mmdeploy/README.md

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English | [简体中文](README_zh-CN.md)

## Introduction

MMDeploy is an open-source deep learning model deployment toolset. It is a part of the [OpenMMLab](https://openmmlab.com/) project.

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  <img src="resources/introduction.png">
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## Main features

### Fully support OpenMMLab models

The currently supported codebases and models are as follows, and more will be included in the future
  - [mmcls](docs/en/04-supported-codebases/mmcls.md)
  - [mmdet](docs/en/04-supported-codebases/mmdet.md)
  - [mmseg](docs/en/04-supported-codebases/mmseg.md)
  - [mmedit](docs/en/04-supported-codebases/mmedit.md)
  - [mmocr](docs/en/04-supported-codebases/mmocr.md)
  - [mmpose](docs/en/04-supported-codebases/mmpose.md)
  - [mmdet3d](docs/en/04-supported-codebases/mmdet3d.md)
  - [mmrotate](docs/en/04-supported-codebases/mmrotate.md)

### Multiple inference backends are available

Models can be exported and run in the following backends, and more will be compatible

| ONNX Runtime | TensorRT | ppl.nn | ncnn | OpenVINO | LibTorch | more                                           |
| ------------ | -------- | ------ | ---- | -------- | -------- | ---------------------------------------------- |
| ✔️            | ✔️        | ✔️      | ✔️    | ✔️        | ✔️        | [benchmark](docs/en/03-benchmark/benchmark.md) |



### 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

## Get Started

Please read [getting_started.md](docs/en/get_started.md) for the basic usage of MMDeploy. We also provide tutoials about:

* [Build](docs/en/01-how-to-build/build_from_source.md)
  * [Build from Docker](docs/en/01-how-to-build/build_from_docker.md)
  * [Build for Linux](docs/en/01-how-to-build/linux-x86_64.md)
  * [Build for Win10](docs/en/01-how-to-build/windows.md)
  * [Build for Android](docs/en/01-how-to-build/android.md)
  * [Build for Jetson](docs/en/01-how-to-build/jetsons.md)
* User Guide
  * [How to convert model](docs/en/02-how-to-run/convert_model.md)
  * [How to write config](docs/en/02-how-to-run/write_config.md)
  * [How to evaluate deployed models](docs/en/02-how-to-run/how_to_evaluate_a_model.md)
  * [How to measure performance of deployed models](docs/en/02-how-to-run/how_to_measure_performance_of_models.md)
* Developer Guide
  * [How to support new models](docs/en/06-developer-guide/support_new_model.md)
  * [How to support new backends](docs/en/06-developer-guide/support_new_backend.md)
* [FAQ](docs/en/faq.md)
* [Contributing](.github/CONTRIBUTING.md)

## Benchmark and Model zoo

You can find the supported models from [here](docs/en/03-benchmark/supported_models.md) and their performance in the [benchmark](docs/en/03-benchmark/benchmark.md).

## Contributing

We appreciate all contributions to 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)
- [ncnn](https://github.com/Tencent/ncnn)

## Citation

If you find this project useful in your research, please consider citing:

```BibTeX
@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](LICENSE).

## 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.