This week I got the training to run on GCP. I created a non-CMU account, and got $300 of free credit on GCP which I used to train 10, and then 20 epochs. The code runs much faster, with 10 epochs taking only 5 minutes. The training accuracy after 20 epochs is over 90% for all four types of drinking waste.
I also started setting up the Jetson. I worked on setting up an instance to be able to use with colab. I was able to connect colab with the local GPU. But after some roadblocks that came when I tried to run inference on a random picture of a bottle, and a conversation w Prof. Tamal, we realized that we can just run on terminal, which we will try next week. We also connected the camera to the Jetson, and will work on the code to have the snapshots from live camera stream be the source to the ML inference.
I believe in terms of the software and hardware coding, we are on track. We are slightly behind on the Jetson and camera integration, having it working this week would have been better. We are definitely behind on the mechanical portion, as our materials have not arrived yet. We plan to digitalize a detailed sketch-up with measurements to send to Prof. Fedder.
Our accuracy is >90% for all 4 types of drinking waste after training for 20 epochs
example of yolov5 doing labeling and giving confidence values. they are all mostly 90%, which is past our agreed threshold