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
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
- 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.
- 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.
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