Justin’s Status Report for 11/9

This week was spent training a YOLOv7 model with a custom dataset. I set up a fork of the YOLOv7 Github repo with our training data to be cloned into Google Colab, and was able to successfully run training. I was worried that Colab usage limits would mean that we would have to partially train the model over multiple times, but it seems like we can train for ~50 epochs before the runtime is terminated, which should offer pretty good accuracy based on other people’s experience custom training YOLO. If not, it is also possible to continue training the model from the weights file, or we can use the Jetson’s GPU to train. I found a another dataset online that contains more varied waste categories. I want to try training a model with this dataset, and figuring out training parameters and evaluating models is my plan for the next week. I’ve also found a dataset of battery images, and will further train our model on that to identify something that should be rejected. This should be enough for an interrim demo. I’m hoping in the next week to have a model that is good enough to deploy for project to at least identify recycling, since the schedule specifies that we should be done with the model next week. If needed, I could continue working on more training, since deploying another model on the Jetson is as easy as saving a new weight file.