OpenMMLab Model Deployment Framework
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.
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.
The currently supported codebases and models are as follows, and more will be included in the future
The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.
The benchmark can be found from here
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:
You can find the supported models from here and their performance in the benchmark.
We appreciate all contributions to MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
We would like to sincerely thank the following teams for their contributions to MMDeploy:
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}
}
This project is released under the Apache 2.0 license.