CVAT (Computer Vision Annotation Tool)

CVAT (Computer Vision Annotation Tool) is an open-source web-based platform for creating and managing annotations for computer vision datasets (data labeling / Machine Learning Operations (MLOps) tooling).

• Web-based annotation platform for images, video, and other visual data (data labeling).
• Supports multiple annotation types such as bounding boxes, polygons, polylines, points, skeletons, and classification (computer vision tooling).
• Multi-user, project- and task-based workflow with role management and collaboration features (MLOps / data operations).
• Plug-in architecture and Representational State Transfer (REST) Application Programming Interface (API) for integration with external systems and automation pipelines (platform extensibility / integration).
• Import/export of datasets in various common computer vision formats to connect with training pipelines (ML dataset management).

More About CVAT

CVAT (Computer Vision Annotation Tool) is an open-source web-based system for annotating visual data such as images and video to prepare labeled datasets for computer vision training and evaluation (data labeling / MLOps tooling). Developed under the OpenCV organization, it targets teams that need a browser-accessible environment to manage labeling projects, assign work, and maintain consistency of annotations across large datasets.

The platform focuses on interactive annotation for common computer vision tasks (computer vision tooling). It provides tools for drawing and editing bounding boxes, polygons, polylines, points, and skeletons, as well as assigning attributes or labels for classification. These tools support object detection, segmentation, tracking, pose estimation, and related workflows where pixel- or region-level labels are required. Frame-by-frame and track-based mechanisms support video annotation, including interpolation of object positions across frames.

CVAT organizes work into projects, tasks, and jobs with user roles that separate administration, annotation, and review activities (MLOps / data operations). This structure supports enterprise teams with multiple annotators and reviewers, enabling workload distribution, quality control, and auditability of labeling outcomes. Built-in user and access management allow organizations to control who can create tasks, modify datasets, or approve completed work.

The system exposes a REST API for automation and integration (platform extensibility / integration). Enterprises can programmatically create tasks, upload datasets, trigger annotation workflows, and export labeled data into downstream training pipelines. CVAT also supports plug-ins and extensions for custom models or tools, enabling integration with internal Machine Learning (ML) services or specific domain requirements. Integration capabilities position it as a component in broader MLOps architectures rather than a standalone tool.

Dataset import and export functions connect CVAT to common computer vision formats (ML dataset management). Organizations can bring in existing datasets and export labels to formats that are compatible with typical training frameworks and pipelines, supporting reproducibility and reuse. The web-based architecture enables deployment on-premises (on-prem) or in private cloud environments, where enterprises can align CVAT with internal security, compliance, and network policies.

Within an enterprise taxonomy, CVAT fits into categories such as data labeling platforms, MLOps tooling, and computer vision dataset management. It provides core capabilities for structured annotation workflows, collaborative labeling, and integration with model training and evaluation environments.