Generative augmentation
Image generation models extend the variation in a small real dataset, controlled by clear prompts.
Generative augmentation in one paragraph.
A few real images become a much larger, more varied training set. Image generation models produce controlled variations in lighting, background, and surface appearance, while keeping the object and the labels intact.
Why a small dataset is not enough.
Small real datasets
Most teams start with a few hundred real images. Not enough to cover the variation a model meets in production.
Texture and appearance gaps
Color, finish, and surface variation are hard to capture exhaustively with real photography.
Uncontrolled augmentation hurts
Random crops and filters add noise but not signal. Augmentation needs to reflect real-world variation.
How we use generative models.
Prompt-driven variation
Generate controlled variants of real samples with clear prompts. Backgrounds, lighting, and texture change, the part stays the part.
Expand the long tail
Target the rare classes and edge cases that the real dataset under-represents. Balance the distribution.
Pair with simulation
Simulation controls geometry and physics. Generative models add texture realism. The two together cover both.
Talk to us about your dataset.
Tell us the inspection task and the conditions. We will come back with what is feasible, the timeline, and the cost.