This week, I was able to gain access to a new computing cluster with higher amounts of storage and GPU availability late into the week. As such, I began downloading an open source objection detection dataset, BD100K, from Kaggle onto the cluster for evaluation/fine-tuning. After all of the images were downloaded (the version I downloaded had 120,000+ images), I was able to start working on the implementation of the evaluation/fine-tuning pipeline, although this is still a work in progress.
With regards to schedule, I believe that I am slightly behind schedule. Due to some issues with gaining access to the cluster and the download time required to fetch a large dataset, I did not anticipate not being able to work on this until the later half of the week. I would have liked to have finished the evaluation/fine-tuning implementation by this week, and so I anticipate having to put in a bit of extra work this week to catch up and have a few different versions of the model ready to export to our Jetson Nano.
By the end of this week, I hope to have completed the evaluation/fine-tuning pipelines. More specifically, I would like to have concrete results for evaluating a few out of the box YOLO models with regards to accuracy and other metrics, in addition to hopefully have fine-tuned a few models for evaluation.