This week started off with my presentation, so I was able to present and take in the feedback and questions to better our project and my presentation skills. Thank you to all our peers and faculty for the thought and attention! I also worked with my group on the group poster early in the week.
I was able to debug and fix our ML classification model with ResNet-18. There were many bugs with integrating it into the front-end, so I was able to tweak it to run properly and output the classified results. Before it was not outputting any result, so I was able to modify it to be able to process images correctly. Likewise, I was able to improve our background reduction algorithm to make the RPi image clearer and easier to classify due to less external features in the database entry. This ultimately improved accuracy to around 89% which is roughly similar to our desired goals. I will work to fine-tune it more to get the accuracy up to 95% hopefully.
Likewise, I worked closely with Grace and Surya to deploy the web application on Nginx to create a MySQL database to be connected with the RPi using a MySQLConnector. I assisted Surya in the process of forwarding data from the RPi to the SQL database. We worked closely using MySQL Connector, and there slight configuration issues that we ended up solving. This involved work with coordinating between two USB cameras and testing with one vs two RPis. Likewise, I helped code python scripts to take images of both the object and the digital scale output.
Lastly, I conducted more testing and validation with Grace regarding the ML components which included a lot of unit test with our compiled data that we had from previous weeks. These tests will be talked about more in the team status report, but we did speed and accuracy tests on the ML algorithms for image classification and text extraction. Most of these tests were done locally first with stock images to ensure quick and efficient testing. The next step is live testing with live pictures from the RPi on the website.
Overall, this week involved a lot of collaborative work as we seek to integrate all our individual components into a working product. I hope to conduct more testing and fine-tuning to the ML models as we near the demo date. However, we are slowly seeing our product in full form.