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Flare Box LogoLabelOp

Annotation workflows
for teams shipping vision models.

Upload, annotate, review, and export computer vision datasets in one workspace.

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No credit card required.

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How It Works

From upload to export, one connected workflow.

Upload, label, review, export, and train without breaking the flow.

Workflow map

The boring handoffs disappear.

Upload

Bring media into one queue.

Validate & Split

Check structure and prep the split.

AI & Manual Labeling

Pre-label, then refine by hand.

Review

Approve before release.

Export

Package the dataset.

Train

Launch the next run.

Core Capabilities

Built for high-velocity vision teams

AI Pre-label & Human Loop

Speed

Sub-second pre-labeling combined with intuitive human review workflows.

Universal Format Export

Formats

Seamless export to COCO, YOLO, Pascal VOC, LabelMe, CVAT, CSV, and JSONL.

Local & Cloud Inference

AI Engine

Run SAM2 segmentations locally in browser or connect managed GPU models.

Team Roles & QA Queues

Collaboration

Assign batch queues, set member permissions, and track real-time team stats.

Immutable Versioning

Control

Snapshot dataset states, freeze release tags, and maintain complete audit logs.

Platform features

Feature overview

Annotation workspace

Open any project image and annotate without leaving the review workflow.

  • Canvas, toolbar, gallery, and side panel stay together
  • Boxes, points, polygons, and SAM2 use the same workspace
  • Labels and saved annotations stay visible while labeling
  • Undo, redo, filters, and image navigation are part of the flow
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Start free today.

Free Tier

Free Tier

$0forever

A managed hosted tier for individuals and small evaluations

Get started free
All features and limits
2 team members · 1 project
2,000 images · 2 GB managed storage per project
Managed AI-assisted labeling: 200 runs per day
Unlimited local AI-assisted labeling
Unlimited review workflows
Unlimited export formats
3 version snapshots per project

Pro + optional packages

Pro

Coming soon
$49/month + tax

Higher managed limits with optional capacity packages

All features and limits
5 team members · 5 projects
10,000 images · 20 GB managed storage per project
Managed AI-assisted labeling: 5,000 runs per day
Unlimited local AI-assisted labeling
Unlimited review workflows
Unlimited export formats
Optional people, project, dataset, and managed AI packages
Add capacity only when you need it. Increases are immediate and prorated; decreases take effect next period after usage checks.
Compare Free Tier and Pro

Questions before you move the workflow.

Which AI models are supported for batch labeling?

LabelOp supports managed cloud models and local offline models (such as SAM2) for AI pre-labeling. You can run inferences across entire datasets or selected image ranges with real-time progress tracking and confidence controls.

How do I assign annotation tasks to team members?

Create assignments for individual images or image ranges, set priorities (low, medium, high, urgent), add due dates and instructions, and track completion status. Assignees see their tasks in a dedicated queue with filters for status, priority, and project. Project owners and reviewers can monitor assignment progress, view annotator performance metrics, and reassign tasks as needed. All annotations are linked to their assignments for easy tracking.

How does collaboration and review work?

Invite teammates with owner, reviewer, or annotator roles, assign tasks, and filter queues by status, split, or assignee. When email delivery is configured, the service emails the invitation; otherwise the owner shares the generated invitation link manually. Audit logs and review workflows help teams coordinate quality.

Can I train custom models using GPU compute?

Yes, by connecting your own Vast.ai account. LabelOp helps browse offers and monitor jobs, but does not provide or resell the GPU. Vast.ai terms, availability, quota, and charges apply to the project owner's credential.

Can I audit dataset versions and roll back changes?

Create named dataset version snapshots from the project team area, compare two versions to see what changed, and rely on audit logs for traceability. Snapshots capture annotation state so you can align training releases with a specific checkpoint.

What export formats and integrations do you support?

Export to COCO JSON, YOLO, Pascal VOC XML, LabelMe JSON, CSV, TSV, CVAT XML, or JSONL in a single click. Exports preserve annotation metadata, confidence scores, and creator information. You can filter exports by label, split, annotator, or date range.

Is my data private and secure?

The hosted service uses access controls, encrypted transport, and encrypted saved provider credentials. No system can promise absolute security. Browser-side models can reduce third-party transfer, but you remain responsible for your device, storage provider, sharing settings, and backups.

How does LabelOp handle large datasets?

Capacity depends on hosted platform guardrails, browser memory, and database limits. Free Tier and Pro publish concrete limits; optional Pro packages expand people, projects, or pooled dataset capacity without changing import, annotation, file-safety, rate, or abuse limits.

Does LabelOp fund GPU usage?

No. LabelOp does not currently fund or resell GPU usage. You can connect a Vast.ai account and control provider-side budgets and limits yourself.