A cook who learns to cook from hand-drawn pictures risks ruining every dish. The same trap awaits computer vision: models need thousands of labeled images, but collecting them manually is expensive and tedious. Synthetic frames solve the problem, but only if they look real. The GraNatPy tool assesses plausibility: one metric, like a fussy taster, checks the smoothness of brightness transitions at object boundaries. Another — entropy — ensures the set isn't monotonous and the images come out diverse. If the resemblance falls short, the program suggests improvements: photometry (lighting) and spectroscopy (color). A twist: a model trained solely on such enhanced images recognizes details more accurately than after seeing real photos. The SynthClaw agent automatically hunts for the perfect recipe for visual deception, tirelessly sifting through hundreds of options.
Virologists manually count plaques in Petri dishes — dead zones from viruses the size of a pen dot. An error of even one plaque leads to a wrong diagnosis. Synthetic images slash model training costs a hundredfold and make counting flawless.