Coder Social home page Coder Social logo

pa4y / mmpose Goto Github PK

View Code? Open in Web Editor NEW

This project forked from open-mmlab/mmpose

0.0 0.0 0.0 53.43 MB

OpenMMLab Pose Estimation Toolbox and Benchmark.

Home Page: https://mmpose.readthedocs.io/en/latest/

License: Apache License 2.0

Shell 0.13% C++ 0.79% Python 98.59% Jupyter Notebook 0.44% Dockerfile 0.05%

mmpose's Introduction

Introduction

English | 简体中文

MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project.

The main branch works with PyTorch 1.8+.

mmpose.demo.mp4

Major Features

  • Support diverse tasks

    We support a wide spectrum of mainstream pose analysis tasks in current research community, including 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, 3d human mesh recovery, fashion landmark detection and animal pose estimation. See Demo for more information.

  • Higher efficiency and higher accuracy

    MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We achieve faster training speed and higher accuracy than other popular codebases, such as HRNet. See benchmark.md for more information.

  • Support for various datasets

    The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc. See dataset_zoo for more information.

  • Well designed, tested and documented

    We decompose MMPose into different components and one can easily construct a customized pose estimation framework by combining different modules. We provide detailed documentation and API reference, as well as unittests.

What's New

  • We have added support for two new datasets:

  • Support for four new algorithms:

  • Released the first whole-body pose estimation model, RTMW, with accuracy exceeding 70 AP on COCO-Wholebody. For details, refer to RTMPose. Try it now!

rtmw

  • Welcome to use the MMPose project. Here, you can discover the latest features and algorithms in MMPose and quickly share your ideas and code implementations with the community. Adding new features to MMPose has become smoother:

    • Provides a simple and fast way to add new algorithms, features, and applications to MMPose.
    • More flexible code structure and style, fewer restrictions, and a shorter code review process.
    • Utilize the powerful capabilities of MMPose in the form of independent projects without being constrained by the code framework.
    • Newly added projects include:
    • Start your journey as an MMPose contributor with a simple example project, and let's build a better MMPose together!

  • October 12, 2023: MMPose v1.2.0 has been officially released, with major updates including:

    • Support for new datasets: UBody, 300W-LP.
    • Support for new algorithms: MotionBERT, DWPose, EDPose, Uniformer.
    • Migration of Associate Embedding, InterNet, YOLOX-Pose algorithms.
    • Migration of the DeepFashion2 dataset.
    • Support for Badcase visualization analysis, multi-dataset evaluation, and keypoint visibility prediction features.

    Please check the complete release notes for more details on the updates brought by MMPose v1.2.0!

0.x / 1.x Migration

MMPose v1.0.0 is a major update, including many API and config file changes. Currently, a part of the algorithms have been migrated to v1.0.0, and the remaining algorithms will be completed in subsequent versions. We will show the migration progress in this Roadmap.

If your algorithm has not been migrated, you can continue to use the 0.x branch and old documentation.

Installation

Please refer to installation.md for more detailed installation and dataset preparation.

Getting Started

We provided a series of tutorials about the basic usage of MMPose for new users:

  1. For the basic usage of MMPose:

  2. For developers who wish to develop based on MMPose:

  3. For researchers and developers who are willing to contribute to MMPose:

  4. For some common issues, we provide a FAQ list:

Model Zoo

Results and models are available in the README.md of each method's config directory. A summary can be found in the Model Zoo page.

Supported algorithms:
Supported techniques:
Supported datasets:
Supported backbones:

Model Request

We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in MMPose Roadmap.

Contributing

We appreciate all contributions to improve MMPose. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

MMPose is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new models.

Citation

If you find this project useful in your research, please consider cite:

@misc{mmpose2020,
    title={OpenMMLab Pose Estimation Toolbox and Benchmark},
    author={MMPose Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmpose}},
    year={2020}
}

License

This project is released under the Apache 2.0 license.

Projects in OpenMMLab

  • MMEngine: OpenMMLab foundational library for training deep learning models.
  • MMCV: OpenMMLab foundational library for computer vision.
  • MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
  • MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMDeploy: OpenMMLab Model Deployment Framework.
  • MMRazor: OpenMMLab model compression toolbox and benchmark.
  • MIM: MIM installs OpenMMLab packages.
  • Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

mmpose's People

Contributors

ly015 avatar jin-s13 avatar tau-j avatar innerlee avatar ben-louis avatar liqikai9 avatar yaochaorui avatar zengwang430521 avatar wusize avatar cherryjm avatar liuxin9608 avatar lareinam avatar canwang-sjtu avatar xin-li-67 avatar luminxu avatar joannalxy avatar zheng-linxiao avatar motokimura avatar tommyzihao avatar hobeom avatar rangilyu avatar bladrome avatar dominic23331 avatar xinxinxinxu avatar vansin avatar dreampoet avatar ailingzengzzz avatar zhiyuan-ren avatar juxuan27 avatar zwfcrazy avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.