<div align="center">
<img src="resources/mmdeploy-logo.png" width="450"/>
<div> </div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div> </div>
</div>
[](https://mmdeploy.readthedocs.io/en/latest/)
[](https://github.com/open-mmlab/mmdeploy/actions)
[](https://codecov.io/gh/open-mmlab/mmdeploy)
[](https://github.com/open-mmlab/mmdeploy/blob/master/LICENSE)
[](https://github.com/open-mmlab/mmdeploy/issues)
[](https://github.com/open-mmlab/mmdeploy/issues)
English | [简体中文](README_zh-CN.md)
## Highlights
The MMDeploy 1.x has been released, which is adapted to upstream codebases from OpenMMLab 2.0. Please **align the version** when using it.
The default branch has been switched to `main` from `master`. MMDeploy 0.x (`master`) will be deprecated and new features will only be added to MMDeploy 1.x (`main`) in future.
| mmdeploy | mmengine | mmcv | mmdet | others |
| :------: | :------: | :------: | :------: | :----: |
| 0.x.y | - | \<=1.x.y | \<=2.x.y | 0.x.y |
| 1.x.y | 0.x.y | 2.x.y | 3.x.y | 1.x.y |
## Introduction
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
<div align="center">
<img src="resources/introduction.png">
</div>
## 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)
- [mmaction2](docs/en/04-supported-codebases/mmaction2.md)
### Multiple inference backends are available
The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.
The benchmark can be found from [here](docs/en/03-benchmark/benchmark.md)
| Device / Platform | Linux | Windows | macOS | Android |
| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
| x86_64 CPU | [![Build Status][pass-backend-ort]][ci-backend-ort]ONNXRuntime<br>[![Build Status][pass-backend-pplnn]][ci-backend-pplnn]pplnn<br>[![Build Status][pass-backend-ncnn]][ci-backend-ncnn]ncnn<br>[![Build Status][pass-backend-torchscript]][ci-backend-torchscript]LibTorch<br>[![Build Status][pass-build-rknpu]][ci-build-rknpu]OpenVINO<br>[![Build Status][pass-build-tvm]][ci-build-tvm]TVM | ![][pass-no-status]ONNXRuntime<br>![][pass-no-status]OpenVINO | - | - |
| ARM CPU | [![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn | - | - | [![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn |
| RISC-V | [![Build Status][pass-build-riscv64-gcc]][ci-build-riscv64-gcc]ncnn | - | - | - |
| NVIDIA GPU | ![Build Status][pass-no-status]ONNXRuntime<br>![Build Status][pass-no-status]TensorRT<br>![Build Status][pass-no-status]pplnn<br>![Build Status][pass-no-status]LibTorch<br>![Build Status][pass-no-status]TVM | ![Build Status][pass-no-status]ONNXRuntime<br>![Build Status][pass-no-status]TensorRT<br>![Build Status][pass-no-status]pplnn | - | - |
| NVIDIA Jetson | [![Build Status][pass-build-rknpu]][ci-build-rknpu]TensorRT | - | - | - |
| Huawei ascend310 | [![Build Status][pass-backend-ascend]][ci-backend-ascend]CANN | - | - | - |
| Rockchip | [![Build Status][pass-backend-rknn]][ci-backend-rknn]RKNN | - | - | - |
| Apple M1 | - | - | [![Build Status][pass-backend-coreml]][ci-backend-coreml]CoreML | - |
| Adreno GPU | - | - | - | [![Build Status][pass-backend-snpe]][ci-backend-snpe]SNPE<br>[![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn |
| Hexagon DSP | - | - | - | [![Build Status][pass-backend-snpe]][ci-backend-snpe]SNPE |
```
|
```
### 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
## [Documentation](https://mmdeploy.readthedocs.io/en/latest/)
Please read [getting_started](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 from Script](docs/en/01-how-to-build/build_from_script.md)
- [Build for Linux](docs/en/01-how-to-build/linux-x86_64.md)
- [Build for Windows](docs/en/01-how-to-build/windows.md)
- [Build for macOS](docs/en/01-how-to-build/macos-arm64.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)
- [Build for SNPE](docs/en/01-how-to-build/snpe.md)
- [Build for Rockchip](docs/en/01-how-to-build/rockchip.md)
- [Cross Build for aarch64](docs/en/01-how-to-build/cross_build_ncnn_aarch64.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 profile model](docs/en/02-how-to-run/profile_model.md)
- [How to quantize model](docs/en/02-how-to-run/quantize_model.md)
- [Useful tools](docs/en/02-how-to-run/useful_tools.md)
- Developer Guide
- [Architecture](docs/en/07-developer-guide/architecture.md)
- [How to support new models](docs/en/07-developer-guide/support_new_model.md)
- [How to support new backends](docs/en/07-developer-guide/support_new_backend.md)
- [How to partition model](docs/en/07-developer-guide/partition_model.md)
- [How to test rewritten model](docs/en/07-developer-guide/test_rewritten_models.md)
- [How to test backend ops](docs/en/07-developer-guide/add_backend_ops_unittest.md)
- [How to do regression test](docs/en/07-developer-guide/regression_test.md)
- Custom Backend Ops
- [ncnn](docs/en/06-custom-ops/ncnn.md)
- [onnxruntime](docs/en/06-custom-ops/onnxruntime.md)
- [tensorrt](docs/en/06-custom-ops/tensorrt.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.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark
- [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.
