KopiKat

KopiKat offers an intriguing approach to data augmentation. It uniquely creates a new copy of the original image, retaining all necessary data annotations. Consequently, it generates a rich, high-quality, and diverse model that stands out compared to those generated by traditional data augmentation techniques. KopiKat's ultimate purpose is to aid real-world applications where the aggregation of large datasets can be problematic. It is designed to work with datasets of up to 5,000 images, a typical size in many AI projects. Its implementation allows engineers to enhance mean Average Precision (mAP), and also expand and diversify datasets—an essential function in areas such as object detection, neural network training, and transfer learning.