Accomplishments this week:
I retrained our object detection model by changing fine-tuning parameters to improve performance, such as increasing the starting learning rate for the learning rate scheduler and changes to lower memory usage. Additionally, I performed more complex data transformations to augment part of our dataset to work better in different indoor lightings by editing features like saturation, shadows, and rotations/flips. Additionally, debugged with my teammates, helped Maya with woodworking, started testing with Kaya, and started on our final documentation.
Reflection on schedule:
On schedule!
Plans for next week:
Finish testing and poster.
New tools/knowledge:
As I worked on our project, the main knowledge I had to learn was deep learning techniques to fine-tune and improve model performance and also integration knowledge with our peripherals. The learning strategies I used were applying knowledge from the deep learning class I am currently in and going through forum posts such as stack overflow posts and YOLO support posts of similar problems to ours with fine-tuning. Additionally, I learned how to efficiently go through technologies’ documentation and support websites to learn integration techniques for the technology I have not used before.