Image Annotation Tool

This tool is an open-source image annotation platform designed to help teams prepare labeled data for computer vision and machine learning. It supports bounding boxes, polygons, points, and semantic/attribute labeling across projects; datasets can be imported in bulk and exported to common formats for model training. The architecture emphasizes privacy and reproducibility, enabling self-hosted deployments or private cloud installations with complete control over data and labeling policies.
Conceptual workflow: A project defines an annotation schema with a label taxonomy and optional attributes. Annotators add annotations per image by applying geometric shapes and attaching labels; the system validates coordinates and enforces required fields. Annotations are versioned and auditable to track changes, and exports convert the in-tool data into widely adopted formats such as COCO, VOC, YOLO, or CSV for downstream training and evaluation.
Audience and value: data scientists, ML engineers, researchers, and teams responsible for data governance benefit from an open-source, self-hosted solution that avoids vendor lock-in and supports collaboration. The tool reduces labeling latency through schema reuse, supports multiple annotator roles, and provides consistent data structures that improve model performance through higher-quality ground truth labels.
Unique differentiators and use cases: being open-source and self-hosted delivers control over privacy and compliance. Extensibility through custom label types and plugins enables domain-specific labeling (e.g., medical imaging, industrial inspection). Use cases include autonomous driving datasets, medical image annotation, retail product tagging, and academic benchmarks where reproducibility and auditability matter.