Object Detection Model Training
Ground-truth datasets for training and benchmarking detectors, from lightweight real-time models to transformer-based 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 seem straightforward, and that's precisely why they degrade so quietly at scale — boxes drawn too loose, occlusions handled inconsistently, and borderline class calls made differently by different annotators all chip away at model mAP over time. Vidyut Data holds every box to defined pixel-tightness tolerances, explicit occlusion rules, and clear class-boundary decisions drawn from a continuously maintained labeling guide, with IoU-based QA sampling applied to each batch.
What's included
- ✓2D bounding boxes with configurable pixel-tightness tolerances
- ✓Defined rules for occlusion, truncation, and crowded scenes
- ✓Small-object and high-density scene annotation
- ✓Class balancing with instance-count reporting
- ✓IoU-audited QA with per-batch accuracy reports
- ✓Delivery in COCO, YOLO, TFRecord, or custom formats
Use cases
Wildlife & Conservation Monitoring
Detecting and counting animals in camera-trap, drone, or aerial imagery to support population and habitat studies.
Traffic & Smart City Systems
Vehicle, pedestrian, and infrastructure detection from street-level and intersection camera feeds.
Warehouse & Inventory Automation
Detecting pallets, packages, and stock items to support automated inventory tracking and fulfillment systems.
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.