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Highlights

The MMDeploy 1.x has been released, which is adapted to upstream codebases from OpenMMLab 2.0. Please align the version when using it. The default branch has been switched to main from master. MMDeploy 0.x (master) will be deprecated and new features will only be added to MMDeploy 1.x (main) in future.

mmdeploy	mmengine	mmcv	mmdet	others
0.x.y	-	<=1.x.y	<=2.x.y	0.x.y
1.x.y	0.x.y	2.x.y	3.x.y	1.x.y

deploee offers over 2,300 AI models in ONNX, NCNN, TRT and OpenVINO formats. Featuring a built-in list of real hardware devices, deploee enables users to convert Torch models into any target inference format for profiling purposes.

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

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 _ncnn	-	-
ARM CPU	_ncnn	-	-	_ncnn
RISC-V	_ncnn	-	-	-
NVIDIA GPU	_onnxruntime _TensorRT _LibTorch _pplnn	_onnxruntime _TensorRT	-	-
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:

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:

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

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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.
MMYOLO: OpenMMLab YOLO series toolbox and benchmark
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.

open-mmlab/mmdeploy

open-mmlab

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