# pprp/awesome-attention-mechanism-in-cv

Awesome List of Attention Modules and Plug&Play Modules in Computer Vision

Repository: https://github.com/pprp/awesome-attention-mechanism-in-cv
Canonical: https://ross.abutalabs.com/products/awesome-attention-mechanism-in-cv
Language: Python
License: MIT
License Family: permissive
Topics: pytorch-attention, attention-model, attention-mechanisms, implementation, vision-transformer, plugandplay, computer-vision
Last push: 2023-05-11T10:28:13+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": 2061, "days_push": 1210, "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 1282, forks 172 (observed 2026-08-28T04:04:13.946260+00:00)

## What it is
A curated awesome list of attention mechanisms and plug-and-play modules for computer vision, with links to papers and implementations. It covers SENet, CBAM, Non-Local Networks, and Vision Transformers.

## Use cases
- find attention modules for computer vision models
- learn about vision transformer papers
- find plug-and-play attention blocks for pytorch
- compare channel and spatial attention mechanisms
- find implementations of SENet or CBAM

## When to choose
- researching attention mechanisms for image models
- looking for ready-to-use attention module implementations

## When to avoid
- you need production-ready maintained code
- you want a runnable library rather than a link collection

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, computer-vision, deep-learning
- domain: computer-vision, deep-learning, awesome-lists
- platform: python
- tags: -

## Member repositories
- pprp/awesome-attention-mechanism-in-cv (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:13.946260+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 2026-08-30T04:57:43.538077+00:00, confidence not recorded.
  - readme: https://github.com/pprp/awesome-attention-mechanism-in-cv (fetched 2026-08-28T04:04:13.946260+00:00, sha 4e0cd833a2ba)
- Data as of 2026-08-30T08:39:29.467469+00:00.