KopiKat extends standard methods of data augmentation by creating a new copy of the original image while preserving all essential data annotations. The result is a rich model whose quality and diversity are superior to those produced using traditional data augmentation techniques. Our goal at Kopikat is to enable real-world applications, especially where compiling large datasets is challenging. Kopikat fits perfectly on datasets of up to 5,000 images, which is typical for many real-world AI projects. It provides engineers with the ability to improve mAP, expand and diversify datasets—indispensable advantages in areas such as object detection, neural network training, and transfer learning.