Overview
+The following labs offer a unique chance to gain hands-on experience with machine learning (ML) systems by deploying TinyML models onto real embedded devices. Instead of working with large models that need data center-scale resources, you’ll interact directly with both hardware and software. These exercises cover different sensor modalities, giving you exposure to a variety of applications. This approach helps you understand the real-world challenges and opportunities in deploying AI on real systems.
+Supported Devices
+Device/Board | +Installaion & Setup | +Keyword Spotting (KWS) | +Image Classification | +Object Detection | +Motion Detection | +
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Nicla Vision | +Link | +Link | +Link | +Link | +Link | +
XIAO ESP32S3 | +Link | +Link | +Link | +Coming soon. | +Link | +
Lab Structure
+Each lab follows a similar structure:
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- Introduction to the application and its real-world significance +
- Step-by-step instructions to set up the hardware and software environment +
- Detailed guidance on deploying the pre-trained TinyML model +
- Exercises to modify and experiment with the model and its parameters +
- Discussion on the results and potential improvements +
Troubleshooting and Support
+If you encounter any issues during the labs, please refer to the troubleshooting guides and FAQs provided with each lab. If you cannot find a solution, feel free to reach out to our support team or the community forums for assistance.
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