166 lines
9.4 KiB
Markdown
166 lines
9.4 KiB
Markdown
<div align="center">
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<img src="resources/mmdeploy-logo.png" width="450"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab website</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab platform</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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</div>
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[](https://mmdeploy.readthedocs.io/en/latest/)
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[](https://github.com/open-mmlab/mmdeploy/actions)
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[](https://codecov.io/gh/open-mmlab/mmdeploy)
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[](https://github.com/open-mmlab/mmdeploy/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmdeploy/issues)
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[](https://github.com/open-mmlab/mmdeploy/issues)
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English | [简体中文](README_zh-CN.md)
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## Introduction
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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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<div align="center">
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<img src="resources/introduction.png">
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</div>
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## Main features
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### Fully support OpenMMLab models
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The currently supported codebases and models are as follows, and more will be included in the future
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- [mmcls](docs/en/04-supported-codebases/mmcls.md)
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- [mmdet](docs/en/04-supported-codebases/mmdet.md)
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- [mmseg](docs/en/04-supported-codebases/mmseg.md)
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- [mmedit](docs/en/04-supported-codebases/mmedit.md)
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- [mmocr](docs/en/04-supported-codebases/mmocr.md)
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- [mmpose](docs/en/04-supported-codebases/mmpose.md)
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- [mmdet3d](docs/en/04-supported-codebases/mmdet3d.md)
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- [mmrotate](docs/en/04-supported-codebases/mmrotate.md)
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### Multiple inference backends are available
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The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.
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The benchmark can be found from [here](docs/en/03-benchmark/benchmark.md)
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| Device / Platform | Linux | Windows | macOS | Android |
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| ----------------- | --------------------------------------------------------------- | --------------------------------------- | -------- | ---------------- |
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| x86_64 CPU | ✔️ONNX Runtime<br>✔️pplnn<br>✔️ncnn<br>✔️OpenVINO<br>✔️LibTorch | ✔️ONNX Runtime<br>✔️OpenVINO | - | - |
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| ARM CPU | ✔️ncnn | - | - | ✔️ncnn |
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| RISC-V | ✔️ncnn | - | - | - |
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| NVIDIA GPU | ✔️ONNX Runtime<br>✔️TensorRT<br>✔️pplnn<br>✔️LibTorch | ✔️ONNX Runtime<br>✔️TensorRT<br>✔️pplnn | - | - |
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| NVIDIA Jetson | ✔️TensorRT | ✔️TensorRT | - | - |
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| Huawei ascend310 | ✔️CANN | - | - | - |
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| Rockchip | ✔️RKNN | - | - | - |
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| Apple M1 | - | - | ✔️CoreML | - |
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| Adreno GPU | - | - | - | ✔️ncnn<br>✔️SNPE |
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| Hexagon DSP | - | - | - | ✔️SNPE |
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### Efficient and scalable C/C++ SDK Framework
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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
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## [Documentation](https://mmdeploy.readthedocs.io/en/latest/)
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Please read [getting_started](docs/en/get_started.md) for the basic usage of MMDeploy. We also provide tutoials about:
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- [Build](docs/en/01-how-to-build/build_from_source.md)
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- [Build from Docker](docs/en/01-how-to-build/build_from_docker.md)
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- [Build from Script](docs/en/01-how-to-build/build_from_script.md)
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- [Build for Linux](docs/en/01-how-to-build/linux-x86_64.md)
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- [Build for macOS](docs/en/01-how-to-build/macos-arm64.md)
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- [Build for Win10](docs/en/01-how-to-build/windows.md)
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- [Build for Android](docs/en/01-how-to-build/android.md)
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- [Build for Jetson](docs/en/01-how-to-build/jetsons.md)
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- [Build for SNPE](docs/en/01-how-to-build/snpe.md)
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- [Cross Build for aarch64](docs/en/01-how-to-build/cross_build_ncnn_aarch64.md)
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- User Guide
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- [How to convert model](docs/en/02-how-to-run/convert_model.md)
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- [How to write config](docs/en/02-how-to-run/write_config.md)
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- [How to profile model](docs/en/02-how-to-run/profile_model.md)
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- [How to quantize model](docs/en/02-how-to-run/quantize_model.md)
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- [Useful tools](docs/en/02-how-to-run/useful_tools.md)
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- Developer Guide
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- [Architecture](docs/en/07-developer-guide/architecture.md)
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- [How to support new models](docs/en/07-developer-guide/support_new_model.md)
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- [How to support new backends](docs/en/07-developer-guide/support_new_backend.md)
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- [How to partition model](docs/en/07-developer-guide/partition_model.md)
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- [How to test rewritten model](docs/en/07-developer-guide/test_rewritten_models.md)
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- [How to test backend ops](docs/en/07-developer-guide/add_backend_ops_unittest.md)
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- [How to do regression test](docs/en/07-developer-guide/regression_test.md)
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- Custom Backend Ops
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- [ncnn](docs/en/06-custom-ops/ncnn.md)
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- [onnxruntime](docs/en/06-custom-ops/onnxruntime.md)
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- [tensorrt](docs/en/06-custom-ops/tensorrt.md)
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- [FAQ](docs/en/faq.md)
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- [Contributing](.github/CONTRIBUTING.md)
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## Benchmark and Model zoo
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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).
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## Contributing
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We appreciate all contributions to 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 sincerely thank the following teams for their contributions to [MMDeploy](https://github.com/open-mmlab/mmdeploy):
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- [OpenPPL](https://github.com/openppl-public)
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- [OpenVINO](https://github.com/openvinotoolkit/openvino)
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- [ncnn](https://github.com/Tencent/ncnn)
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## Citation
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If you find this project useful in your research, please consider citing:
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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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## License
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This project is released under the [Apache 2.0 license](LICENSE).
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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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- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
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- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model 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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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning 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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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
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