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* add yolov3 head to SDK * add yolov5 head to SDK * fix export-info and lint, add reverse check * fix lint * fix export info for yolo heads * add output_names to partition_config * fix typo * config * normalize config * fix * refactor config * fix lint and doc * c++ form * resolve comments * fix CI * fix CI * fix CI * float strides anchors * refine pipeline of rknn-int8 * config * rename func * refactor * rknn wrapper dict and fix typo * rknn wrapper output update, mmcls use end2end type * fix typo |
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README.md
English | 简体中文
Introduction
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.
Main features
Fully support OpenMMLab models
The currently supported codebases and models are as follows, and more will be included in the future
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
| Device / Platform | Linux | Windows | macOS | Android |
|---|---|---|---|---|
| x86_64 CPU | ✔️ONNX Runtime ✔️pplnn ✔️ncnn ✔️OpenVINO ✔️LibTorch |
✔️ONNX Runtime ✔️OpenVINO |
- | - |
| ARM CPU | ✔️ncnn | - | - | ✔️ncnn |
| RISC-V | ✔️ncnn | - | - | - |
| NVIDIA GPU | ✔️ONNX Runtime ✔️TensorRT ✔️pplnn ✔️LibTorch |
✔️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
Please read getting_started for the basic usage of MMDeploy. We also provide tutoials about:
- Build
- User Guide
- Developer Guide
- Custom Backend Ops
- FAQ
- Contributing
Benchmark and Model zoo
You can find the supported models from here and their performance in the benchmark.
Contributing
We appreciate all contributions to MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
Acknowledgement
We would like to sincerely thank the following teams for their contributions to MMDeploy:
Citation
If you find this project useful in your research, please consider citing:
@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.
Projects in OpenMMLab
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM installs OpenMMLab packages.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.