This week, I worked on fine-tuning the pretrained YOLOv8 models for better performance. Previously, the models worked reasonably well out of the box on an out of distribution dataset, so I was interested in fine-tuning it on this dataset to improve the robustness of the detection model.
Unfortunately, so far the fine-tuning does not appear to help much. My first few attempts at training the model on the new dataset resulted in the model not detecting any objects, and marking everything as a “background”. See below for the latest confusion matrix:
I’m personally a little confused as to why this is happening. I did verify that the out of the box model’s metrics that I generated for my last status report are reproducible, so I suspect that there might be a small issue with how I am retraining the model, which I am currently looking into.
Due to this unexpected issue, I am currently a bit behind schedule, as I had previously anticipated that I would be able to finish the fine tuning by this point in time. However, I anticipate that after resolving this issue, I will be back on track this week as the remaining action items for me are simply to integrate the model outputs with the rest of the components, which can be done regardless of if I have the new models ready or not. Additionally, I have implemented the necessary pipelines for our model evaluation and training for the most part, and am slightly ahead of schedule in that regard relative to our Gantt chart.
For this week, I hope to begin coordinating efforts to integrate the object detection models’ output to the navigation modules in the hardware, as well as resolving the current issues with the model fine-tuning. Specifically, I plan on beginning to handle the miscellaneous code that will be required to pass control between our modules.