947+00:00, confidence not recorded.
  - readme: https://github.com/BangguWu/ECANet (fetched 2026-08-28T04:04:39.946408+00:00, sha 450a66d9a003)
- Data as of 2026-08-30T08:39:29.467469+00:00.