As Steven prepared for the final presentation, Surya and I helped provide any necessary feedback to ensure the presentation effectively portrayed our current work in progress along with incorporating previous presentation suggestions. While most of the hardware components remained static, the design tradeoffs involved weighing the pros and cons of different image classification libraries, showing greater overall benefits in Tesseract OCR and ResNet-18. With that being the main focus of the presentation, we tried allocating less time towards the video demos but rather verbally explain more.
Since we are approaching all of the final components to our project, I noticed there were still some frontend components on our web application that could be improved involving user experience, performance, and functionality. First, focusing on the UI, the previous designs had too much clutter on one side of the screen so it is important to ensure appropriate whitespace is utilized to not overwhelm users with too much information. I had some trouble with maintaining a consistent branding and typography on the pages since they do range from creating new posts for the global page to displaying parsed caloric information from uploaded images. I will receive further feedback from user testing to see which pages are easier to navigate and proceed from there.
Additionally, since we have made substantial progress in integrating the subsystems together, there were improvements to be made on the interactive elements of the web application upon its setup on Nginx. Since all the necessary buttons and forms have been implemented onto the UI, I worked on making certain ones stood out from others to ensure users visually notice them first. Another issue that emerged was providing feedback and error states/messages to users after their actions such as scanning the product with the camera. Providing helpful error messages can immediately help users identify their incorrect behavior and receive necessary feedback as soon as possible to improve.
Since I previously helped Steven in gathering data for the ML algorithms, we also worked together in its testing and validation. Specifically regarding testing, because we did not yet reach our initial goals in accuracy determination, we conducted various tests on our label reading and image classification algorithms to improve their validity. The crucial part of these tests is to ensure they perform well under controlled conditions before letting users use the product in real time. We previously spent a lot of time with clear images of these nutritional labels and fresh produce, so the necessary action now is to conduct live testing with the RPi cameras, allowing us to assess how the ML algorithms adapt and perform in real-time. This transition in testing is intended to close the gap between theoretical accuracy and real life applicability of the various trained algorithms.
With the remaining time, our group will shift efforts towards maximizing efficiency of our algorithms and make necessary fixes before the final demo on Friday. As I have previously worked on research posters prior to this class, I will be mainly working towards finishing that before the deadline. My group and I look forward to presenting our semesters’ worth of work to the faculty along with others who may be interested in learning more about our product and how it could be expanded to a wider application in the future.