This week was focused on finishing integration and optimizing the ML Model. I was able to integrate the ML model with the Jetson by retraining it on the ECE Clusters on a Python 3.8 environment, and I tested it with TensorRT and it functioned properly. However, something that we anticipated and encountered was how the Yolo11L model was a bit slow for inference, so we retrained for 2 epochs with the Yolo11S model. We will continue to optimize the logic and finish integration with the WebApp over the next week, as we are almost done with the vision model.
I was aware of ways to write CUDA kernels to speed up and optimize inference, but I was not aware of TensorRT for this optimization, which I was made aware of during the 2nd presentation. Overall, the most important skill I have learned throughout this project is how to rapidly prototype Vision Models. I learned how to do so via blog posts and reading other peoples experiences online, through NVIDIA forums and Stack overflow posts. This has greatly decreased the time I need to go from a base idea to a working Vision Model, which I feel has been a great thing to learn throughout Capstone.