Daisykit - D.A.I.S.Y: Deploy AI Systems Yourself!

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Daisykit (opens in a new tab) is an easy AI toolkit with face mask detection, pose detection, background matting, barcode detection and more. This open source project includes:

  • Daisykit SDK - C++, the core of models and algorithms in NCNN deep learning framework.
  • Daisykit Python wrapper for easy integration with Python.
  • Daisykit Android - Example app demonstrate how to use Daisykit SDK in Android.


Demo Video: (opens in a new tab).

1. Environment Setup


Install packages from Terminal

sudo apt install -y build-essential libopencv-dev
sudo apt install -y libvulkan-dev vulkan-utils
sudo apt install -y mesa-vulkan-drivers # For Intel GPU support


For Windows, Visual Studio 2019 + Git Bash is recommended.

2. Build and run C++ examples

Clone the source code:

git clone --recursive
cd daisykit


Build Daisykit:

mkdir build
cd build
cmake .. -Dncnn_FIND_PATH="<path to ncnn lib>"

Run face detection example:


If you dont specify ncnn_FIND_PATH, NCNN will be built from scratch.


Build Daisykit:

mkdir build
cd build
cmake -G "Visual Studio 16 2019" -Dncnn_FIND_PATH="<path to ncnn lib>" ..
cmake --build . --config Release

Run face detection example:


3. C++ Coding convention

Read coding convention and contribution guidelines here.

4. Known issues and problems

  • Slow model inference - Low FPS

This issue can happen on development build. Add -DCMAKE_BUILD_TYPE=Debug to cmake command and build again. The FPS can be much better.

5. References

This toolkit is developed on top of other source code. Including