This week, I worked a bit more on the machine learning model. At the review, Professor Yu had suggested I make a small dataset from a few images of my desk. While this is not ideal, it does help me start some of the work. I have faced some issues with the model which I plan on debugging on Monday and after Thanksgiving break. I have two main issues right now, namely overfitting and sensitivity to hyperparameters.
Overfitting. With a small dataset, it is easy to make a machine learning model that memorizes the specific output expected on the training data. This does not identify patterns and therefore doesn’t generalize well. Due to the delays in acquiring data, I haven’t been able to collect a dataset of the right size, so the model is not as strong as I would like it to be.
Sensitivity to hyperparameters. Despite having only a small dataset, there are issues even with learning the training data. Small changes in the training hyperparameters cause large changes to the accuracy of inference on the training dataset. This will get worse as the dataset increases in size. This is a more fundamental issue that I need to research more before I attempt to fix it. My guess is that there is an issue with running darknet on a Jupyter notebook. (Darknet is the platform on which YOLO is written). One fix that I want to explore is changing the version of YOLO to one that was written in PyTorch (i.e. Python). This might fix the issue.
I also wrote a short python script that solves the problem I described in the last status update. One issue that Pablo is facing is that some of the images lose all their data and the bottom part of the image becomes a set of vertical lines. I described the approach in the last status update, but I had not implemented it then. Since then, there have been no problems with implementing my approach.