AI and ML Network Services
Bounding Box Annotation Services
Professional 2D and 3D bounding box annotation services for YOLO, Faster R-CNN, and object detection models. Tight box placement, consistent labeling, and 99%+ accuracy guaranteed.
AI and ML Network provides precision bounding box annotation services for object detection AI models. We deliver tight, consistent box labels that maximize your model’s mAP and localization accuracy.
2D Bounding Box Annotation
Standard rectangular annotation for camera-based object detection. We support all major formats:
- YOLO format — normalized coordinates (class_id x_center y_center width height)
- COCO JSON — absolute pixel coordinates (x_min, y_min, width, height)
- Pascal VOC — XML format (xmin, ymin, xmax, ymax)
Every box is placed with strict tightness standards — minimal background inclusion, consistent handling of occlusion and truncation, and uniform class assignment across the entire dataset.
3D Bounding Box Annotation
Cuboid annotation for LiDAR point cloud data and sensor fusion datasets:
- Position (x, y, z) in 3D space
- Dimensions (length, width, height)
- Rotation angle
- Object class and tracking ID
Used for autonomous driving, robotics, warehouse automation, and drone perception.
Why Box Tightness Matters
Loose bounding boxes are the single most common annotation error and the single most impactful quality variable on detection model performance. When boxes include excessive background, the model learns background context as part of the object — reducing precision, increasing false positives, and degrading localization IoU.
Our annotation guidelines enforce strict tightness standards with visual reference examples, and our QA process catches loose boxes before they reach your training pipeline.
Industries We Serve
- Autonomous driving — vehicle, pedestrian, cyclist, and traffic sign detection
- Retail analytics — product detection and shelf monitoring
- Security surveillance — person and vehicle detection from CCTV
- Manufacturing — defect detection and part recognition
- Medical imaging — lesion and anatomical structure detection
- Agriculture — crop and pest detection from drone imagery
If you need bounding box annotation, go to the Start Project page and send your requirements. We provide a free sample batch so you can judge quality before committing.
Frequently Asked Questions
What is bounding box annotation? +
Bounding box annotation is the process of drawing rectangles around objects in images to define their location and class. In YOLO format, each box is represented as class_id x_center y_center width height with normalized coordinates. It is the foundation of all object detection model training.
Do you provide 3D bounding box annotation? +
Yes, we provide 3D bounding box (cuboid) annotation for LiDAR point cloud data and camera-LiDAR fusion datasets. 3D annotations capture position (x, y, z), dimensions (length, width, height), and rotation angle — essential for autonomous driving and robotics perception.
How tight should bounding boxes be? +
Bounding boxes should hug the object as closely as possible with minimal background padding. Loose boxes that include excessive background pixels cause the model to learn background context as part of the object, degrading localization accuracy and increasing false positive rates.
What is the cost of bounding box annotation? +
Simple bounding box tasks on clean objects cost $0.03-0.15 per box. Moderate complexity with occlusion and varied backgrounds costs $0.15-0.50 per box. High complexity including dense scenes, small objects, and 3D cuboids costs $0.50-2.00+ per box.
Ready to Start?
Get a free sample batch to test our quality before committing.