105 lines
4.5 KiB
Markdown
105 lines
4.5 KiB
Markdown
<div align="center">
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<img src="resources/mmdeploy-logo.png" width="450"/>
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</div>
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## Introduction
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English | [简体中文](README_zh-CN.md)
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MMDeploy is an open-source deep learning model deployment toolset. It is
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a part of the [OpenMMLab](https://openmmlab.com/) project.
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<div align="center">
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<img src="resources/introduction.png" width="800"/>
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</div>
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### Major features
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- **Fully support OpenMMLab models**
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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
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- [x] MMClassification
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- [x] MMDetection
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- [x] MMSegmentation
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- [x] MMEditing
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- [x] MMOCR
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- **Multiple inference backends are available**
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Models can be exported and run in different backends. The following ones are supported, and more will be taken into consideration
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- [x] ONNX Runtime
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- [x] TensorRT
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- [x] PPLNN
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- [x] ncnn
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- [x] OpenVINO
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- **Efficient and highly scalable SDK Framework by C/C++**
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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
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## License
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This project is released under the [Apache 2.0 license](LICENSE).
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## Installation
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Please refer to [build.md](docs/en/build.md) for installation.
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## Getting Started
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Please see [getting_started.md](docs/en/get_started.md) for the basic usage of MMDeploy. We also provide other tutorials for:
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- [how to convert model](docs/en/tutorials/how_to_convert_model.md)
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- [how to write config](docs/en/tutorials/how_to_write_config.md)
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- [how to support new models](docs/en/tutorials/how_to_support_new_models.md)
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- [how to measure performance of models](docs/en/tutorials/how_to_measure_performance_of_models.md)
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Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
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## Benchmark and model zoo
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Results and supported model list are available in the [benchmark](docs/en/benchmark.md) and [model list](docs/en/tutorials/how_to_convert_model.md).
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## Contributing
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We appreciate all contributions to improve MMDeploy. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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## Acknowledgement
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We would like to thank OpenVINO team, for their remarkable efforts to export MMDetection models to OpenVINO and integrate OpenVINO into MMDeploy backends
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## Citation
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If you find this project useful in your research, please consider cite:
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```BibTeX
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@misc{=mmdeploy,
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title={OpenMMLab's Model Deployment Toolbox.},
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author={MMDeploy Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
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year={2021}
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}
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```
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## Projects in OpenMMLab
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
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- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
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- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab FewShot Learning Toolbox and Benchmark.
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab Human Pose and Shape Estimation Toolbox and Benchmark.
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- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning Toolbox and Benchmark.
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- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab Model Compression Toolbox and Benchmark.
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