Image Annotation Tool

The tool provides image annotation capabilities tailored to Python-based machine learning pipelines. It supports creating bounding boxes, polygons, and keypoints, assigning consistent labels, and organizing annotations by image within a session. Annotations are stored in a structured internal model and can be exported to widely used formats for model training, such as COCO JSON, VOC XML, and YOLO TXT. The system emphasizes data integrity, including bounds validation, label consistency, and schema alignment, to ensure annotated datasets are ready for ingestion by standard CV frameworks. Conceptually, users load images, apply annotation primitives, and manage a per-image annotation set that can be batch-exported or individually inspected. The tool is designed for labeling teams, researchers, and developers who require repeatable, exportable annotation schemas without in-tool model execution. It supports multi-image projects, label templates, and import of existing annotations for editing or extension. Unique value lies in tight alignment with common dataset schemas, robust validation, and straightforward, script-friendly exports that integrate with Python data pipelines and training scripts. Use cases include autonomous driving datasets, medical imaging labeling, and retail product localization, where accurate geometry and consistent labels drive model performance and reproducibility.