* change domain to mmdeploy * update tests * resolve comments |
||
---|---|---|
.github | ||
cmake | ||
configs | ||
csrc | ||
demo | ||
docs | ||
docs_zh_CN | ||
mmdeploy | ||
requirements | ||
tests | ||
third_party | ||
tools | ||
.clang-format | ||
.gitignore | ||
.gitmodules | ||
.isort.cfg | ||
.pre-commit-config.yaml | ||
.pylintrc | ||
.readthedocs.yml | ||
CITATION.cff | ||
CMakeLists.txt | ||
LICENSE | ||
MANIFEST.in | ||
README.md | ||
README_zh-CN.md | ||
requirements.txt | ||
setup.cfg | ||
setup.py |
README.md
Introduction
English | 简体中文
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.
Major features
-
OpenMMLab model support
Models in OpenMMLab can be deployed with this project. Such as MMClassification, MMDetection, etc.
-
Multiple inference engine support
Models can be exported and run in different backends. Such as ONNX Runtime, TensorRT, etc.
-
Model rewrite
Modules and functions used in models can be rewritten to meet the demand of different backends. It is easy to add new model support.
License
This project is released under the Apache 2.0 license.
Codebase and Backend support
Supported codebase:
- MMClassification
- MMDetection
- MMSegmentation
- MMEditing
- MMOCR
Supported backend:
- ONNX Runtime
- TensorRT
- PPL
- ncnn
- OpenVINO
Installation
Please refer to get_started.md for installation.
Getting Started
Please read how_to_convert_model.md for the basic usage of MMDeploy. There are also tutorials on how to write config, how to support new models and how to measure performance of models.
Please refer to FAQ for frequently asked questions.
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}
}
Contributing
We appreciate all contributions to improve MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
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.