Visual Search & Product Discovery
Attribute and category tagging that powers image-based search and “shop the look” style recommendation engines.
Computer Vision
Image Annotation Services
Precise, QA-verified image labeling for classification, detection, and segmentation — across any domain.
Overview
Image annotation sits at the foundation of every vision model, and small errors don't stay small — a 2% labeling mistake at the data layer can surface as a real production failure downstream. Vidyut Data's trained annotators work from your labeling guidelines and pass every batch through layered QA — peer consensus, gold-standard benchmarking, and statistical sampling — to deliver datasets that consistently meet agreed accuracy targets, whether you're working with 10 classes or 10,000.
What's included
- ✓Image classification and multi-label tagging
- ✓Object detection using bounding boxes and polygons
- ✓Attribute-level annotation (occlusion, truncation, pose, state)
- ✓Support for hierarchical taxonomies with large class counts
- ✓Edge-case escalation with iterative guideline refinement
- ✓Delivery in COCO, Pascal VOC, YOLO, or your custom schema
Use cases
Agriculture & Crop Monitoring
Labeled imagery for identifying crop health, pest damage, and growth stages from drone or field-camera footage.
Manufacturing Quality Control
Defect and anomaly labeling to train automated visual inspection systems on production lines.
Content Moderation
Labeling for identifying policy-violating, sensitive, or restricted image content across user-generated platforms.
Frequently asked questions
How do you guarantee quality on image annotation?
Every project runs through layered QA — annotators are benchmarked against reference tasks before production, batches are sampled against agreed accuracy targets, and ambiguous cases are escalated and documented in a living guide. You get accuracy reports with each delivery.
Can you handle very large or hierarchical class taxonomies?
Yes — from a handful of classes to thousands, with class-priority rules so annotators resolve overlapping categories the same way every time.
What annotation formats do you deliver in?
COCO, Pascal VOC, YOLO, or your custom schema, via the delivery method your pipeline uses.
Can we start with a small pilot before committing?
Yes — a paid pilot lets you evaluate quality, turnaround, and communication before scaling.
Ready to scale your image annotation services?
Start with a pilot batch — see the quality of the data before you commit.