CVAT ONLINE

Cloud Data Annotation Platform for Vision AI Teams

Turn raw images, videos, and 3D point clouds into model-ready datasets. Run the full workflow from dataset distribution to AI-assisted labeling to quality review, all in one managed workspace.

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CVAT PLATFORM

All-in-one data annotation platform for vision AI teams

ANNOTATION EDITOR

Full-featured annotation editor for simple and complex vision tasks

Create accurate labels with dedicated shapes, modes, and editing controls for classification, segmentation, and tracking.

Multimodal data support

Annotate images, videos, 3D LiDAR point clouds, and audio directly in your browser.

Diverse labeling shapes & tools

Use the right annotation type for the task, from bounding boxes and polygons to pixel masks, skeletons, and 3D cuboids.

Multiple annotation modes

Switch between dedicated modes for standalone objects, sequences, frame-level classes, and object attributes.

Advanced shape and track editing

Refine and restructure annotations without redrawing them using snap, merge, group, split, join, and slice tools.

Video tracking and interpolation

Track objects across frames with automatic shape interpolation between keyframes.

Purpose-built tools for 3D data

Place, resize, rotate, and track cuboids in point clouds with projection views for precise positioning and orientation.

Bulk shape conversion

Convert masks, polygons, and rectangles in bulk without recreating existing annotations.

Layers, filters, and canvas controls

Organize the canvas to match your workflow with layers, object filters, visibility controls, image adjustments, and customizable shortcuts.

AI LABELING

AI-assisted labeling with built-in and custom models

Speed up annotation by pre-labeling data with native models, models from external hubs, or your own models.

Segment matching objects with SAM 3
Quickly segment all matching objects in a frame with Segment Anything 3 using a text or visual prompt.

Track objects across frames with SAM 2
Track objects across frames automatically with Segment Anything 2 instead of recreating them frame by frame.

Bring the right pretrained model for the task
Connect any compatible model from supported hubs to pre-label data for your specific use case.

Connect your own models through AI Agents
Generate first-pass annotations with models trained on your own data.

Team Collaboration

Organized annotation workflows and team collaboration

Easily organize, manage, and review annotation projects across parallel workstreams for in-house or outsourced annotators.

Structured workflows for annotation projects

Manage the full workflow of setting up projects, splitting datasets, assigning jobs, reviewing, and approving annotations.

Define labels and attributes once at the project level
and apply them across every task and job.

Automatically split large datasets into manageable jobs and distribute them for parallel annotation.

Track assignees, workflow stages, status, and completion for every project, task and job.

Team collaboration

Highlight and discuss annotation issues, share project instructions, and align on labeling decisions.

Secure role-based controls

Keep your data safe through organization and project-level roles.

Team activity audit trail

Monitor user activity and maintain a traceable record across projects, tasks, and jobs.

Advanced data annotation QA

Catch labeling errors before they reach your training set with a comprehensive set of manual and automated validation tools.

Benchmark jobs against Ground Truth.
Automatically spot missing or extra annotations and label mismatches by comparing regular jobs against validated Ground Truth jobs.

Resolve ambiguity through consensus.
Reduce individual bias in ambiguous cases by automatically comparing independent annotations and flagging disagreements for review.

Review and resolve annotation issues.
Use Review mode to open issues on specific objects or frame regions, discuss corrections, and return jobs for rework.

Immediate Job Feedback. Give job assignees an instant quality score upon completion, so below-threshold work can be corrected immediately.

Analytics

Annotation analytics and reporting

Get insights into your labeling team's performance. Spot bottlenecks and balance workloads to keep dataset delivery on track.

Measure time and annotation speed

Track total working time and average annotation speed to estimate effort and understand labeling throughput.

Find workflow bottlenecks

Break down activity by assignee, workflow stage, job status, and date to see where work slows down or waits for the next handoff.

Analyze annotation output

Review object counts by label and annotation type, along with tracks, keyframes, and interpolation rates, to understand dataset composition and labeling progress.

Export data for deeper analysis

Export annotation statistics and event data for custom reporting and analysis.

CASE STUDY

USA

“CVAT is central to our annotation pipeline for two reasons: SAM-powered labeling that saves critical time for busy surgeons, and multi-user access that enables seamless collaboration for researchers across continents.”

Luca Morgantini

MD Resident, Department of Urology

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CASE STUDY

CANADA

“The reason I chose CVAT is because whenever I had a question or an issue, I got a quick response from the team. Unlike other tools, the team behind CVAT was always listening and willing to work with me. The labeling platform is great and user-friendly, but it's the team behind it that makes the difference.”

