Written guidelines before volume
Class definitions, edge cases, and rejection criteria agreed and documented before bulk work begins.
Data Services
Semantic Segmentation
Pixel-level class labels across an image, for models where a boundary drives a measurement rather than a bounding box.
Overview
Every pixel is assigned a class, which is what you need when the downstream task measures area, extent, or contact between regions rather than counting objects.
We agree the class taxonomy and edge-case rules with you before volume work starts, because ambiguity in the guidelines is what produces inconsistent datasets.
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 more2D Bounding Boxes
Rectangular object annotation for detection and counting tasks, at the volumes detection training actually needs.
Read more3D Cuboid Annotation
Three-dimensional extents and orientation for objects in a scene, where depth and heading matter to the model.
Read moreWant to talk through a semantic segmentation 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.