[![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url]

Tutorial of AI Kit with Raspberry Pi From Zero to Hero

- Play your AI Kit from Beginner to Expert -

Report Bug | Request Feature

🛠️ About The Project

This course is designed to teach you how to harness the power of AI on the Raspberry Pi, with a particular focus on using an AI kit to perform essential computer vision tasks. Throughout the course, you'll learn how to integrate AI into real-world IoT (Internet of Things) applications, from object detection and image classification to more complex visual recognition tasks. We will guide you step-by-step through setting up your Raspberry Pi, using AI frameworks, and deploying these models in various practical scenarios. Whether you are a hobbyist, a student, or a professional, this course will provide you with the foundational knowledge and hands-on experience necessary to bring AI-driven solutions to life on resource-constrained devices like the Raspberry Pi.

📚 Pre-requisites

For Vision&LLM object

| reComputer AI R2130 | | :----------------------------------------------------------------------------------------------------------------: | | | | Purchase Now |

For LLM object

| Raspberry Pi 5 Starter Kit | | :----------------------------------------------------------------------------------------------------------------: | | | | Purchase Now |

For AIoT objects

| Raspberry Pi AI Kit | reComputer R1100 | | :----------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------: | | | | | Purchase Now | Purchase Now |

📚 Recommended Reading

Machine Learning

Introduction to Machine Learning with Python Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Computer Vision

Programming Computer Vision with Python Deep Learning for Computer Vision

Large Language Model

Deep Learning for Natural Language Processing: Creating Neural Networks with Python

🧱 Built With

• [![Raspberry Pi][Raspberry Pi.js]][Raspberry Pi-url]

• [![Seeed Studio][Seeed Studio.js]][Seeed Studio-url]

• [![HAILO][HAILO.js]][HAILO-url]

• [![Python][Python.js]][Python-url]

• [![Node Red][Node Red.js]][Node Red-url]

• [![TensorFlow][TensorFlow.com]][TensorFlow-url]

• [![OpenCV][OpenCV.com]][OpenCV-url]

• [![Pytorch][Pytorch.com]][Pytorch-url]

🗺️ Roadmap

⏳ Indicates in progress, ✔️ indicates completed.

Chapter 1 [In Progress, Expected Completion: November 2024]

• ✔️ Introduction of Artificial Intelligence

• ✔️ Introduction of Deep Neural Network

• ✔️ Introduction of Convolutional Neural Network

• ✔️ Introduction of Computer Vision

• ✔️ Introduction of Large Language Model

Chapter 2 [In Progress, Expected Completion: December 2024]

• ✔️ Introduction to Pytorch in Raspberry Pi Environment

• ✔️ Introduction to TensorFlow in Raspberry Pi Environment

• ✔️ Introduction to OpenCV in Raspberry Pi Environment

• ✔️ Introduction to Ultralytics in Raspberry Pi Environment

• ✔️ Introduction to Hailo in Raspberry Pi Environment

Chapter 3 [In Progress, Expected Completion: January 2025]

• ✔️ Running AI Tasks with Hailo -With AI Kit

• ✔️ Deploying Custom AI Models Across Applications with Hailo NPU

• ✔️ Run Clip Application with Hailo NPU

Chapter 4 [Completed: November 2024]

• ✔️ Setup Ollama on Raspberry Pi

• ✔️ Run Llama on Raspberry Pi

• ✔️ Run Gemma2 on RaspberryPi

• ✔️ Run Phi3.5 on RaspberryPi

• ✔️ Run Multimodal on RaspberryPi

• ✔️ Use Ollama with Python

Chapter 5 [In Progress, Expected Completion: December 2024]

• ✔️ Training

• ✔️ Converting

• ✔️ Deploying

Chapter 6

• ✔️ Smart Retail with reComputerR11 and AI kit

• ✔️ Hailo-Powered Car Park Management with ThingsBoard

Open for everyone to contribute See the open issues for a full list of proposed features (and known issues).

🤝 Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. If you have a suggestion that would make this better, please follow this Contributor Guidelines and contribute your own code. Don't forget to give the project a star! Thanks again!

💞 Top contributors:

📄 License

Distributed under the MIT License. See LICENSE for more information.

🌟 Star History

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