Output Formats

We provide data annotations in a variety of formats to ensure seamless integration with your machine learning models, regardless of the framework you’re using. Our supported formats include:

Output Formats

XML (Pascal VOC Format)
Popular for object detection tasks, this format stores image annotations in a structured XML file.
YOLO (TXT Format), YOLOv8
Designed for real-time object detection, this TXT format is lightweight and optimized for high-performance models.
CSV (Comma-Separated Values)
A tabular format that's easy to work with in spreadsheet tools like Excel or for custom data pipelines using Python or other scripting languages.
JSON (JavaScript Object Notation)
A versatile format for a wide range of machine learning projects, JSON provides easy-to-read, structured annotations for object detection, segmentation, and more.
COCO Format (Common Objects in Context)
Widely used for large datasets, the COCO format supports complex annotations like object segmentation and key-point detection.
Segmentation Mask
A pixel-level annotation format used for tasks such as semantic segmentation and instance segmentation. Each pixel is labeled according to the object or region it represents.

Tagging Types

We support data tagging across a wide range of media types, ensuring that your specific project requirements are fully met:

Text Tagging: Annotating text data for NLP tasks such as named-entity recognition and sentiment analysis.
Image Tagging: Annotating objects, people, and scenes in images for tasks like object detection and classification.
Video Tagging: Frame-by-frame annotations for tracking objects, actions, and scenes in video content.

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