Following our return from fall break, we spent some time this week to debrief and re-calibrate our expected deliverables for the Interim Demo. One important change that was made for more convenient development was pivoting to the Jetson Nano as our prototyping platform. Outside of working on the Ethics assignment, I spent some time this week partitioning the dataset into separate datasets for cross-validation (train, validate, test), using roughly a 60/20/20 division, respectively. Because of the size of the dataset, I was confident that I could use a larger partition for validating and testing. Once done, I formatted the dataset in accordance to the SageMaker tutorial for TensorFlow, then uploaded it to an AWS S3 Bucket.
This weekend, I was granted AWS credits which I will use to begin training our ML model on SageMaker. Since SageMaker offers multiple frameworks for Image Classification (MXNet, TensorFlow), I will make sure to test both to see which is more accurate. Furthermore, I am planning to use K-Fold cross validation to test the robustness of our dataset. I am currently still training on the open-source dataset without any meaningful modifications outside of relabeling (see last weekly update), however we hope to add some more images that have been run through the pre-processing pipeline soon.
Since we are beginning to pivot toward preparing hardware for our interim demo, I also took some time this week to work independently on bringing up the Jetson Nano and eCAM-50. However, I ran into some issues flashing the SD card, due to a version mismatch between the on-board memory and the image provided by NVIDIA online. Since I do not have an Ubuntu system readily available, I will need to use Jetpack SDK manager on the lab computers to resolve this.
As mentioned above, I’ve run into some unexpected blockers both on hardware bring-up and AWS, but I’m hoping to catch up early this week, hopefully ending tomorrow with a working Jetson Nano and integrated camera, and a working SageMaker model. The rest of the next week will be spent measuring the results of tuning various parameters on SageMaker and choosing the best model to use for our application, in addition to working with Jay to integrate our phases.