# hujie-frank/SENet

Squeeze-and-Excitation Networks

Repository: https://github.com/hujie-frank/SENet
Canonical: https://ross.abutalabs.com/products/senet
Language: Cuda
License: Apache-2.0
License Family: permissive
Topics: senet, caffe, gpu
Last push: 2019-02-25T08:41:20+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": 3301, "days_push": 2746, "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 3646, forks 843 (observed 2026-08-28T04:08:12.916300+00:00)

## What it is
Official Caffe/CUDA implementation of Squeeze-and-Excitation Networks (SENet), channel-attention building blocks for convolutional neural networks that won ILSVRC 2017 image classification. Includes pretrained ImageNet models (SE-ResNet, SE-ResNeXt, SENet-154) and optimized GPU layers like Axpy.

## Use cases
- add channel attention to CNN architectures
- reproduce ILSVRC 2017 winning image classification results
- download pretrained SE-ResNet models for ImageNet
- speed up global average pooling on GPU in Caffe
- train image classifiers with squeeze-and-excitation blocks

## When to choose
- you work in Caffe and want SE blocks or pretrained SENet weights
- you need the reference implementation of the SE paper

## When to avoid
- you use PyTorch or TensorFlow - SE blocks are built into modern frameworks
- you need actively maintained code - the repo is a research artifact from 2018

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: computer-vision, deep-learning, image-processing
- platform: cpp
- tags: caffe, cuda, image-classification, channel-attention, pretrained-models, cvpr-2018, gpu, linux

## Member repositories
- hujie-frank/SENet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:12.916300+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 2026-08-29T18:31:44.335561+00:00, confidence not recorded.
  - readme: https://github.com/hujie-frank/SENet (fetched 2026-08-28T04:08:12.916300+00:00, sha 33a9de3c4521)
- Data as of 2026-08-30T08:39:29.467469+00:00.