OpenMMLab Model Deployment Framework
 
 
 
 
 
 
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[Enhancement]: Added support for masks in OpenVINO. (#148)
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requirements.txt
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setup.py

README.md

Introduction

English | 简体中文

MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.

Major features

  • OpenMMLab model support

    Models in OpenMMLab can be deployed with this project. Such as MMClassification, MMDetection, etc.

  • Multiple inference engine support

    Models can be exported and run in different backends. Such as ONNX Runtime, TensorRT, etc.

  • Model rewrite

    Modules and functions used in models can be rewritten to meet the demand of different backends. It is easy to add new model support.

License

This project is released under the Apache 2.0 license.

Codebase and Backend support

Supported codebase:

  • MMClassification
  • MMDetection
  • MMSegmentation
  • MMEditing
  • MMOCR

Supported backend:

  • ONNX Runtime
  • TensorRT
  • PPL
  • ncnn
  • OpenVINO

Installation

Please refer to build.md for installation.

Getting Started

Please read how_to_convert_model.md for the basic usage of MMDeploy. There are also tutorials for how to write config, how to support new models and how to test model.

Please refer to FAQ for frequently asked questions.

Citation

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

@misc{=mmdeploy,
    title={OpenMMLab's Model deployment toolbox.},
    author={MMDeploy Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
    year={2021}
}

Contributing

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

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MIM: MIM Installs OpenMMLab Packages.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
  • MMGeneration: OpenMMLab image and video generative models toolbox.