A computer vision system that looks at a photo of a solar panel and tells you what's wrong with it — dust, bird droppings, snow cover, electrical or physical damage — then shows its reasoning with a heatmap. No server, no upload to anywhere: the trained model downloads once and every prediction runs directly on your device.
Try it yourself
This is the actual trained model, exported to run in the browser — not a mockup and not a call to a server. Your photo never leaves your device.
How this works: the trained model (~80MB, EfficientNetV2-S) downloads once directly from this page and runs fully client-side with onnxruntime-web — nothing is sent to a server. The first load takes a little while depending on your connection; your browser caches it after that, and every prediction after the first runs in a couple of seconds.
Under the hood
Four stages run every time an image is uploaded, entirely inside your browser tab.
What it detects
Trained to distinguish six visually distinct panel states — the categories that matter most for maintenance decisions.
Why explainability
A raw class prediction isn't enough for a decision-support tool. A technician needs to know where on the panel the model is looking before trusting the call.
Every prediction is paired with a class-activation heatmap, built by combining the network's final feature maps with its classifier weights — highlighting exactly which pixels pushed the prediction toward its answer.
This turns the model from a black box into something a human can sanity-check at a glance, which is exactly what a prototype decision-support tool needs to be trustworthy in the field.
Built with
Trained in PyTorch, exported to ONNX, running entirely client-side with onnxruntime-web.
Explore more