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Introduction

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

Main features

Fully support OpenMMLab models

The currently supported codebases and models are as follows, and more will be included in the future

• mmcls
• mmdet
• mmseg
• mmedit
• mmocr
• mmpose
• mmdet3d
• mmrotate
• mmaction2

Multiple inference backends are available

The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.

The benchmark can be found from here

Device / Platform	Linux	Windows	macOS	Android
x86_64 CPU	ONNXRuntime pplnn ncnn LibTorch OpenVINO TVM	ONNXRuntime OpenVINO	-	-
ARM CPU	ncnn	-	-	ncnn
RISC-V	ncnn	-	-	-
NVIDIA GPU	ONNXRuntime TensorRT pplnn LibTorch TVM	ONNXRuntime TensorRT pplnn	-	-
NVIDIA Jetson	TensorRT	-	-	-
Huawei ascend310	CANN	-	-	-
Rockchip	RKNN	-	-	-
Apple M1	-	-	CoreML	-
Adreno GPU	-	-	-	SNPE ncnn
Hexagon DSP	-	-	-	SNPE

                                                     |

Efficient and scalable C/C++ SDK Framework

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

Documentation

Please read getting_started for the basic usage of MMDeploy. We also provide tutoials about:

• Build
  • Build from Docker
  • Build from Script
  • Build for Linux
  • Build for Windows
  • Build for macOS
  • Build for Win10
  • Build for Android
  • Build for Jetson
  • Build for SNPE
  • Build for Rockchip
  • Cross Build for aarch64
• User Guide
  • How to convert model
  • How to write config
  • How to profile model
  • How to quantize model
  • Useful tools
• Developer Guide
  • Architecture
  • How to support new models
  • How to support new backends
  • How to partition model
  • How to test rewritten model
  • How to test backend ops
  • How to do regression test
• Custom Backend Ops
  • ncnn
  • onnxruntime
  • tensorrt
• FAQ
• Contributing

Benchmark and Model zoo

You can find the supported models from here and their performance in the benchmark.

Contributing

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

Acknowledgement

We would like to sincerely thank the following teams for their contributions to MMDeploy:

• OpenPPL
• OpenVINO
• ncnn

Citation

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

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

License

This project is released under the Apache 2.0 license.

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.
• MMYOLO: OpenMMLab YOLO series toolbox and benchmark
• MMRotate: OpenMMLab rotated object detection 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.
• MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
• MMRazor: OpenMMLab model compression toolbox and benchmark.
• MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMFlow: OpenMMLab optical flow toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.