This week, for the item recognition part, I worked more on finalizing the CNN model and improving the classification accuracy after testing with much more images (more images than 21 images for the last week). However, after updating the algorithm, while the accuracy I’m currently having is fine for the interim demo, I still found that the image recognition accuracy is lower than expected accuracy for our requirements set for the Design Proposal/Presentation. Although my current model just presents the top 1 item recognition given a student item image input, making the model presenting the top 3 item recognition will help increase the accuracy. Moreover, for this weekend before the interim demo, I will devote most of the time working on improving the image recognition model and algorithm.
My progress on the item recognition part is slightly behind because I was trying to get the item recognition algorithm better than our item recognition accuracy requirement. So, I was unable to integrate this item recognition into Janet’s web application. Thus, for the demo, we will demonstrate the item recognition functionality separate from the web application which tracks the tagged items inside/outside of the backpack. Our schedule has been modified to take account of this delay in the progress on the item recognition part.
For next week, I hope to do more extensive testing not only with the scraped images but also with the “real” images taken from a user’s smartphone camera. Therefore, I will take some student item images on my phone to test the model and ask my teammates to take some student time images to test the item recognition module. I will also make the system present the top 3 item recognition, instead of the current top 1 item recognition. Then, I hope to integrate item recognition with Janet’s web application.