KopiKat is a revolutionary generative data augmentation tool to improve the accuracy of an AI model without changing the network architecture. KopiKat extends standard data augmentation approaches by creating fresh replicas of original images while retaining all essential data annotations. The result is an enriched model that exceeds the quality and diversity of those generated by traditional data augmentation techniques. Our purpose with Kopikat is to empower real-world applications, particularly where assembling a large dataset is a challenge. Kopikat is perfectly tailored for datasets of up to 5,000 images, characteristic of many real-life AI projects. It provides engineers with the capacity to boost mAP, expand, and diversify datasets—an indispensable advantage in areas such as object detection, neural network training, and transfer learning. We are committed to continuously refining Kopikat, increasing its efficacy and versatility to meet evolving needs. As we look towards a future where data's importance will only amplify, we stand ready to facilitate this growth. Our innovative solutions, such as Kopikat, are designed to be at the forefront of this burgeoning field.
