Good news before spring break! After writing scripts to process the labeling files as Ting and I have discussed, our model is finally able to run through the whole training process! Using Google Colab without GPU, training for a single epoch took more than half an hour (while the default number of epochs is 150). Therefore, we have worked on using the HH 1305 machines as well as GCP credits to accelerate the training process. Once running on GPU, training will be done soon and we will use backup datasets to practice fine tuning.
As we were starting to set up Jetson, we realized that the Jetson Xavier NX (paired with a microSD memory card) is a better choice in terms of computing power than the Jetson Nano that we’ve chosen before. After a brief research, we decided to switch. After spring break, we will set up Jetson & camera and deploy image capture and (change) detection code to Jetson.
Besides that, this week I have used the most of our time on the design doc. Alas, simply moving stuff from a google doc to the latex version took so long. On Friday (Mar 3rd) as I am writing now, I have just finished 2 hours of proofreading and reformatting and we are finally ready to submit.
In the coming weeks, setting up Jetson and integrating what we have now will be a major task. As none of us has worked with Jetson extensively before, there would be challenges. Either way, I am excited to transform work from computers to reality.
Link to code (update mostly on scripts organizing training data):
https://github.com/AichenYao/capstone-scripts