An-augmenter: A unified platform for efficient image annotation and data augmentation

The growing reliance on AI and deep learning in vision-based applications requires efficient dataset preparation tools, however, existing solutions are often commercially licensed or lack integrated, multi-format workflows. This study presents An-Augmenter, a cross-platform, open-source software that integrates image annotation and augmentation within an offline environment. It supports YOLO, XML, and JSON formats and ensures annotation-consistent augmentation for labeled and unlabeled datasets. Processing 1200 images with all possible augmentation techniques required 50 s on a standard CPU. Validation using YOLO11n object detection model improved [email protected] from 0.905 to 0.941 on a custom egg dataset and from 0.799 to 0.825 on a public apple dataset, demonstrating improved detection performance with augmented data.