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 |
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.
The currently supported codebases and models are as follows, and more will be included in the future
The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.
The benchmark can be found from here
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
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
You can find the supported models from here and their performance in the benchmark.
We appreciate all contributions to MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
We would like to sincerely thank the following teams for their contributions to MMDeploy:
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}
}
This project is released under the Apache 2.0 license.
- 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.