CVAT (Computer Vision Annotation Tool) is a free, open-source, web-based platform designed for annotating images and videos in computer vision projects. Initially developed by Intel and now maintained by the open-source community, CVAT caters to data scientists, machine learning engineers, and annotation teams across industries such as autonomous vehicles, medical imaging, and surveillance. The platform supports a wide range of annotation tasks, including object detection, image classification, and segmentation, and offers both manual and AI-assisted labeling tools.

What are the main features of CVAT?

Comprehensive Annotation Tools

CVAT provides a variety of tools to accommodate different annotation needs:

• Bounding Boxes: For object detection tasks.

• Polygons and Polylines: Suitable for segmenting complex shapes and linear structures.

• Points and Skeletons: Ideal for keypoint detection and pose estimation.

• Cuboids: For 3D object representation in 2D images.

• Brush Tool: Allows for freehand segmentation, useful in medical imaging.

• Tags: For image-level classification tasks.

These tools enable precise and efficient annotation across various computer vision applications.

AI-Assisted Annotation

To expedite the labeling process, CVAT incorporates AI-assisted features:

• Automatic Annotation: Utilizes integrated AI models or allows users to add custom models to automate labeling.

• Interpolation: Automatically propagates annotations across video frames, reducing manual effort.

• Semi-Automatic Tools: Includes intelligent scissors and other interactive algorithms to assist with complex annotations.

These features can significantly reduce the time and effort required for large-scale annotation projects.

Support for Diverse Data Formats

CVAT supports a wide array of data formats:

• Image Formats: JPEG, PNG, BMP, GIF, TIFF, and more.

• Video Formats: MP4, AVI, MOV, among others.

• 3D Point Clouds: Supports formats like PCD and BIN for LiDAR data.

Additionally, annotations can be exported in various formats compatible with popular machine learning frameworks, including COCO, YOLO, Pascal VOC, and TFRecord.

Collaborative Project Management

CVAT is designed to facilitate team-based annotation projects:

• Role-Based Access Control: Assign roles such as annotator, reviewer, or administrator to team members.

• Task Management: Create and assign tasks, monitor progress, and manage workflows.

• Review System: Implement a review process to ensure annotation quality and consistency.

These features support efficient collaboration among distributed annotation teams.

Deployment Options and Integrations

CVAT offers flexible deployment options to suit different organizational needs:

• Cloud-Based: Use CVAT online for quick access without installation.

• Self-Hosted: Deploy CVAT on-premises using Docker or Kubernetes for greater control and customization.

The platform also integrates with tools like Roboflow and Hugging Face, enabling seamless incorporation into existing machine learning pipelines.

Why choose CVAT?

• Versatility: Supports a broad range of annotation types and data formats.

• Efficiency: AI-assisted tools and interpolation features accelerate the annotation process.

• Collaboration: Built-in project management tools facilitate teamwork and quality control.

• Flexibility: Offers both cloud-based and self-hosted deployment options.

• Community-Driven: As an open-source project, CVAT benefits from continuous contributions and improvements from a global community.