Autonomous driving
Road, lane, vehicle, and pedestrian scene understanding.
Computer Vision
Semantic Segmentation Services
Class-level pixel labeling that teaches models to understand every region of an image.
Overview
Semantic segmentation assigns every pixel a class — no gaps, no overlaps — which makes annotation quality brutally visible. Vidyut Data's segmentation teams use edge-refinement tooling plus human review passes to deliver dense masks with clean class boundaries, validated by per-class IoU sampling against gold references.
What's included
- ✓Full-scene dense pixel labeling
- ✓Per-class IoU quality targets and reporting
- ✓Boundary refinement for thin structures (poles, lane lines, wires)
- ✓Large ontologies with class-priority rules
- ✓Panoptic-ready outputs (stuff + things)
- ✓Masks delivered as PNG, RLE, or polygon sets
Use cases
Medical imaging
Organ and lesion segmentation with expert review.
Satellite imagery
Land-use and land-cover classification at scale.
AR & spatial computing
Scene parsing for occlusion-aware rendering.
How we work
1
Scope & pilot
We review your data, define the labeling guide together, and run a paid pilot batch so you can judge quality before scaling.
2
Team & calibrate
A dedicated, trained team ramps on your guidelines, benchmarked against gold-standard tasks until accuracy targets are hit.
3
Produce & QA
Production batches flow through multi-tier QA — consensus review, statistical sampling, and edge-case escalation.
4
Deliver & iterate
Data ships in your format with accuracy reports. Guidelines evolve with your model's failure cases.
Frequently asked questions
How do you guarantee quality forsemantic segmentation services?
Every project runs through multi-tier QA: annotators are benchmarked against gold-standard tasks before production, batches are statistically sampled against agreed accuracy targets, and ambiguous cases are escalated and documented in a living labeling guide. You receive accuracy reports with every delivery.
Can we start with a small pilot before committing?
Yes — we recommend it. A paid pilot batch on your real data lets you evaluate our quality, turnaround, and communication before scaling. Pilot learnings become the project's labeling guide.
How is our data kept secure?
Client data is encrypted in transit and at rest, access is limited to the assigned project team under NDAs, and we support VPN-restricted or client-hosted workflows where data cannot leave your environment. Retention and certified deletion terms are set per engagement.
What tools and output formats do you support?
We work in your annotation platform or ours, and deliver in the format your pipeline expects — COCO, YOLO, Pascal VOC, JSON, CSV, or a custom schema — with delivery via API, cloud bucket, or scheduled export.
Ready to scale yoursemantic segmentation services?
Start with a pilot batch — see our quality on your data before you commit.