This week, my goal was to read documents about the potential ML models we could implement in order to perform the computer vision object detection as well as the image classification. From some research and local testing, I found that the best choice for us at this time is the YOLOv3 algorithm which performs real time object detection and image classification in an extremely efficient manner which can be made more efficient due to the fewer amount of images we will have classify and the lower frequency at which we will need to classify them at. My progress is on schedule at this point because I was able to read all the documents I wanted and narrow it down to the model and overarching parameters I want to use. Now, since we have ordered the thermal printer, cardstock, and clips, as soon as they arrive, I can create all 52 of the different cards in order to build our dataset and start building and training my model locally on my computer. This week, I hope to start building the model locally, and if all the parts come on time, hopefully also start building the training dataset. By the end of the week, I also hope to have designed what the card looks like and figure out how to encode it on the thermal printer so that I can start printing out the cards for the datasets.