Merge pull request #51 from KasunThushara/main

Update readme and overview

Author: KasunThushara

README.md

@@ -108,10 +108,13 @@ This course is designed to teach you how to harness the power of AI on the Raspb
 - ✔️ 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
+- ✔️ Introduction to Hailo in Raspberry Pi Environment
 
 ### Chapter 3 [In Progress, Excepted 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 RaspberryPi
@@ -129,6 +132,9 @@ This course is designed to teach you how to harness the power of AI on the Raspb
 
 ### 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](https://github.com/Seeed-Projects/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/issues) for a full list of proposed features (and known issues).

articles/Overview.md

@@ -80,11 +80,22 @@ Here, you’ll get hands-on experience setting up your Raspberry Pi for AI proje
 |-------|-------------|
 | [Introduction to OpenCV in Raspberry Pi Environment](./Chapter_2-Configuring_the_RaspberryPi_Environment/Introduction_to_OpenCV) | Learn how to set up and use OpenCV on the Raspberry Pi for computer vision projects, from installation to basic functions. |
 | [Introduction to TensorFlow in Raspberry Pi Environment](./Chapter_2-Configuring_the_RaspberryPi_Environment/Introduction_to_TensorFlow_in_Raspberry_Pi_Environment) | Discover the setup and basics of TensorFlow on Raspberry Pi, enabling AI model deployment on a resource-constrained device. |
+| [Introduction to Pytorch in Raspberry Pi Environment](./Chapter_2-Configuring_the_RaspberryPi_Environment/Introduction_to_Pytorch_in_Raspberry_Pi_Environment) | Explore how to set up PyTorch on Raspberry Pi, enabling deep learning model training and inference on an edge device.|
+| [Introduction to Ultralytics in Raspberry Pi Environment](./Chapter_2-Configuring_the_RaspberryPi_Environment/Introduction_to_Ultralytics_in_Raspberry_Pi_Environment) |Learn how to use Ultralytics YOLO models on Raspberry Pi for object detection and tracking in real-world applications.|
+| [Introduction to Hailo in Raspberry Pi Environment](./Chapter_2-Configuring_the_RaspberryPi_Environment/Introduction_to_Hailo_in_Raspberry_Pi_Environment) |Get started with the Hailo AI accelerator on Raspberry Pi, covering installation, setup, and performance benefits for AI applications.|
+
 
 ### Chapter 3: Computer Vision Projects and Practical Applications
 
 This chapter moves into practical applications, starting with simple object detection tasks (like identifying specific objects with a trained model). You’ll work on a hands-on project: building an Intelligent Monitoring System that sends an alarm and screenshot via email when a person is detected.
 
+| Topic | Description |
+|-------|-------------|
+|[Running AI Tasks with Hailo -With AI Kit](./Chapter_3-Computer_Vision_Projects_and_Practical_Applications/Run_Yolov8_on_Hailo_Environment)|Learn how to accelerate AI tasks on Raspberry Pi using the Hailo AI Kit, enabling faster and more efficient deep learning inference.|
+|[Deploying Custom AI Models Across Applications](./Chapter_3-Computer_Vision_Projects_and_Practical_Applications/Run_Custom_Models_with_Hailo_NPU)|Discover how to convert, optimize, and deploy custom AI models on the Hailo NPU for various real-world applications.|
+|[Run Clip Application with Hailo NPU](./Chapter_3-Computer_Vision_Projects_and_Practical_Applications/Run_Clip_Application_with_Hailo_NPU)|Implement OpenAI's CLIP model with the Hailo NPU on Raspberry Pi, enabling image-text understanding and classification tasks.|
+
+
 ### Chapter 4: Large Language Models (LLMs)
 
 Here, you’ll explore lightweight but powerful large language models, focusing on Ollama, an open-source framework compatible with Raspberry Pi. We’ll also introduce models like Meta's LLaMA, Google’s Gemini, and Microsoft’s Phi, alongside libraries and Python APIs to run these models on the Raspberry Pi.
@@ -111,3 +122,8 @@ In this chapter, we’ll dive into creating a custom model with Hailo using your
 ### Chapter 6: Raspberry Pi and AIoT
 
 Finally, we’ll explore integrating AI and IoT (AIoT) by connecting to platforms like Node-RED, ThingsBoard, and Home Assistant. This chapter covers real-time applications embedding computer vision, such as smart retail, security systems, smart parking management, and IoT integrations with large language models for tasks like anomaly detection.
+
+| Topic | Description |
+|-------|-------------|
+|[Smart Retail with reComputerR11 and AI kit](./Chapter_6-RaspberryPi_and_AIoT/Smart_Retail_with_reComputerR11_and_AIkit)|Explore how AI-powered solutions using reComputer R11 and the Hailo AI Kit can enhance smart retail applications, such as customer analytics and automated checkout.|
+|[Hailo-Powered Car Park Management with ThingsBoard](./Chapter_6-RaspberryPi_and_AIoT/Smart_Retail_with_reComputerR11_and_AIkit)|Learn how to integrate the Hailo AI Kit with ThingsBoard to build an intelligent car parking management system using Raspberry Pi.|