Written guidelines before volume
Class definitions, edge cases, and rejection criteria agreed and documented before bulk work begins.
Data Services
Image Categorisation
Whole-image classification against your taxonomy, including multi-label and hierarchical schemes.
Overview
Straightforward in principle, and almost always harder in practice because the taxonomy is underspecified. We spend the first pass tightening class definitions against real examples.
Supports multi-label and hierarchical schemes, with a documented rule for every ambiguous case we hit.
What you receive
- Annotated dataset in your training format
- Written annotation guidelines
- Quality report with agreement metrics
- Sample batch for sign-off before volume
Typical stack
- CVAT
- Label Studio
- COCO
- YOLO
- Pascal VOC
- Python
Capabilities
What this covers in practice
Calibrated annotator teams
Annotators calibrated against a reference set, with agreement measured before they join a project.
Independent review pass
A second annotator reviews sampled output, with rework triggered on defined error thresholds.
Delivery in your format
COCO, YOLO, Pascal VOC, or a custom schema - exported to match your existing training pipeline.
Related
Also under Data Services
Data Engineering & Pipelines
Ingestion, transformation, and quality checks that turn scattered operational data into something a model or a report can rely on.
Read moreSemantic Segmentation
Pixel-level class labels across an image, for models where a boundary drives a measurement rather than a bounding box.
Read more2D Bounding Boxes
Rectangular object annotation for detection and counting tasks, at the volumes detection training actually needs.
Read moreWant to talk through a image categorisation project?
Send us the shape of the problem and we'll come back with a scoped approach, a timeline, and an honest read on what's achievable.