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Copy file name to clipboardExpand all lines: contents/labs/getting_started.qmd
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- The Arduino Nicla Vision is a powerful, compact board designed for professional-grade computer vision and audio applications. It features a high-quality camera module, a digital microphone, and an IMU, making it suitable for demanding projects in industries such as robotics, automation, and surveillance.
- The Seeed Studio XIAO ESP32S3 Sense is a tiny, feature-packed board designed for makers, hobbyists, and students interested in exploring edge AI applications. It comes with a camera, microphone, and IMU, making it easy to get started with projects like image classification, keyword spotting, and motion detection.
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- The Seeed Studio XIAO ESP32S3 Sense is a tiny, feature-packed board designed for makers, hobbyists, and students interested in exploring edge AI applications. It comes equipped with a camera, microphone, and IMU, making it easy to get started with projects such as image classification, keyword spotting, and motion detection.
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-[XIAO ESP32S3 Sense specifications](https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/#specification)
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-[XIAO ESP32S3 Sense pinout diagram](https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/#hardware-overview)
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3.**Raspberry Pi Single Computer board**
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3.**Grove Vision AI V2 board**
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- The Seeed Studio Grove Vision AI V2 is a compact, low-power, yet powerful device. It is an MCU-based system powered by the Arm Cortex-M55 and vision AI module Ethos-U55. It supports TensorFlow and PyTorch frameworks and is compatible with the Arduino IDE. With the SenseCraft AI algorithm platform, trained machine learning (ML) models can be deployed to the sensor without the need for coding. It features a standard CSI interface, an onboard digital microphone, and an SD card slot, making it highly suitable for various embedded AI vision projects.
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-[Grove Vision AI V2 features and hardware overview](https://wiki.seeedstudio.com/grove_vision_ai_v2/)
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4.**Raspberry Pi Single Board Computer**
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- The Raspberry Pi is a powerful and versatile single-board computer that has become an essential tool for engineers across various disciplines. Developed by the [Raspberry Pi Foundation](https://www.raspberrypi.org/), these compact devices offer a unique combination of affordability, computational power, and extensive GPIO (General Purpose Input/Output) capabilities, making them ideal for prototyping, embedded systems development, and advanced engineering projects.
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-[Raspberry Pi Hardware Documentation](https://www.raspberrypi.com/documentation/computers/raspberry-pi.html)
- USB-C cable for programming and powering the XIAO
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5.**Additional accessories**
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- USB-C cable for programming and powering the XIAO and the Grove Vision AI V2
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- Micro-USB cable for programming and powering the Nicla
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- Power Supply for the Raspberries
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- Breadboard and jumper wires (optional, for connecting additional sensors)
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The Arduino Nicla Vision is tailored for professional-grade applications, offering advanced features and performance suitable for demanding industrial projects. On the other hand, the Seeed Studio XIAO ESP32S3 Sense is geared toward makers, hobbyists, and students who want to explore edge AI applications in a more accessible and beginner-friendly format. Both boards have their strengths and target audiences, allowing users to choose the best fit for their needs and skill level. The Raspberry Pi is aimed at more advanced engineering and machine learning projects.
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The Arduino Nicla Vision is tailored for professional-grade applications, offering advanced features and performance suitable for demanding industrial projects. On the other hand, the Seeed Studio XIAO ESP32S3 Sense is geared toward makers, hobbyists, and students who want to explore edge AI applications in a more accessible and beginner-friendly format. Both boards have their strengths and target audiences, allowing users to choose the best fit for their needs and skill level. The Grove Vision AI V2 and the Raspberry Pi are aimed at more advanced engineering and machine learning projects.
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- For the XIAO ESP32S3 Sense, you can use the onboard Wi-Fi module or connect an external Wi-Fi or Ethernet module using the available pins.
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- For the Grove Vision AI V2, you can use the onboard Wi-Fi module on the Master Controller (for example the XIAO ESP32S3) or connect an external Wi-Fi or Ethernet module using the available pins.
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- For the Raspberry Pi, you can use the onboard Wi-Fi module to connect an external Wi-Fi or Ethernet module using the available connector.
Copy file name to clipboardExpand all lines: contents/labs/overview.qmd
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# Overview {.unnumbered}
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Welcome to the hands-on labs section where you'll explore deploying ML models onto real embedded devices, which will offer a practical introduction to ML systems. Unlike traditional approaches with large-scale models, these labs focus on interacting directly with both hardware and software. They help us show case various sensor modalities across different application use cases. This approach provides valuable insights into the challenges and opportunities of deploying AI on real physical systems.
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Welcome to the hands-on labs section, where you'll explore deploying machine learning (ML) models onto real embedded devices, offering a practical introduction to ML systems. Unlike traditional approaches with large-scale models, these labs focus on interacting directly with both hardware and software. They help us showcase various sensor modalities across different application use cases. This approach provides valuable insights into the challenges and opportunities of deploying AI on real physical systems.
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## Learning Objectives
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* Understand the steps involved in adapting and experimenting with ML models for different applications, allowing you to optimize performance and efficiency.
