Daisykit

DaisyKit is an easy AI toolkit with face mask detection, pose detection, background matting, barcode detection, face recognition and more. - with NCNN, OpenCV, Python wrappers

AI & Machine LearningC++Apache-2.0

Abstract

Daisykit is an open-source AI & Machine Learning project. DaisyKit is an easy AI toolkit with face mask detection, pose detection, background matting, barcode detection, face recognition and more. - with NCNN, OpenCV, Python wrappers. This issue can happen on development builds. Add -DCMAKE_BUILD_TYPE=Debug to cmake command and build again. It is built using C++, Computer Vision, Python. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

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

2. Objective

DaisyKit is an easy AI toolkit with face mask detection, pose detection, background matting, barcode detection, face recognition and more. - with NCNN, OpenCV, Python wrappers

This project demonstrates how C++, Computer Vision, Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

C++Computer VisionPython

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Arduino IDE / PlatformIO or a C++ compiler (g++)
  • Target board (e.g. Arduino, ESP32) where applicable
  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/nrl-ai/daisykit.git
cd daisykit
  1. DaisyKit SDK - C++, the core of models and algorithms in NCNN deep learning framework.
  2. DaisyKit Python wrapper for easy integration with Python.
  3. DaisyKit Android - Example app demonstrates how to use Daisykit SDK in Android.
  4. Python Package: https://pypi.org/project/daisykit/.
  5. Documentation: https://daisykit.nrl.ai/docs.
  6. Sponsor this project: https://github.com/sponsors/vietanhdev.

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by nrl-ai and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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