What you provide.
Images, class ontology and spatial labeling instructions.
Image Annotation
Add spatial and semantic labels to images for computer vision workflows.
Request a SampleWhat the service does
From raw input to reviewable structure.
Add spatial and semantic labels to images for computer vision workflows. Labels and instructions are calibrated with your team on a small pilot before larger batches begin.
Typical tasks
- Bounding boxes
- Polygons
- Semantic, instance and panoptic segmentation
- Landmarks, keypoints and polylines
- Classification and captioning
What you receive.
Geometry, class labels and review metadata tied to image IDs.
Demo / Illustrative example
Make the output tangible.
This synthetic example shows the structure of a record. It is not a client dataset or a claim of project performance.
vehicle · box 01
DEMO / ILLUSTRATIVE EXAMPLE{ "image_id": "demo-01", "class": "vehicle", "bbox": [24, 40, 160, 90] }Human-in-the-loop quality
Quality is a process. Not a percentage on a page.
Acceptance thresholds, review methods and sampling are agreed for each project. Calibration happens before volume.
Annotator review
Peer review
Quality reviewer
Sample audit
Client feedback loop
We document uncertainty and disagreement, revise guidelines with your team and keep an audit trail of corrections. Specialist medical, legal or financial review is available subject to project requirements and qualified reviewer availability.
Use cases
A fit for your data workflow.
Computer visionInventory detection
Supported formats
Agree the schema first.
COCOYOLOJSONMasks
Shared responsibilities.
Clear delivery options.
You retain responsibility for source rights, lawful access, required approvals and intended use. We agree secure transfer, retention and deletion arrangements in the project scope.
- Client-approved guidelines and representative inputs
- A project owner for edge-case decisions
- An agreed acceptance rubric and sample audit
- Pilot, batch or milestone-based delivery
- Versioned exports and a documented handover
Project questions
Scope the work with confidence.
What do we need to provide?
Images, class ontology and spatial labeling instructions. You also provide lawful access and usage rights, security requirements, acceptance criteria and a project owner who can resolve ambiguities.
How is annotation quality measured?
We agree a task-specific rubric, calibration pilot and sampling plan. Peer review, quality review and client feedback inform acceptance. No universal accuracy percentage is advertised.
Can you handle specialist subject matter?
Specialist medical, legal, financial or expert RLHF work is available subject to project requirements and qualified reviewer availability. Suitability is confirmed during scoping.
What delivery options are available?
Pilot batches, milestone-based deliveries or a scoped recurring workflow. Typical formats include COCO, YOLO, JSON, Masks; exact schemas, tools, volumes and timelines are agreed before starting.
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