Automatic platform and slider movements to capture 360° figurine images. Scheduled listing tracking and feature-rich web interface on UNO Q.

This audio classification neural network model is trained on audio samples of metal gearmotor turning and mini-figurines falling on the rotary platform to detect figurine falls in order to suspend the cataloging process to let the user reposition the figurine and resume the process without producing unsolicited figurine photographs.

• background_noise
• model_fall

After training and validating, I deployed my audio classification model. Since I developed this project on the Arduino UNO Q, I was able to employ the official pipeline to directly import my model into the Arduino App Lab by linking my Arduino account to this Edge Impulse project.

The project GitHub repository provides:

• Code files
• The mini-figurine cataloger App Lab application's ZIP folder
• 3D component design files (STL)
• Edge Impulse audio classification model (EIM binary for UNO Q)
• Hermes AI agent skill files (markdown)