LightTag
The high-throughput text annotation platform for professional NLP teams.
The industry-standard open-source platform for professional data labeling and computer vision management.
Computer Vision Annotation Tool (CVAT) is a high-performance, web-based platform designed for the complex requirements of professional data annotation for computer vision models. Originally developed by Intel and now managed by CVAT.ai, the platform has evolved into a comprehensive data management suite in 2026, offering seamless support for 2D images, video interpolation, and 3D point cloud (Lidar) data. Its architecture is built around a robust Django backend and a React frontend, optimized for high-throughput labeling tasks. CVAT distinguishes itself through its tight integration with automated annotation tools like Segment Anything (SAM) and YOLO models via Nuclio, allowing teams to leverage AI-assisted pre-labeling. This reduces manual effort by up to 80% in high-density scenarios. In the 2026 market, CVAT maintains a dominant position as the bridge between open-source flexibility and enterprise-grade SaaS reliability, supporting diverse deployment models from local Docker containers to fully managed cloud environments. It remains a critical piece of the MLOps pipeline for industries ranging from autonomous driving to precision agriculture, providing granular quality control, role-based access, and deep versioning capabilities.
Integration with Segment Anything (SAM) and custom models via Nuclio serverless framework.
The high-throughput text annotation platform for professional NLP teams.
Enterprise-grade automated data labeling and dataset curation for production-ready AI models.
The leading API-driven platform for high-quality human translation and AI training data.
High-fidelity human feedback and RLHF infrastructure for enterprise-grade model alignment.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Support for Lidar data (PCD, BIN) with specialized viewports for Top/Side/Front perspectives.
Linear and non-linear interpolation of object positions between keyframes in video.
Directly mount S3, Azure, or GCS buckets as data sources without moving files to CVAT servers.
Configurable keypoint structures with parent-child relationships for human pose estimation.
Full programmatic access to all UI functions through the CVAT REST API and PyCVAT library.
Specific user roles for 'Annotator' and 'Reviewer' with status tracking for every 'Job'.
Identifying thousands of dynamic objects (cars, pedestrians) across multi-sensor Lidar and Video data.
Registry Updated:2/7/2026
Export to Kitti format
Differentiating weeds from crops in high-resolution aerial imagery for robotic spraying.
Precise pixel-level segmentation of anomalies in MRI scans.