Data labeling tool built for computer vision teams
ImageLabeling is a data labeling tool for teams that ship computer vision models and retrain them often. It treats labeling as part of the ML pipeline, not as a side project in spreadsheets: data comes in by upload or API, AI drafts the labels, people fix and approve them, versions are frozen, and training code pulls the export. This page explains how the tool fits into the way a vision team already works.
Where labeling usually breaks in a growing team
The first thousand images are labeled by the ML engineer who needs them. The next ten thousand are split across interns, contractors and a shared drive. Classes drift, someone labels a forklift as a vehicle, exports from three different tools refuse to merge, and nobody knows which labels the last model was trained on. A data labeling tool should prevent each of those failures by design, not by a team wiki.
- One label schema per project, shared by every labeler
- One review step before anything reaches training
- One frozen version per training run
Data labeling software that fits the ML pipeline
Labeling is a loop. You train, find weak classes, collect more images, label them, retrain. ImageLabeling is built around that loop. Upload new images by drag and drop, zip or cloud bucket URL, or push them through the API on Team and higher. Pre-label them with the AI model against your current schema. When a reviewer approves a batch, a webhook on Business tells your pipeline, which fetches the frozen version and starts training.
- API on Team and higher for upload, fetching labels and triggering exports
- Webhooks on Business and higher, including an event when a batch is reviewed
- Exports in COCO JSON, YOLO txt plus data.yaml, Pascal VOC XML and CSV
Label schemas that do not drift
A schema holds your classes, their colors, attributes per object and hierarchical classes such as vehicle, then truck and forklift under it. Save a schema as a template and start the next project with the same definitions. When the schema changes, every labeler sees the change at once, and version compare shows which labels were affected.
- Classes, colors and attributes in one place
- Hierarchical classes for coarse and fine labels
- Reusable templates across projects
Labelers, reviewers and the people who just watch
Roles are owner, admin, reviewer, labeler and viewer, with access per project. A new labeler starts on a small batch, the reviewer sends back mistakes with a comment, and consensus on a sample of images (Team and higher) shows whether the new person agrees with the rest. Business adds a QA score per labeler. The decision maker view of these controls is on the data annotation platform page.
Versions you can retrain on
Freeze a version after review and it stays exactly as it was. Compare two versions to see added, changed and removed labels, and keep the train, validation and test split stable so evaluation numbers stay comparable between runs. Starter keeps 3 versions, every other plan keeps unlimited versions.
Annotation types the tool covers
Boxes, polygons, points and image classes are on every plan. Team adds segmentation masks and attributes. Business adds keypoint skeletons and custom schemas. The individual tools are explained on the data annotation tools page, and the AI drafting step on the AI data labeling page. For a step by step pass, see the image annotation workflow.
What it costs to run
Plans start at 49 USD per month, or 24 USD per month billed yearly. Team is 149 USD per month, Business 499 USD and Enterprise 1,499 USD, each half price per month when billed yearly. Manual labeling is not metered, only AI pre-labels count against a monthly quota. See pricing for every limit.
FAQ
Can I connect the data labeling tool to my training code?
Yes. On Team and higher the API fetches labels and triggers exports. On Business, webhooks notify your pipeline when a batch is reviewed.
Can I bring labels from another tool?
Yes. Import COCO JSON, YOLO txt, Pascal VOC XML or CSV and the labels appear as editable objects on your images.
Does it run in the browser?
Yes. Labelers work in the browser with nothing to install, which is covered on the annotate image online page.
What happens to data when we cancel?
Access runs to the end of the paid period. Export your versions before then, and request deletion if you want your images removed.
Put labeling inside your pipeline
Create a workspace, define your schema and pre-label your first batch.