This week, I made significant progress on the card classification algorithm. Initially, artificially creating new data before splitting up into test and validation sets was causing misleading results on the testing and validation sets. Also, the model I had designed was having issues with overfitting to the training data. Because of this, I switched to fine-tuning the ResNet18 model on our data instead of training a model completely from scratch. I have trained 3 card classification models, one trained on data from the top camera, one trained on data from the bottom camera, and one trained on both. Across all three of these models, the only image being incorrectly classified is pictured below.
I would argue thinking the card is a 1 instead of a 7 is more our fault than the model’s because of the glare, which we will mitigate by covering more of the backlight. Finally, I have a color classifying model, which is achieving 100% accuracy, as expected. Because of this progress, I believe I am back on track. This week, I plan on fully integrating the software UNO implementation with the hardware and begin work on the website.