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📌AiDB : A toolbox for deep learning model deployment using C++. Abstract mainstream deep learning inference frameworks into unified interfaces, including ONNXRUNTIME, MNN, NCNN, TNN, PaddleLite, and OpenVINO. Provide deployment demo for multiple scenarios and languages. This project is not only used to record learning, but also a summary of my work over the past few years. If this project inspires you or helps you, welcome ⭐Star⭐ to support me, which is the driving force for me to keep updating! 🍔

📌Article

AiDB:一个集合了6大推理框架的AI工具箱 | 加速你的模型部署

知乎 | GiantPandaCV

如何使用“LoRa”的方式加载ONNX模型：StableDiffusion相关模型的C++推理

知乎 | GiantPandaCV

https://github.com/TalkUHulk/ai.deploy.box/assets/18474163/f9a03178-8d50-4627-9c61-21e99734a37a

https://github.com/TalkUHulk/ai.deploy.box/assets/18474163/6f8136b7-07fa-4d39-97ef-797d6f6c37de

Features 🍉🍉

rich backend: integrating mainstream AI inference frameworks, including OnnxRuntime, MNN, NCNN, TNN, PaddleLite, and OpenVINO;
user friendly: abstracting all inference frameworks into a unified interface and selecting them through configuration files for ease of use;
multiple scenarios: support Linux、MacOS、Android(Win64 coming soon、IOS--poverty has limited my work)，provide PC Demo(Qt) 、 Android Demo(Kotlin) 、 Lua Demo and minimal server deployment demo( Go Zeros and Python FastAPI )
multiple languages: provide calling instances for Python, Lua, and Go;

Demo 🍓🍓

Try out the web demo:
Run the C++ demo on Google Colab:
Run the Python demo on Google Colab:
Run the Go demo on Google Colab:
Run the Lua demo on Google Colab:

Demo Repo 🍇🍇

Contents 📖💡

Features
Demo
Demo Repo
FrameWork
Quick Start
- Environment
- Build
- Models
- Usage
Sample Usage
- Face Detect
- Face Landmark
- Face Alignment
- Face Parsing
- OCR
- YoloX
- YoloV7
- YoloV8
- MobileVit
- MoveNet
- MobileStyleGan
- AnimeGan
Pitfalls
Maintainers
Contributing
- Contributors
LICENSE

FrameWork 📝 📝

There are two modes available, S mode(C api) and H mode(C++ api)

Quick Start ⚡⚡

environment

<details> <summary>Recommend using Docker</summary>

docker pull mister5ive/ai.deploy.box

</details>

Build

Docker(recommend)

git lfs clone https://github.com/TalkUHulk/ai.deploy.box.git
cd ai.deploy.box.git
docker build -t aidb-dev .
docker run -it --name aidb-test aidb-dev

Build from source code

git lfs clone https://github.com/TalkUHulk/ai.deploy.box.git
cd ai.deploy.box.git
mkdir build && cd build
cmake .. -DC_API={ON/OFF} -DBUILD_SAMPLE={ON/OFF} -DBUILD_PYTHON={ON/OFF} -DBUILD_LUA={ON/OFF} -DENGINE_NCNN_WASM={ON/OFF} -DOPENCV_HAS_FREETYPE={ON/OFF} -DENGINE_MNN={ON/OFF} -DENGINE_ORT={ON/OFF} -DENGINE_NCNN={ON/OFF} -DENGINE_TNN={ON/OFF} -DENGINE_OPV={ON/OFF} -DENGINE_PPLite={ON/OFF}
make -j8

C_API: compile C library;
BUILD_PYTHON: compile Python api;
BUILD_LUA: compile Lua api;
OPENCV_HAS_FREETYPE: opencv-contrib complied with freetype or not. cv::putText can put chinese;
BUILD_SAMPLE: compile sample;
ENGINE_NCNN_WASM: compile sample;
ENGINE_MNN: enable mnn;
ENGINE_ORT: enable onnxruntim;
ENGINE_NCNN: enable ncnn;
ENGINE_TNN: enable tnn;
ENGINE_OPV: enable openvino;
ENGINE_PPLite: enable paddle-lite;
ENABLE_SD: enable stable diffusion;

Models

Model Lite: MEGA | Baidu: 92e8

[News!!!] All models convert to 🤗.

<details> <summary>Model List</summary>

AI.deploy.box

Install / Use

README