Accepted inputs
- JPG, JPEG, PNG, TIFF and other agreed image formats
- Extracted video frames
- Existing pre-annotations requiring correction
- Client-hosted datasets inside an annotation platform
IMAGE ANNOTATION SERVICES
Image Annotation Services for Computer Vision
We turn image collections into consistent training and validation data using the geometry, taxonomy and output structure your model requires. Before production, we align object definitions, visibility rules, attributes and ambiguous cases through a representative calibration batch.
SCOPE
What this service covers
Axis-aligned and oriented bounding boxesPolygon outlines for irregular objectsKeypoints and landmark skeletonsSingle-label, multi-label and hierarchical classificationObject and image-level attributesNull, quality and exclusion flags
Annotation approach
- Class-taxonomy and attribute mapping
- Minimum-size and visibility thresholds
- Occluded and truncated object states
- Batch-based production with documented questions
- COCO, YOLO, CVAT and client-defined exports
Common edge cases
- Crowded or overlapping objects
- Reflections and duplicated appearances
- Partially visible or motion-blurred targets
- Uncertain class boundaries
- Duplicate or near-duplicate frames
QUALITY ASSURANCE
Review that follows the guideline
Quality checks are selected for the data type and acceptance criteria. Metrics and review coverage depend on the project, platform and agreed workflow.
Missed-object and false-positive reviewClass and attribute verificationGeometry tightness and coverage checksGuideline deviation trackingExport structure and file-reference validation
Applications
- Object detection
- Vehicle and damage inspection
- Retail and visual search
- Aerial and geospatial analysis
- Agriculture
- Security and smart-city perception
Output formats
- COCO JSON
- YOLO TXT
- CVAT XML/JSON
- Pascal VOC
- Client-defined JSON or CSV
What to include in your brief
- Representative sample images
- Class list and definitions
- Visibility, occlusion and minimum-size rules
- Required attributes and output format
- Volume, schedule and acceptance criteria
START WITH A CALIBRATION BATCH
Reduce uncertainty before production.
Share your guidelines, sample characteristics, volume, preferred platform and timeline. We’ll assess the scope and propose a representative calibration step.
Discuss your dataset