This week I worked on labeling the over 300 data points of pictures of cards I took this week. This involved creating bounding boxes for each of the cards in the pictures and then defining the labels for each of the bounding boxes. At this point, I got the outputs of each of the labeled bounding boxes, and fed them into the machine learning model for the YOLOv7 algorithm. These thousands of data points lead to a very high time to train the model. After training the model, I have realized that I have to make the algorithm more efficient and training should be a lot more efficient so this week I will work on training the network in different ways to optimize the time and ensure that it will work with our resources. To do this, I will try lower versions of YOLO which don’t require as much computing power. The progress is still on schedule and I hope to have the finalized ML model ready to integrate by the end of the week.