What is Annotated Imagery Data? Uses, Examples & Data Sources

What is Annotated Imagery Data?

Annotated imagery data refers to visual content, such as images or videos, that have been labeled or annotated with additional information. This information can include object recognition, semantic segmentation, or other annotations that provide context and understanding to the visual content. Annotated imagery data is commonly used in computer vision tasks, such as training machine learning models for object detection, image classification, or scene understanding.
Examples of annotated imagery data include labeled images of objects, scenes, or events with corresponding annotations such as bounding boxes, semantic segmentation masks, or keypoint locations. Annotated imagery data is used for training and evaluating computer vision models, enabling them to recognize and understand visual information. In this page, you’ll find the best data sources for annotated image datasets.