This week was dedicated to slack time and integration. Since our system has slightly changed due to the addition of LEDs, the images slightly changed in appearance. Hence, I spent most of this week gathering more training data with Jeremy and labeling the images to constitute training, validation, and testing data for our model. Since our previous model was able to achieve high validation accuracies with the previous images (without LEDs), not many changes needed to be applied to our machine learning code. These were my main tasks for this week (gathering more data and training our ML model), so I am satisfied with my progress. Our model hits our overall latency and accuracy requirements. One of our user requirements is having the web app update within 2 seconds of a card being pulled from the trigger, and our web app updates (on average) 0.18 seconds after the trigger. Our accuracy requirement was 98%, and our final test accuracy is 98.1%. For the remainder of the weekend, I will help gather more metrics and performance results for our final presentation. I am definitely on schedule. Next week will be dedicated to further testing and making our final video, poster, and report. If we fail to satisfy our desired requirements during future testing, then I will have to help make modifications to our existing system. If time permits, I might also make the web app even more robust by allowing users to edit card classifications on the web app (for example, if a card was classified incorrectly). This is not a necessary feature, but it would add value to the overall functionality of our system.