This week, we met with Professor Kim and Ryan during the regular lab meetings. As usual, we again discussed our current progress on the project and goals for the interim demo.
For the item recognition part, I completed the implementation of the CNN model using Python and PyTorch. First, the training is done using the 1000 images per item that were obtained from the scraping from the web server and implementing an image processing algorithm to a single image to augment the image dataset. The training is done on my machine to fix any bugs and errors in my CNN model implementation. For the implementation and training, much guidance was received from this blog post.
Additionally, I worked on testing the CNN model using a few sample images from the test dataset. I have found that the CNN model developed does a good job identifying the student items. I have tested it with 21 images, which comes from a single image (not from the training dataset) from each 21 identifying student images, and it showed a 100% accuracy. However, I hope to test extensively with a much larger test dataset.
My progress is on schedule according to the Gantt chart. I also have made changes to the schedule to take account of the training and finalizing the CNN model implementation time and delayed the schedule for testing the CNN model. This testing should be done in correlation to the integration with the web application prior to the interim demo.
Next week, I plan on extensively testing the CNN model. I will also work with Janet to integrate this feature into the web application. As a group, I will be working with my teammates to fully prepared for the interim demo.