# ai-dawang/PlugNPlay-Modules

Repository: https://github.com/ai-dawang/PlugNPlay-Modules
Canonical: https://ross.abutalabs.com/products/plugnplay-modules
Language: Python
License Family: other
Last push: 2025-08-05T11:10:38+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 35, release rhythm 35, longevity 51
- inputs: {"age_days": 726, "days_push": 393, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5105, forks 368 (observed 2026-08-28T04:09:09.812523+00:00)

## What it is
A curated collection of plug-and-play deep learning modules (convolutions, attention mechanisms, downsampling, and feature fusion blocks) implemented in Python for computer vision tasks. It aims to let researchers quickly insert these modules into their own models for image classification, detection, segmentation, super-resolution, and other 2D vision tasks.

## Use cases
- add attention modules to my CNN to improve accuracy
- find plug-and-play feature fusion blocks for object detection
- improve YOLO model performance with new modules
- collect attention mechanism implementations for image segmentation
- boost super-resolution model with drop-in modules
- learn how to integrate modules from papers into my model

## When to choose
- you want ready-made vision modules to drop into classification, detection, or segmentation models
- you are a researcher looking for inspiration and implementations of recent module papers
- you want Chinese-language guidance on 'module stitching' techniques for paper writing

## When to avoid
- you need a maintained, tested library with stable APIs and a license
- you need production-grade, optimized implementations rather than research snippets
- you work mainly on 1D or 3D tasks, which are not yet covered

## Facets
- artifact type: library
- maturity: active
- function: deep-learning, machine-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, image-processing, machine-learning, tutorials
- platform: python, cross-platform
- tags: plug-and-play-modules, attention-mechanism, convolution, feature-fusion, downsampling, yolo, model-improvement, paper-reproduction, chinese-community

## Member repositories
- ai-dawang/PlugNPlay-Modules (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.812523+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 2026-08-29T18:02:11.236564+00:00, confidence not recorded.
  - readme: https://github.com/ai-dawang/PlugNPlay-Modules (fetched 2026-08-28T04:09:09.812523+00:00, sha d110b61d74c7)
- Data as of 2026-08-30T08:39:29.467469+00:00.