Object detection
Training data for detectors from YOLO to DETR-class architectures.
Computer Vision
Bounding Box Annotation Services
Tight, consistent 2D bounding boxes for object detection models — delivered at scale with accuracy SLAs.
Overview
Bounding boxes look simple, which is exactly why they're easy to get wrong at scale: loose boxes, missed occlusions, and inconsistent class decisions quietly degrade mAP. Vidyut Data enforces pixel-tolerance standards, occlusion rules, and class-boundary decisions from a living labeling guide, with IoU-based QA sampling on every batch.
What's included
- ✓2D boxes with configurable pixel-tightness tolerances
- ✓Occlusion, truncation, and crowd handling rules
- ✓Small-object and dense-scene annotation
- ✓Class balancing and instance-count reporting
- ✓IoU-audited QA with per-batch accuracy reports
- ✓Delivery in COCO, YOLO, TFRecord, or custom formats
Use cases
Shelf analytics
Product and planogram detection in retail imagery.
Aerial imagery
Vehicle, building, and asset detection in drone and satellite data.
Document layout
Region detection for tables, figures, and text blocks.
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 forbounding box annotation 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 yourbounding box annotation services?
Start with a pilot batch — see our quality on your data before you commit.