CVAT

What is CVAT?

CVAT (Computer Vision Annotation Tool) is a comprehensive data annotation platform designed to streamline the process of creating labeled datasets for machine learning and computer vision applications. The platform provides teams with an intuitive interface and powerful annotation capabilities to handle projects of any scale, from small-scale initiatives to enterprise-level datasets. CVAT supports diverse annotation types including bounding boxes, polygons, keypoints, skeletons, cuboids, and trajectories, allowing users to prepare data for various machine learning tasks. With integration of semi-automatic annotation tools and support for major cloud storage providers, CVAT accelerates the annotation workflow while maintaining data privacy and security.

Key Features

• Multi-Type Annotation Support

  Comprehensive annotation capabilities including bounding boxes, polygons, polylines, keypoints, skeletons, cuboids, and trajectories for diverse computer vision tasks such as object detection, segmentation, and tracking.

• Semi-Automatic Annotation

  Built-in algorithms including intelligent scissors, histogram equalization, and model-assisted annotation that reduce manual effort and accelerate the labeling process by up to 10x faster.

• Cloud Storage Integration

  Secure dataset storage with support for AWS S3, Google Cloud Storage, and Azure Blob Storage, enabling teams to manage datasets privately in any format.

• Collaborative Workflow Management

  Role-based access control and task assignment features that enable seamless coordination across distributed teams, with built-in progress tracking and quality metrics.

• Quality Control and Analytics

  Comprehensive metrics dashboard providing actionable insights including annotator productivity, objects per hour, and annotation consistency monitoring.

• Multi-Format Export

  Export annotations in multiple formats including YOLO, Pascal VOC, and MS COCO, ensuring compatibility with various machine learning frameworks and pipelines.

Use Cases

• Autonomous Vehicle Development : Annotate road scenes and traffic elements including vehicles, pedestrians, lanes, and signs to build robust models for autonomous driving systems.
• Medical Imaging : Create annotated datasets for semantic and instance segmentation tasks in medical imaging, including disease detection and anatomical structure identification.
• Surveillance and Security : Annotate video footage for activity recognition, person re-identification, and object tracking in surveillance applications.
• Object Detection Model Training : Prepare high-quality labeled datasets through precise bounding box and keypoint annotations for training object detection models across industries.
• Research and Development : Support academic and industrial research by creating annotated datasets for developing and validating new computer vision algorithms.

FAQs