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Team Status Report for 4/18/20
Redeemed our cloud credits Planned out integration Work on: Actually integrating all the components Making video Sharing code
Redeemed our cloud credits Planned out integration Work on: Actually integrating all the components Making video Sharing code
This week our group continued to work on our individual modules. Patrick trained around 60000 images for his facial emotion recognition and will add a secondary SVM for detecting emotions that are harder to tell apart. Vinay finished his first iteration of training for textual sentiment analysis. Yoojin added video Read more…
This week we just continued working on our individual components of our project. Patrick continued training with images from AffectNet. Vinay continued training and working on the LSTM for sentiment analysis. Yoojin found a paper that detailed a CNN for tone recognition and collected the libraries and documentation to start Read more…
This week, the team -Refocused our project to transition into working remotely -Set up team zoom meetings -Changed final product from physical device to server-hosted application -Added new part to our project to make up for loss of physical product To do: -Work on individual components -Start planning how to Read more…
This week we focused on finalizing our design review slides and document. It allowed us to finalize important details in our architecture. We plan to have the basic framework for our networks by the time we come back from Spring Break in order to start training. Then we can start Read more…
Updates: Received AWS credits, ordered parts (RPi and camera). There are no changes to the design or schedule. The team is working on individual components (writing algorithms, writing app, etc.) There are no significant risks we can think of at this time; however we did discuss the possibility of storing Read more…
Summary: Acquired datasets to train Worked on Design Review Presentation Decided on training algorithm Changes: We decided to use pre-trained convolutional neural networks to increase accuracy and reduce the time required in the training process. We will still be training the neural networks ourselves using the SVM model. Our schedule Read more…