The important thing is not necessarily the amount of data, but its diversity. This means that different appearances of error types are relevant for a good and representative sample of defecets. We do not need many OK images, as the diversity in these images is usually smaller than for NOK images. Accordingly, our focus is on images with defects. We can give you the following rough rule of thumb: In 90% of the cases, 50 images per task (e.g. 50 NOK images of a defect class) are sufficient. In 10% of the cases, another 50 images (i.e. 100 in total) are needed to reliably adapt the Maddox AI system to your use case.