This week, I began looking into different object detection algorithms online that we can use as part of our first iteration of our implementation. Specifically, I installed a pre-trained YOLOv8 model from the YOLO package “ultralytics”, and was able to get it working on a CMU computing cluster. Since a rigorous evaluation and fine-tuning of the models will be necessary for integration, I’m planning on beginning to implement an fine-tuning and evaluation pipeline in the next few days to measure the model performance on unseen data, such as generic datasets containing images of streets such as BDD100K, EuroCity Persons, and Mapillary Vistas. Unfortunately, these datasets are way too big to store on the clusters I currently have access to, so I am working on obtaining access to alternative computing resources, which should be approved in the next few days.
With regards to progress, I believe that I am about on schedule. We have specifically set aside the upcoming week and next to evaluate and handle the ML side of our project based on our Gantt chart, and I am optimistic that we should be able to get this done in the next two weeks as the models themselves can simply be fine-tuned to any degree as we see fit with our constraints.
By the end of next week, I’d hope to have completed the download of the image datasets, as well as finished preliminary evaluation of the YOLOv8 model. We may also consider using different object detection models, although this is likely something we will consider more seriously as we get the first results from our YOLOv8 model.