Introduction

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

Major features

• Fully support OpenMMLab models

  We provide a unified model deployment toolbox for the codebases in OpenMMLab. The supported codebases are listed as below, and more will be added in the future

  • MMClassification
  • MMDetection
  • MMSegmentation
  • MMEditing
  • MMOCR

• Multiple inference backends are available

  Models can be exported and run in different backends. The following ones are supported, and more will be taken into consideration

  • ONNX Runtime
  • TensorRT
  • PPLNN
  • ncnn
  • OpenVINO

• Efficient and highly scalable SDK Framework by C/C++

  All kinds of modules in SDK can be extensible, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on

License

This project is released under the Apache 2.0 license.

Installation

Please refer to build.md for installation.

Getting Started

Please see getting_started.md for the basic usage of MMDeploy. We also provide other tutorials for:

• how to convert model
• how to write config
• how to support new models
• how to measure performance of models

Please refer to FAQ for frequently asked questions.

Benchmark and model zoo

Results and supported model list are available in the benchmark and model list.

Contributing

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

Acknowledgement

We would like to thank OpenVINO team, for their remarkable efforts to export MMDetection models to OpenVINO and integrate OpenVINO into MMDeploy backends

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}
}

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.
• MMFlow: OpenMMLab optical flow toolbox and benchmark.
• MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.
• MMHuman3D: OpenMMLab Human Pose and Shape Estimation Toolbox and Benchmark.
• MMSelfSup: OpenMMLab self-supervised learning Toolbox and Benchmark.
• MMRazor: OpenMMLab Model Compression Toolbox and Benchmark.