[ci-backend-ascend]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ascend.yml
[ci-backend-coreml]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-coreml.yml
[ci-backend-ncnn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ncnn.yml
[ci-backend-ort]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ort.yml
[ci-backend-pplnn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-pplnn.yml
[ci-backend-rknn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-rknn.yml
[ci-backend-snpe]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-snpe.yml
[ci-backend-torchscript]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-torchscript.yml
[ci-build-riscv64-gcc]: https://github.com/open-mmlab/mmdeploy/actions/workflows/linux-riscv64-gcc.yml
[ci-build-rknpu]: https://github.com/open-mmlab/mmdeploy/actions/workflows/linux-rknpu.yml
[ci-build-tvm]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-tvm.yml
[pass-backend-ascend]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ascend.yml?branch=master
[pass-backend-coreml]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-coreml.yml?branch=master
[pass-backend-ncnn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ncnn.yml?branch=master
[pass-backend-ort]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ort.yml?branch=master
[pass-backend-pplnn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-pplnn.yml?branch=master
[pass-backend-rknn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-rknn.yml?branch=master
[pass-backend-snpe]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-snpe.yml?branch=master
[pass-backend-torchscript]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ort.yml?branch=master
[pass-build-riscv64-gcc]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/linux-riscv64-gcc.yml?branch=master
[pass-build-rknpu]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-rknn.yml?branch=master
[pass-build-tvm]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-tvm.yml?branch=master
[pass-no-status]: https://img.shields.io/badge/build-no%20status-lightgrey
Raw data
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"description": "<div align=\"center\">\n <img src=\"resources/mmdeploy-logo.png\" width=\"450\"/>\n <div> </div>\n <div align=\"center\">\n <b><font size=\"5\">OpenMMLab website</font></b>\n <sup>\n <a href=\"https://openmmlab.com\">\n <i><font size=\"4\">HOT</font></i>\n </a>\n </sup>\n \n <b><font size=\"5\">OpenMMLab platform</font></b>\n <sup>\n <a href=\"https://platform.openmmlab.com\">\n <i><font size=\"4\">TRY IT OUT</font></i>\n </a>\n </sup>\n </div>\n <div> </div>\n</div>\n\n[](https://mmdeploy.readthedocs.io/en/latest/)\n[](https://github.com/open-mmlab/mmdeploy/actions)\n[](https://codecov.io/gh/open-mmlab/mmdeploy)\n[](https://github.com/open-mmlab/mmdeploy/blob/master/LICENSE)\n[](https://github.com/open-mmlab/mmdeploy/issues)\n[](https://github.com/open-mmlab/mmdeploy/issues)\n\nEnglish | [\u7b80\u4f53\u4e2d\u6587](README_zh-CN.md)\n\n## Highlights\n\nThe MMDeploy 1.x has been released, which is adapted to upstream codebases from OpenMMLab 2.0. Please **align the version** when using it.\nThe default branch has been switched to `main` from `master`. MMDeploy 0.x (`master`) will be deprecated and new features will only be added to MMDeploy 1.x (`main`) in future.\n\n| mmdeploy | mmengine | mmcv | mmdet | others |\n| :------: | :------: | :------: | :------: | :----: |\n| 0.x.y | - | \\<=1.x.y | \\<=2.x.y | 0.x.y |\n| 1.x.y | 0.x.y | 2.x.y | 3.x.y | 1.x.y |\n\n## Introduction\n\nMMDeploy is an open-source deep learning model deployment toolset. It is a part of the [OpenMMLab](https://openmmlab.com/) project.