This week, I mostly worked on training a pretrained model on an existing trump card dataset. I thought it would be pretty straightforward to make an existing object-detection model (yolov11) to be trained on a card dataset and be capable of detecting cards. However, this was a oversight– the model, after being trained on 8000+ training data of cards, it struggled to perform on the test dataset. I had the plots for mAP50 and a couple other metrics, but forgot to save it on google drive, as I was running on google colab and were gone after I reaccessed it. However, the result was bad and made me think if we would need a model that is capable of classifying cards on general purpose. I instead realized that it would be a lot better for having a custom dataset that captures our own environment where the model has to see the card. Furthermore, instead of using a general-purpose pretrained object detection model, I realized it would be even better if I use a pretrained card-detecting model, then finetune it to be capable of detecting cards in our environment.
My progress is a little behind, since by this week, I wanted to have the trained model deployed on raspberry pi, along with the camera module attached. However, since the time was consumed as why was experimenting with how to train the model, I didn’t have enough time to do that. As such, I’ll have to dive right in and generate the dataset myself and train the model as soon as possible. Also, since I finally managed to borrow a deck of cards from the CMU Poker club, it will be viable to do so.
By next week, I’ll need to have Raspberry Pi that has the card detecting model deployed.