Feature
- Harvard-Aligned Learning Experience: Following Professor Vijay Janapa Reddi's Machine Learning Systems curriculum from Harvard University, the tinyML4D group, this kit offers a structured learning path through dedicated hands-on lab sessions. Each lab is carefully designed to build practical skills in TinyML® development, supported by detailed documentation and example code. This academic alignment ensures learners gain both theoretical understanding and practical experience in machine learning implementation.
- Complete Machine Learning Development Platform: The kit provides a comprehensive hardware solution centered around the XIAO ESP32-S3 Sense, featuring an integrated OV3660 camera and digital microphone. The included expansion board enhances functionality with a crisp 0.42" OLED display and precise 6-axis IMU, while the SD card support enables extensive data logging capabilities. This powerful combination of components creates an ideal platform for developing diverse machine learning applications of Vision, Sound, and Motion.
- Versatile Application Development: Unlock endless possibilities in edge AI and tinyML® development across multiple domains. From computer vision applications like image classification and object detection to audio processing for keyword recognition and voice commands, the kit supports a wide range of projects. The integrated motion sensors enable sophisticated gesture recognition and movement analysis, allowing learners, educators, and developers to create innovative IoT and wearable technology solutions.
- User-Friendly Design: Engineered for accessibility, the kit features a compact and portable design that's perfect for both classroom development and field deployment. The expansion board connects seamlessly with the MCU, while the USB-C interface ensures convenient programming and power delivery. Compatible with popular development environments like Arduino IDE, no-code AI model training & deployment platform SenseCraft AI, and TensorFlow Lite, the kit removes technical barriers and allows developers to focus on creating innovative applications.
Specification
Parameter | Description |
MCU | -Onboard Sensors: OV3660 Camera, Digital Microphone -Wireless Connectivity: Wi-Fi, BLE -SD Card Reader |
Display | Driver IC: SSD1315 |
0.42-Inch OLED | |
IMU | Chip: LSM6DS3TR-C |
Type: 6DOF | |
Button | Reset |
BAT Connector | JST 1.0 mm, 2 Pin |
Power Supply | Type-C or 3.7V Battery Connector |
Dimensions | 21×17.8×30 mm |
Weight | 10.6g |
Hardware Overview
Application
- Computer Vision: Image Classification & Object Detection
- Keyword Detection
- Motion Detection
- Wearable devices
- Health monitoring
- Education
Documents
Part List
Seeed Studio XIAO ESP32-S3 Sense (Pre-Soldered) | ×1 |
IMU Breakout Board for Seeed Studio XIAO ESP32-S3 | ×1 |
2.4GHz FPC Antenna (1.16dBi) | ×1 |
Heatsink | x2 |
Micro SD Card Tool Kit: | x1 |
The XIAOML Kit
Experience hands-on machine learning with this XIAOML Kit designed to complement Professor Vijay Janapa Reddi 's textbook "Machine Learning Systems". Powered by the XIAO ESP32-S3 Sense with built-in camera and microphone, this comprehensive kit includes an expansion board featuring a 6-axis IMU and 0.42" OLED display, plus an SD card toolkit for data storage. Perfect for students, educators, and developers, the kit enables you to build practical applications in vision, sound, and motion through tinyML® lab sessions developed with Marcelo Rovai (UNIFEI). Create real-world projects ranging from image classification and object detection to keyword recognition and movement analysis.
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1 on 1 Support | 12-Month Warranty | 30-Day Return Policy