4:54 PM - A few hours after the initial log, the separate pieces of our project have moved forward.

On the software side, Isaac and Ruize are testing different Roboflow datasets between an Identification and Classification model.  They ran the training for our YOLO model, and the initial validation metrics partially work. Matthew decided that downloading and combining multiple datasets may help with accuracy.  While waiting, we looked into other potential methods for recognition and came across Vision Language Models such as Moondream and SmolVLM.  Given our hardware specifications, it was decided that we would stick with the CNN.  During training, Ruize determined the model would take too much time to train with extra datasets so we’ve decided on a pre-trained model with slight modifications on accuracy readings.  

Meanwhile, Cory is finalizing the serial communication between our Python environment and the Arduino UNO Q with the OLED graphic display.  The screen did not display anything for a while until Cory burned his finger on the screen.  A minute later, we were able to finally display a message.

Our next step will be tuning the confidence levels for our readings before flashing the trained model onto the Arduino.