\n\n<div align=\"center\">\n <img src=\"resources/introduction.png\">\n</div>\n\n## Main features\n\n### Fully support OpenMMLab models\n\nThe currently supported codebases and models are as follows, and more will be included in the future\n\n- [mmcls](docs/en/04-supported-codebases/mmcls.md)\n- [mmdet](docs/en/04-supported-codebases/mmdet.md)\n- [mmseg](docs/en/04-supported-codebases/mmseg.md)\n- [mmedit](docs/en/04-supported-codebases/mmedit.md)\n- [mmocr](docs/en/04-supported-codebases/mmocr.md)\n- [mmpose](docs/en/04-supported-codebases/mmpose.md)\n- [mmdet3d](docs/en/04-supported-codebases/mmdet3d.md)\n- [mmrotate](docs/en/04-supported-codebases/mmrotate.md)\n- [mmaction2](docs/en/04-supported-codebases/mmaction2.md)\n\n### Multiple inference backends are available\n\nThe supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.\n\nThe benchmark can be found from [here](docs/en/03-benchmark/benchmark.md)\n\n| Device / Platform | Linux | Windows | macOS | Android |\n| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |\n| x86_64 CPU | [![Build Status][pass-backend-ort]][ci-backend-ort]ONNXRuntime<br>[![Build Status][pass-backend-pplnn]][ci-backend-pplnn]pplnn<br>[![Build Status][pass-backend-ncnn]][ci-backend-ncnn]ncnn<br>[![Build Status][pass-backend-torchscript]][ci-backend-torchscript]LibTorch<br>[![Build Status][pass-build-rknpu]][ci-build-rknpu]OpenVINO<br>[![Build Status][pass-build-tvm]][ci-build-tvm]TVM | ![][pass-no-status]ONNXRuntime<br>![][pass-no-status]OpenVINO | - | - |\n| ARM CPU | [![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn | - | - | [![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn |\n| RISC-V | [![Build Status][pass-build-riscv64-gcc]][ci-build-riscv64-gcc]ncnn | - | - | - |\n| NVIDIA GPU | ![Build Status][pass-no-status]ONNXRuntime<br>![Build Status][pass-no-status]TensorRT<br>![Build Status][pass-no-status]pplnn<br>![Build Status][pass-no-status]LibTorch<br>![Build Status][pass-no-status]TVM | ![Build Status][pass-no-status]ONNXRuntime<br>![Build Status][pass-no-status]TensorRT<br>![Build Status][pass-no-status]pplnn | - | - |\n| NVIDIA Jetson | [![Build Status][pass-build-rknpu]][ci-build-rknpu]TensorRT | - | - | - |\n| Huawei ascend310 | [![Build Status][pass-backend-ascend]][ci-backend-ascend]CANN | - | - | - |\n| Rockchip | [![Build Status][pass-backend-rknn]][ci-backend-rknn]RKNN | - | - | - |\n| Apple M1 | - | - | [![Build Status][pass-backend-coreml]][ci-backend-coreml]CoreML | - |\n| Adreno GPU | - | - | - | [![Build Status][pass-backend-snpe]][ci-backend-snpe]SNPE<br>[![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn |\n| Hexagon DSP | - | - | - | [![Build Status][pass-backend-snpe]][ci-backend-snpe]SNPE |\n\n```\n |\n```\n\n### Efficient and scalable C/C++ SDK Framework\n\nAll 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\n\n## [Documentation](https://mmdeploy.readthedocs.io/en/latest/)\n\nPlease read [getting_started](docs/en/get_started.md) for the basic usage of MMDeploy. We also provide tutoials about:\n\n- [Build](docs/en/01-how-to-build/build_from_source.md)\n - [Build from Docker](docs/en/01-how-to-build/build_from_docker.md)\n - [Build from Script](docs/en/01-how-to-build/build_from_script.md)\n - [Build for Linux](docs/en/01-how-to-build/linux-x86_64.md)\n - [Build for Windows](docs/en/01-how-to-build/windows.md)\n - [Build for macOS](docs/en/01-how-to-build/macos-arm64.md)\n - [Build for Win10](docs/en/01-how-to-build/windows.md)\n - [Build for Android](docs/en/01-how-to-build/android.md)\n - [Build for Jetson](docs/en/01-how-to-build/jetsons.md)\n - [Build for SNPE](docs/en/01-how-to-build/snpe.md)\n - [Build for Rockchip](docs/en/01-how-to-build/rockchip.md)\n - [Cross Build for aarch64](docs/en/01-how-to-build/cross_build_ncnn_aarch64.md)\n- User Guide\n - [How to convert model](docs/en/02-how-to-run/convert_model.md)\n - [How to write config](docs/en/02-how-to-run/write_config.md)\n - [How to profile model](docs/en/02-how-to-run/profile_model.md)\n - [How to quantize model](docs/en/02-how-to-run/quantize_model.md)\n - [Useful tools](docs/en/02-how-to-run/useful_tools.md)\n- Developer Guide\n - [Architecture](docs/en/07-developer-guide/architecture.md)\n - [How to support new models](docs/en/07-developer-guide/support_new_model.md)\n - [How to support new backends](docs/en/07-developer-guide/support_new_backend.md)\n - [How to partition model](docs/en/07-developer-guide/partition_model.md)\n - [How to test rewritten model](docs/en/07-developer-guide/test_rewritten_models.md)\n - [How to test backend ops](docs/en/07-developer-guide/add_backend_ops_unittest.md)\n - [How to do regression test](docs/en/07-developer-guide/regression_test.md)\n- Custom Backend Ops\n - [ncnn](docs/en/06-custom-ops/ncnn.md)\n - [onnxruntime](docs/en/06-custom-ops/onnxruntime.md)\n - [tensorrt](docs/en/06-custom-ops/tensorrt.md)\n- [FAQ](docs/en/faq.md)\n- [Contributing](.github/CONTRIBUTING.md)\n\n## Benchmark and Model zoo\n\nYou 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).