fd07b26c2e
* [FEATURE] add mmpose configs for compatibility with Hourglass model [Motivation] - The added configs are aim to support Hourglass pose-detection model in ncnn backend. - Hourglass model needs explicit input shape with 256x256 or 384x384. - Hourglass model can be quantitized to int8 successfully in ncnn backend. [Complementary] The mmpose config of Hourglass mode is in: - https://github.com/open-mmlab/mmpose/blob/master/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hourglass52_coco_256x256.py - https://github.com/open-mmlab/mmpose/blob/master/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hourglass52_coco_384x384.py Quantitization results are shown in https://github.com/Qingrenn/mmdeploy-summer-camp/blob/main/week4/result.md * Keep only one config (pose-detection_ncnn-int8_static-256x256.py) and remove the others. |
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| demo | ||
| docker | ||
| docs | ||
| mmdeploy | ||
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| third_party | ||
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| README.md | ||
| README_zh-CN.md | ||
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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
Models can be exported and run in the following backends, and more will be compatible
| ONNX Runtime | TensorRT | ppl.nn | ncnn | OpenVINO | LibTorch | snpe | Ascend | Core ML | RKNN | more |
|---|---|---|---|---|---|---|---|---|---|---|
| ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | benchmark |
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.
- 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.