Month: March 2024

This week, I accomplished a few more things. Firstly, the card classification models have been tested. We found the most success with two separate models for the top and bottom cameras, but use the same color classification model across both of them. At this point, after at least ~500 times of using the models, we have not seen an incorrect classification. We will continue to monitor and train on new data if something comes up. Although I had implemented most of the UNO code around week 1 or 2, we finally got around to testing it live. We ironed out a few bugs with the implementation, and everything seems to be working now. I have also helped Thomas with making some modular modifications to the dispenser, which are being printed. We will continue to iterate on the dispenser over the next few weeks. I also began designing a new control flow to allow better async communication (such as from the website). I also made a crude GUI for visualizing the state alongside logging for debugging.

At this point, I am on schedule. This week, I will finalize the new software macro design and begin work on the website.

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.

I recently completed several significant tasks as part of a project. Firstly, I developed a color classifier, which appears to be highly effective, although I am in the process of gathering metrics to quantify its performance accurately. Additionally, I successfully finalized the UNO interface, enabling a controller to receive inputs from users and incorporating redundant state displayers. Furthermore, I gathered data from both top and bottom cameras and devised a script to generate diverse images from existing inputs. By executing this script, I generated an extensive dataset for each camera. Finally, I conducted thorough testing to ensure that the model can effectively learn from and fit to the training data, currently getting 99.9%. I have done a lot of work to get back on track, so I feel we’re in a good place. Next week, I will have full testing metrics for the model on both datasets, having swept through hyper-parameters.

The above two images were generated dataset images from an actual photo taken from our pi camera.