# moskomule/senet.pytorch

PyTorch implementation of SENet

Repository: https://github.com/moskomule/senet.pytorch
Canonical: https://ross.abutalabs.com/products/senetpytorch
Language: Python
License: MIT
License Family: permissive
Topics: senet, pytorch
Last push: 2021-03-02T04:09:12+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3277, "days_push": 2010, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2344, forks 447 (observed 2026-08-28T04:06:39.631031+00:00)

## What it is
A PyTorch implementation of Squeeze-and-Excitation Networks (SENet), the ILSVRC 2017 classification winner, including SE-ResNet and SE-Inception-v3 variants. It provides trainable architectures, training scripts for CIFAR-10 and ImageNet, and pretrained models loadable via torch.hub.

## Use cases
- add squeeze-and-excitation blocks to my pytorch model
- load a pretrained SE-ResNet50 with torch.hub
- train SE-ResNet on CIFAR-10
- train SENet on ImageNet with distributed GPUs
- reproduce ILSVRC 2017 winning architecture in pytorch
- compare SE-ResNet accuracy against plain ResNet

## When to choose
- you need SENet/SE-ResNet architectures in PyTorch with pretrained weights
- you want a lightweight, MIT-licensed reference implementation for research
- you want models loadable via torch.hub

## When to avoid
- you need actively maintained code with recent PyTorch support (last release 2021)
- you want a broad model zoo rather than SENet specifically
- you need production training pipelines with configurable hyperparameters

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: deep-learning, computer-vision, image-processing
- platform: python
- tags: pytorch, senet, squeeze-and-excitation, image-classification, torch-hub, pretrained-models, resnet, inception, gpu

## Member repositories
- moskomule/senet.pytorch (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:39.631031+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 2026-08-30T02:37:04.972531+00:00, confidence not recorded.
  - readme: https://github.com/moskomule/senet.pytorch (fetched 2026-08-28T04:06:39.631031+00:00, sha 60e27a6deb56)
- Data as of 2026-08-30T08:39:29.467469+00:00.