OpenMMLab website HOT      OpenMMLab platform TRY IT OUT
 
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdeploy.readthedocs.io/en/latest/) [![badge](https://github.com/open-mmlab/mmdeploy/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdeploy/actions) [![codecov](https://codecov.io/gh/open-mmlab/mmdeploy/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdeploy) [![license](https://img.shields.io/github/license/open-mmlab/mmdeploy.svg)](https://github.com/open-mmlab/mmdeploy/blob/master/LICENSE) [![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdeploy.svg)](https://github.com/open-mmlab/mmdeploy/issues) [![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdeploy.svg)](https://github.com/open-mmlab/mmdeploy/issues) ## Introduction English | [简体中文](README_zh-CN.md) MMDeploy is an open-source deep learning model deployment toolset. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
### Major features - **Fully support OpenMMLab models** We provide a unified model deployment toolbox for the codebases in OpenMMLab. The supported codebases are listed as below, and more will be added in the future - [x] MMClassification - [x] MMDetection - [x] MMSegmentation - [x] MMEditing - [x] MMOCR - [x] MMPose - **Multiple inference backends are available** Models can be exported and run in different backends. The following ones are supported, and more will be taken into consideration - [x] ONNX Runtime - [x] TensorRT - [x] PPLNN - [x] ncnn - [x] OpenVINO - **Efficient and highly scalable SDK Framework by C/C++** All kinds of modules in SDK can be extensible, such as `Transform` for image processing, `Net` for Neural Network inference, `Module` for postprocessing and so on ## License This project is released under the [Apache 2.0 license](LICENSE). ## Installation Please refer to [build.md](https://mmdeploy.readthedocs.io/en/latest/build.html) for installation. ## Getting Started Please see [getting_started.md](https://mmdeploy.readthedocs.io/en/latest/get_started.html) for the basic usage of MMDeploy. We also provide other tutorials for: - [how to convert model](https://mmdeploy.readthedocs.io/en/latest/tutorials/how_to_convert_model.html) - [how to write config](https://mmdeploy.readthedocs.io/en/latest/tutorials/how_to_write_config.html) - [how to support new models](https://mmdeploy.readthedocs.io/en/latest/tutorials/how_to_support_new_models.html) - [how to measure performance of models](https://mmdeploy.readthedocs.io/en/latest/tutorials/how_to_measure_performance_of_models.html) Please refer to [FAQ](https://mmdeploy.readthedocs.io/en/latest/faq.html) for frequently asked questions. ## Benchmark and model zoo Results and supported model list are available in the [benchmark](https://mmdeploy.readthedocs.io/en/latest/benchmark.html) and [model list](https://mmdeploy.readthedocs.io/en/latest/supported_models.html). ## Contributing We appreciate all contributions to improve MMDeploy. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. ## Acknowledgement We would like to sincerely thank the following teams for their contributions to [MMDeploy](https://github.com/open-mmlab/mmdeploy): - [OpenPPL](https://github.com/openppl-public) - [OpenVINO](https://github.com/openvinotoolkit/openvino) ## Citation If you find this project useful in your research, please consider cite: ```BibTeX @misc{=mmdeploy, title={OpenMMLab's Model Deployment Toolbox.}, author={MMDeploy Contributors}, howpublished = {\url{https://github.com/open-mmlab/mmdeploy}}, year={2021} } ``` ## Projects in OpenMMLab - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages. - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark. - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox. - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark. - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark. - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark. - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark. - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox. - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.