Wiznet makers

The deep learning work assistant, developed by Benjamin at WIZnet Makers, is a creative and innovative project that aims to enhance productivity by utilizing microprocessors and microcontrollers. The project revolves around capturing photos every 5 seconds and analyzing them using artificial intelligence (AI) to recognize work patterns.

The hardware used in this project includes a modified Raspberry Pi Pico clone provided by WIZnet and an Arducam. The software aspect of the project relies on CircuitPython, a variant of the Python programming language designed for microcontrollers. Benjamin has generously shared the code for this project on GitHub, making it accessible for others to use and contribute to.

The core of the project lies in the utilization of the nano model of Ultralytics YOLOv8, a real-time object detection and image segmentation model. This model has been trained to recognize various situations commonly encountered during work, such as normal or drowsy states, yawning, distractions, and cellphone usage. By scanning the user's face, the model can provide insights into their level of productivity.

To process and display the real-time information gathered by the YOLOv8 model, Benjamin has integrated everything into a Flask application. This web framework for Python allows for the creation of a user-friendly interface that provides timely updates and tracks the user's workflow.

Overall, this project showcases the power of deep learning and microcontrollers in creating a work assistant that can analyze and provide insights into productivity. Benjamin's contribution to the open-source community by sharing the code on GitHub allows others to explore and build upon this innovative project.