Véronica Romero-Rosales

Co-founder, Robonotic

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CASE STUDY

GERMANY

“I love CVAT because it's so hands-on, simple and straightforward. We tried other well-known tools before, but there was a lot of friction when we tried to get them up and running, and the UI was harder to work with.”

Andre Kempe

CEO & Product Owner, ProMetronics

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CASE STUDY

NETHERLANDS

“The biggest win for us is the model-assisted annotation loop. We run YOLO and SAM2 pre-annotation directly through CVAT Online, so the work is mostly verifying and correcting rather than labeling from scratch. For a small operation producing the computer-vision training data behind our product, that's the difference between a dataset taking weeks versus days.”

Fabian van Schevikhoven

Co-founder, Younalize

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COMMUNITY VOICE

“We have a dedicated annotation team within our company, comprising over 50 annotators. For the past four years, we have been using self-hosted CVAT, which has been functioning exceptionally well. Recently, we acquired a project that requires annotating approximately 1 million images and videos monthly. We tried various tools, such as Supervisely, Label Studio etc, especially for video annotation, but CVAT remains the best option.”

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INTEGRATIONS

Built to fit into your ML pipeline, not the other way around

Connect CVAT to your existing stack and automate annotation workflows without replacing your current tools or infrastructure.

Automate workflows with the API and SDK

Create projects and tasks, upload data, monitor jobs, and retrieve annotations programmatically.

Script repeatable workflows with the CLI

Script project and task creation, dataset import and export, backups, and auto-annotation from a terminal or CI pipeline.

Trigger downstream steps with webhooks

Send project, task, and job events to external systems for notifications, handoffs, and downstream automation.

Connect your cloud storage

Import and export datasets through Amazon S3, Azure Blob Storage, Google Cloud Storage, and S3-compatible services.

Export in 20+ computer vision formats

Deliver finished annotations in COCO, YOLO, KITTI, Cityscapes, Pascal VOC, and other formats used by downstream ML tools.

Use prebuilt integrations

Connect CVAT to supported model hubs, dataset tools, and crowdsourcing workflows without building every connection from scratch.

PRICING

Plans for individuals and teams

Start free and upgrade as your annotation workload or team grows.

Compare all plans and features

Free

For exploring CVAT and
small projects

What`s included:

Core annotation editor

Manual review

Ground Truth and Honeypot QA

API access

Annotation export

Solo

From$23/month

For individual professionals
with ongoing labeling work

Everything in Free, plus:

Full dataset export

Analytics and reporting

Batch auto-annotation

SAM 2 and SAM 3 image segmentation

Team

From$23/user/month

For in-house teams and
outsourced labelers

Everything in Solo, plus:

Shared organization workspace

Team assignments and reviews

Role-based access controls

SSO and audit logs

CUSTOMER REVIEWS

Why AI teams choose CVAT for labeling their data

4.6/5

At e2m, we use CVAT to review computer vision detections of animals in massive camera trap image datasets. We found CVAT to be the best tool at this scale, enabling efficient image review and robust quality control.

Joshua Wilson

Senior Computational Ecologist

tbmaestro uses CVAT as a key tool to structure large-scale annotations for equipment detection on our inspection photos and to accelerate computer vision deployment in asset management.

Anthony Renard

AI Engineer

Our mission is to make vessels safer for every crew at sea. CVAT plays a critical role in that, helping us transform raw CCTV footage from maritime environments into the training data for our AI detection systems. From identifying unsafe behaviour to surfacing near-miss situations before they escalate, CVAT is the foundation that makes our safety intelligence possible.

Vladimir Ponomarev

CEO

CVAT helps us label sports images to build high-quality ground truth that enables our AI to analyze volleyball performance and provide personalized feedback. It gives us the flexibility to combine automated and human labeling, and makes it easier to test different models and refine the best approach for our AI.

Fabian van Schevikhoven

Founder

At Arboair, we annotate forestry imagery to deliver world-class tree analysis worldwide, and CVAT has been key to making that possible at scale. Its ease of use and flexibility across annotation use cases support our full workflow, from production models to experimental projects.

Jacob Hjalmarsson

COO

Vimaan uses CVAT as its primary data annotation tool and selected this tool after a thorough evaluation of alternative tools both in open source and closed domain. Top 3PLs and warehouses have improved their inventory accuracy, reduced mis-shipments, improved bin utilization, while reducing overall resource.