Month: April 2019

David Diao (dyd)

This week I worked on preparing for the final presentation. We made slides and created a presentation in preparation for next week’s presentations. I also worked on debugging our AWS issues with database integration.

 

Jessie Li (jessieli)

This week I worked on preparing for the final presentation. Our group made slides and created a presentation in preparation for next week’s presentations. Our project is still having bugs with AWS, but it is generally coming along.

David Diao (dyd)

This week I worked primarily on integrating the local web application onto AWS. I also worked more on the frontend user interface and worked with Jessie to conduct some user studies on the workflow of our application.

 

Jessie Li (jessieli)

This week I worked primarily on helping David integrate the local web application onto AWS. In addition, we developed on with the frontend user interface, doing a brief user study to test the flow of data through the system and how the user can interact with it. We made some changes to the frontend based on the user study to make the application more intuitive.

David Diao (dyd)

This week I worked on debugging the integration of Tushar and Jessie’s code in my backend. I also worked on the frontend so the user can check if the backend recognized their digits correctly. After that, the user can edit or submit their answer in order to see their result and move onto a new question.

 

Jessie Li

This week I worked on switching the dataset to MNIST and developed on and trained my CNN to work on the MNIST test set. The resulting accuracy is 98%, but while doing user testing, the accuracy seems somewhere around maybe 70%. This is perhaps related to how we are processing the image to classify; more testing is needed. I also helped Tushar work on his segmentation algorithm, writing a connected components finder in order to aggregate the labels found using the segmentation algorithm.

David Diao (dyd)

This week, I worked on our team’s demo and getting my component ready for the demo. I finished integrating Jessie’s CNN and Tushar’s segmentation portions into the backend, and now have much of the basic use cases finished.

Jessie Li (jessieli)

This week, I worked on our preparing for out team’s demo and getting my CNN component ready for the demo. I trained a few versions of the CNN and tested them for accuracy; the accuracy is still a bit awkward and not as high as I’d like, so further testing is required.

David Diao (dyd)

This week, I worked a lot on the web app to get it ready for the demo. I finished building out the basic frontend interfaces that we will need, and also finished up the backend endpoints that will be crucial for the demo. In addition, I worked on integrating Jessie and Tushar’s code into the web app.

Jessie Li (jessieli)

This week, I worked on building a CNN for the HASYv2 dataset. I trained a few networks with different epoch and batch numbers, but the highest I could get a random test set to was 79%. Part of this is because the reduced HASYv2 dataset is fairly small; much of the huge dataset we did not actually need. This needs some discussion; for the demo, at least, we can use the MNIST dataset of just digits (no characters or symbols), which my CNN has trained to a 98% accuracy rate.

 

Tushar Chetal (tchetal)

Had to change the segmentation algorithm based on the changes we discussed. Finished writing the segmentation algorithm.