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

## 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 | ✔️ONNX Runtime
✔️pplnn
✔️ncnn
✔️OpenVINO
✔️LibTorch
✔️TVM | ✔️ONNX Runtime
✔️OpenVINO | - | - | | ARM CPU | ✔️ncnn | - | - | ✔️ncnn | | RISC-V | ✔️ncnn | - | - | - | | NVIDIA GPU | ✔️ONNX Runtime
✔️TensorRT
✔️pplnn
✔️LibTorch
✔️TVM | ✔️ONNX Runtime
✔️TensorRT
✔️pplnn | - | - | | NVIDIA Jetson | ✔️TensorRT | ✔️TensorRT | - | - | | Huawei ascend310 | ✔️CANN | - | - | - | | Rockchip | ✔️RKNN | - | - | - | | Apple M1 | - | - | ✔️CoreML | - | | Adreno GPU | - | - | - | ✔️ncnn
✔️SNPE | | Hexagon DSP | - | - | - | ✔️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/main/) 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 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) - [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.