Deep Learning Guide: Choosing Your Data Annotation Tool

Choosing the right data annotation tool for deep learning projects, particularly in computer vision, involves evaluating several key criteria: efficiency, functionality, formatting, application type, and price. The blog post reviews five popular annotation tools, each with unique advantages and limitations. LabelImg is praised for its simplicity and intuitive interface, although it only supports bounding boxes. VGG Image Annotator (VIA) offers a broader range of shapes and is web-based, allowing offline work in browsers. The Computer Vision Annotation Tool (CVAT) is noted for its collaborative capabilities and extensive format support but is best suited for Google Chrome. Microsoft’s Visual Object Tagging Tool (VoTT) is web-based and integrates well with cloud storage, although it requires data to be uploaded to the cloud and supports limited annotation shapes. Supervisely, the only paid option discussed, is a comprehensive platform offering advanced features like AI-assisted labeling and supports a wide range of data types, benefiting both experienced engineers and beginners by simplifying workflows. Each tool's suitability varies depending on project requirements, team size, and budget constraints.

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