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Highlights

The VBTI development team is reviving MMLabs code, making it work with newer pytorch versions and fixing bugs. We are only a small team, so your help is appreciated.

Since most backends won't build/succeed anymore we have deleted them from the workflows. If you want to revive them, we need your support.

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

deploee offers over 2,300 AI models in ONNX, NCNN, TRT and OpenVINO formats. Featuring a built-in list of real hardware devices, deploee enables users to convert Torch models into any target inference format for profiling purposes.

Introduction

MMDeploy is an open-source deep learning model deployment toolset.

Main features

Fully support OneDL Lab models

The currently supported codebases and models are as follows, and more will be included in the future

• mmpretrain
• mmdet
• mmseg
• mmrotate

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	onnxruntime pplnn ncnn LibTorch OpenVINO TVM	onnxruntime OpenVINO ncnn	-	-
ARM CPU	ncnn	-	-	ncnn
RISC-V	ncnn	-	-	-
NVIDIA GPU	onnxruntime TensorRT LibTorch pplnn	onnxruntime TensorRT	-	-
NVIDIA Jetson	TensorRT	-	-	-
Huawei ascend310	CANN	-	-	-
Rockchip	RKNN	-	-	-
Apple M1	-	-	CoreML	-
Adreno GPU	-	-	-	SNPE ncnn
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
  • Build from Docker
  • Build from Script
  • Build for Linux
  • Build for macOS
  • Build for Win10
  • Build for Android
  • Build for Jetson
  • Build for SNPE
  • Cross Build for aarch64
• User Guide
  • How to convert model
  • How to write config
  • How to profile model
  • How to quantize model
  • Useful tools
• Developer Guide
  • Architecture
  • How to support new models
  • How to support new backends
  • How to partition model
  • How to test rewritten model
  • How to test backend ops
  • How to do regression test
• Custom Backend Ops
  • ncnn
  • ONNXRuntime
  • tensorrt
• 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:

• OpenPPL
• OpenVINO
• ncnn
• MMLabs

Citation

If you find this project useful in your research, please consider citing:

@misc{=mmdeploy,
    title={OneDL's Model Deployment Toolbox.},
    author={OneDL-MMDeploy Contributors},
    howpublished = {\url{https://github.com/vbti-development/onedl-mmdeploy}},
    year={2025}
}

License

This project is released under the Apache 2.0 license.

Projects in VBTI-development

• OneDL-MMEngine: Foundational library for training deep learning models.
• OneDL-MMCV: Foundational library for computer vision.
• OneDL-MMPreTrain: Pre-training toolbox and benchmark.
• OneDL-MMDetection: Detection toolbox and benchmark.
• OneDL-MMRotate: Rotated object detection toolbox and benchmark.
• OneDL-MMSegmentation: Semantic segmentation toolbox and benchmark.
• OneDL-MMDeploy: Model deployment framework.
• OneDL-MIM: MIM installs VBTI packages.