\n\n## Contributing\n\nWe appreciate all contributions to MMDeploy. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.\n\n## Acknowledgement\n\nWe would like to sincerely thank the following teams for their contributions to [MMDeploy](https://github.com/open-mmlab/mmdeploy):\n\n- [OpenPPL](https://github.com/openppl-public)\n- [OpenVINO](https://github.com/openvinotoolkit/openvino)\n- [ncnn](https://github.com/Tencent/ncnn)\n\n## Citation\n\nIf you find this project useful in your research, please consider citing:\n\n```BibTeX\n@misc{=mmdeploy,\n title={OpenMMLab's Model Deployment Toolbox.},\n author={MMDeploy Contributors},\n howpublished = {\\url{https://github.com/open-mmlab/mmdeploy}},\n year={2021}\n}\n```\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## Projects in OpenMMLab\n\n- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.\n- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.\n- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.\n- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.\n- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark\n- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.\n- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.\n- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.\n- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.\n\n[ci-backend-ascend]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ascend.yml\n[ci-backend-coreml]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-coreml.yml\n[ci-backend-ncnn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ncnn.yml\n[ci-backend-ort]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ort.yml\n[ci-backend-pplnn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-pplnn.yml\n[ci-backend-rknn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-rknn.yml\n[ci-backend-snpe]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-snpe.yml\n[ci-backend-torchscript]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-torchscript.yml\n[ci-build-riscv64-gcc]: https://github.com/open-mmlab/mmdeploy/actions/workflows/linux-riscv64-gcc.yml\n[ci-build-rknpu]: https://github.com/open-mmlab/mmdeploy/actions/workflows/linux-rknpu.yml\n[ci-build-tvm]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-tvm.yml\n[pass-backend-ascend]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ascend.yml?branch=master\n[pass-backend-coreml]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-coreml.yml?branch=master\n[pass-backend-ncnn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ncnn.yml?branch=master\n[pass-backend-ort]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ort.yml?branch=master\n[pass-backend-pplnn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-pplnn.yml?branch=master\n[pass-backend-rknn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-rknn.yml?branch=master\n[pass-backend-snpe]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-snpe.yml?branch=master\n[pass-backend-torchscript]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ort.yml?branch=master\n[pass-build-riscv64-gcc]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/linux-riscv64-gcc.yml?branch=master\n[pass-build-rknpu]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-rknn.yml?branch=master\n[pass-build-tvm]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-tvm.yml?branch=master\n[pass-no-status]: https://img.shields.io/badge/build-no%20status-lightgrey\n",
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