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* Learn troubleshooting techniques specific to embedded ML deployments equipping you with the skills to overcome common pitfalls and challenges.
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* Learn troubleshooting techniques specific to embedded ML deployments, equipping you with the skills to overcome common pitfalls and challenges.
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* Acquire practical experience in deploying TinyML models on embedded devices bridging the gap between theory and practice.
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* Acquire practical experience in deploying TinyML models on embedded devices, bridging the gap between theory and practice.
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* Explore various sensor modalities and their applications expanding your understanding of how ML can be leveraged in diverse domains.
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* Explore various sensor modalities and their applications, expanding your understanding of how ML can be leveraged in diverse domains.
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* Foster an understanding of the real-world implications and challenges associated with ML system deployments preparing you for future projects.
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* Foster an understanding of the real-world implications and challenges associated with ML system deployments, preparing you for future projects.
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:::
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We have included laboratory materials for three key devices that represent different hardware profiles and capabilities.
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* Nicla Vision: Optimized for vision-based applications like image classification and object detection, ideal for compact, low-power use cases.
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* XIAO ESP32S3: A versatile, compact board suitable for keyword spotting and motion detection tasks.
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* Raspberry Pi: A flexible platform for more computationally intensive tasks, including small language models and various classification and detection applications.
***Nicla Vision**: Optimized for vision-based applications like image classification and object detection, ideal for compact, low-power use cases. It is also suitable for keyword spotting and motion detection tasks.
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***XIAO ESP32S3**: A versatile, compact board suitable for vision, keyword spotting, and motion detection tasks.
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***Grove Vision AI V2**: Equipped with a dedicated Neural Processing Unit (NPU), this device enables more advanced machine learning tasks with enhanced on-device inference capabilities, making it ideal for sophisticated computer vision and AI applications.
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***Raspberry Pi**: A flexible platform for more computationally intensive tasks, including small language models and various classification and detection applications.
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| Exercise |[Nicla Vision](https://store.arduino.cc/products/nicla-vision)|[XIAO ESP32S3](https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/)|[Grove Vision AI V2](https://www.seeedstudio.com/Grove-Vision-AI-V2-Kit-p-5852.html)|[Raspberry Pi](https://www.raspberrypi.com/)|
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## Troubleshooting and Support
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If you encounter any issues during the labs, consult the troubleshooting comments or check the FAQs within each lab. For further assistance, feel free to reach out to our support team or engage with the community forums.
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If you encounter any issues during the labs, please consult the troubleshooting comments or refer to the FAQs provided within each lab. For further assistance, feel free to reach out to our support team or engage with the community forums.
These labs offer an opportunity to gain practical experience with machine learning (ML) systems on a high-end, yet compact, embedded device, the Seeed Studio Grove Vision AI V2. Unlike working with large models requiring data center-scale resources, these labs allow you to interact with hardware and software using TinyML directly. This hands-on approach provides a tangible understanding of the challenges and opportunities in deploying AI, albeit on a small scale. However, the principles are essentially the same as what you would encounter when working with larger or even smaller systems.
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The Grove Vision AI V2 occupies a unique position in the embedded AI landscape, bridging the gap between basic microcontroller solutions, such as the Seeed XIAO ESP32S3 Sense or Arduino Nicla Vision, and more powerful single-board computers, like the Raspberry Pi. At its heart lies the Himax WiseEye2 HX6538 processor, featuring a **dual-core Arm Cortex-M55 and an integrated ARM Ethos-U55 neural network unit**.
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The Arm Ethos-U55 represents a specialized machine learning processor class, specifically designed as a microNPU to accelerate ML inference in area-constrained embedded and IoT devices. This powerful combination of the Ethos-U55 with the AI-capable Cortex-M55 processor delivers a remarkable 480x uplift in ML performance over existing Cortex-M-based systems. Operating at 400 MHz with configurable internal system memory (SRAM) up to 2.4 MB, the Grove Vision AI V2 offers professional-grade computer vision capabilities while maintaining the power efficiency and compact form factor essential for edge applications.
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This positioning makes it an ideal platform for learning advanced TinyML concepts, offering the simplicity and reduced power requirements of smaller systems while providing capabilities that far exceed those of traditional microcontroller-based solutions.
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{width=65% fig-align="center"}
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## Pre-requisites
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-**Grove Vision AI V2 Board**: Ensure you have the Grove Vision AI V2 Board.
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-**Raspberry Pi OV5647 Camera Module**: The camera should be connected to the Grove Vision AI V2 Board for image capture.
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-**Master Controller**: Can be a Seeed XIAO ESP32S3, a XIAO ESP32C6, or other devices.
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-**USB-C Cable**: This is for connecting the board to your computer.
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-**Network**: With internet access for downloading the necessary software.
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-**XIAO Expansion Board Base**: This helps connect the Master Device to the Physical World (optional).
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## Setup and No-Code Applications
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-[Setup and No-Code Apps](./setup_and_no_code_apps/setup_and_no_code_apps.qmd)
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