# AIFengheshu/Plug-play-modules

2025年全网最全即插即用模块，免费分享！CVPR2025，AAAI2025，ICLR2025，TNNLS2025，arXiv2025......包含人工智能全领域（机器学习、深度学习等），适用于图像分类、目标检测、实例分割、语义分割、全景分割、姿态识别、医学图像分割、视频目标分割、图像抠图、图像编辑、单目标跟踪、多目标跟踪、行人重识别、RGBT、图像去噪、去雨、去雾、去阴影、去模糊、超分辨率、去反光、去摩尔纹、图像恢复、图像修复、高光谱图像恢复、图像融合、图像上色、高动态范围成像、视频与图像压缩、3D点云、3D目标检测、3D语义分割、3D姿态识别等各类计算机视觉和图像处理任务，以及自然语言处理、大语言模型、多模态等其他各类人工智能相关任务。持续更新中......

Repository: https://github.com/AIFengheshu/Plug-play-modules
Canonical: https://ross.abutalabs.com/products/plug-play-modules
Language: Python
License Family: other
Last push: 2025-05-24T02:30:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 23, release rhythm 35, longevity 49
- inputs: {"age_days": 693, "days_push": 467, "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 1606, forks 122 (observed 2026-08-28T04:05:10.425075+00:00)

## What it is
A curated collection of plug-and-play neural network modules (attention mechanisms, convolutions, fusion blocks) extracted from 2024-2025 papers like CVPR, AAAI, ECCV, and ICLR. Each module is provided as a standalone Python file with paper links and Chinese-language explanations, targeting computer vision and broader AI tasks.

## Use cases
- find plug-and-play attention modules for my model
- boost image segmentation accuracy with drop-in modules
- implement wavelet convolution from ECCV 2024
- find reusable blocks for object detection backbones
- learn how recent paper modules work with code
- add feature fusion modules to my CNN
- find modules for image restoration and super-resolution

## When to choose
- you want ready-to-copy PyTorch modules from recent papers to insert into your own architecture
- you are doing computer vision research and want to experiment with attention or fusion blocks
- you want paper links plus code in one place

## When to avoid
- you need a maintained, tested library with stable APIs and CI
- you need production-grade code with a license for commercial use (no license is provided)
- you need full model implementations rather than isolated modules

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision, nlp
- domain: computer-vision, image-processing, deep-learning, machine-learning, tutorials
- platform: python
- tags: plug-and-play-modules, attention-mechanisms, paper-implementations, awesome-list, research-code, pytorch, natural-language-processing

## Member repositories
- AIFengheshu/Plug-play-modules (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:10.425075+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 2026-08-30T03:51:48.922517+00:00, confidence not recorded.
  - readme: https://github.com/AIFengheshu/Plug-play-modules (fetched 2026-08-28T04:05:10.425075+00:00, sha 8fd06ef4704d)
- Data as of 2026-08-30T08:39:29.467469+00:00.