The majority of time this week was spent on two efforts: verifying that the ±5 pixel expectation of the machine learning model was both not too strict and not to lenient and determining if the Jetson Orin Nano has enough compute for our needs. While evaluating the ±5 pixel expectation, I searched for trash datasets on both Roboflow and Kaggle and eventually settled one on Roboflow that I really liked. After visualizing images from the dataset with their oriented bounding boxes, their centroid, and 5 pixel circle around their centroid, I see that 5 pixels is a robust expectation to have. Regarding the compute of the Jetson Orin Nano, the specifications say that it has 1.28 GFLOPs and medium-sized YOLOv8-OBB model needs 208.6 FLOPs. Even with a FLOP efficiency of 20%, the Jetson Orin Nano should have more than compute to run the model and potential any other assistive processes that strength the centroid calculation process.
Next week, for the first part of the week, I have to investigate a little more time figuring out if fine-tuning existing YOLOv8-OBB models would be better in our use case as opposed to training one from scratch. Moreover, I want to finish preparing the dataset for our use case (e.g. making the background white for the images, making transformations that affect the lighting of the images, and etc.)
Currently, everything is on schedule.
