Max Tang’s Status Report for 3/15/2025

Training the walk sign image classification model has had significant progress. The ResNet model is very easy to work with, and I have been addressing the initial overfitting from last week by training the model on a much more diverse dataset from multiple intersections around the city. I’ve developed the habit of always having my camera open when I get near intersections when I’m walking or commuting around Pittsburgh, and I have been able to get much more images. All I have to do is crop them and feed them into model. I have also been working on some hyperparameter optimization, such as the different layers and sizes. This has not really resulted in improved performance, but it’s possible that I can add more layers that will make it better. This will require some research to determine if layers like additional dense layers will help. By going into the open-source ResNet code, I can I think next week I want to have the model in a finalized state that I can being integrating it into the microcontroller. I think I will have to spend some time figuring out how to quantize the model to make it smaller next week.