Why we generate training images instead of collecting them
The rarest, most valuable cases are the ones with no photos. Here's how generated data covers them — and how we keep it realistic.
See the models in action.
Detection demos, model results, and notes on building computer vision from synthetic data. Watch what D8alytics detects — frame by frame.
Featured
Industrial smoke-plume detection
A detector trained entirely on generated footage flags smoke plumes rising from a power-plant stack — spotting abnormal emissions from a distance, through real-world haze and changing light.
Detection reel
Watch it detect.
Reading
Notes from the lab.
Writing prompts that produce clean, balanced datasets
A short guide to class definitions, environments, and capture variety — the levers that make a detector generalize.
From prompt to labeled dataset in four steps
Generate, auto-label, split, and package — a look at what happens between your prompt and a ready-to-train ZIP.
Want your use case featured?
Tell us what you're detecting — we love a good edge case, and we might turn it into the next demo.