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 keep boxes consistent across millions of images?
Tightness tolerances, occlusion rules, and class-boundary decisions are fixed in the labeling guide and enforced with IoU-based sampling on every batch, so consistency holds as volume scales.
Can you handle dense scenes and very small objects?
Yes — crowded scenes and small-object annotation are covered, with specific rules for occlusion, truncation, and crowd handling.
What formats do you deliver in?
COCO, YOLO, TFRecord, or a custom format — whatever your detector expects.
Can we start with a small pilot before committing?
Yes. A pilot lets you check box tightness and consistency against your standard before you scale.
Ready to scale your bounding box annotation services?
Start with a pilot batch — see the quality of the data before you commit.