This week, we had the last mandatory lab meeting with Professor Kim and Ryan. We have discussed the updates on our progress since the last meeting and our goals for the final presentation and the report.
For the item recognition part, I was able to increase the accuracy of the model by training for longer epochs. Instead of 15 epochs, where the validation accuracy started to converge, I trained for 50 epochs and it increased the classification accuracy to 84.36%. I believe that increasing the number of epochs is fine because the model is eventually trained onto the AWS server and in the web application, the user can simply input the image to the server and get the classification results from the model in the AWS server. For the reporting purposes of the testing component of the CNN model, I’m planning to not only present the accuracy percentage but also show the confusion matrix, which shows an overview of the classification of given labels to the correct labels.
In order to integrate the image recognition component into Janet’s web application, my main goal for the remainder of the semester is to provide an API endpoint for the model. I have been setting up the model (along with the datasets and the trained feature onto the AWS server using Amazon Sagemaker.
Although the development and the testing for the item recognition part are done, my progress on integrating this item recognition part is slightly behind because I need to provide the API endpoint for the web application and the item recognition module. Since the web app and the ML item recognition are running on two different servers and getting the servers to communicate to each other may be difficult, my backup plan is to train the model in the local machine (of a team member doing the final demo) and have the model to communicate with the web application locally. Then the top 3 recommendations can be transferred and displayed to the web application. To catch up on this, I have to work concurrently with providing an API endpoint between two servers (ML server and web application server) and working on the backup plans.
For next week, I plan to integrate this model into Janet’s web application and provide an API endpoint for the server. Finally, I have to check whether the recommendation is displayed correctly on the web application. Our group will also work on the deliverables for the finals week of this course.