Thanks! We used the water tank as the first end-to-end test using the product, and made a website that calls the API every minute to monitor the water level in a dashboard.

The architecture implemented using Lobes for object detection is called Yolo v2 (https://pjreddie.com/darknet/yolo/). It is fairly state-of-the-art for that type of problem and has ~70 million parameters that are being learned (matrices that get multiplied and added together). With a webcam and a GPU over the network, we typically see ~1-5 fps with a lot of network overhead sending output images - looking to make that faster for API deployment. The paper site above shows it having 62.94 Bn FLOPS