This week I worked on:
- Presented the design review presentation. We got a lot of good feedback, especially for our hardware design and our ML algorithm.
- Worked with Fred on details of hardware design. Came up with a design that uses gears so the servo doesn’t need to be in the middle of the axis. This ensures there’s no weight on the servo and it also allows us to potentially add additional servos if we need more torque.
- Wrote code that can train any prebuilt models on pytorch. Uses fastai wrappers to make the job easier.
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- Created a resnet34 classification model with 66% top-1 test accuracy. Not a metric worth noting as I still need to have better data augmentation and hyperparameter tuning. We also need validation testing from internet sources and we’re looking at top-3 accuracy, but it’s a start.
For next week:
- Finish the visualizer section of the design report.
- Bootstrap training data with additional data from the web.
- Create validation set by manually labeling web-scraped images.
- If I have the time, try training an object detection model that can find top and bottom.
I am on schedule.