2D bounding box annotation for object detection models Tight boxes around every object, drawn the same way in every batch
Bounding box annotation services for detection and search Damco draws the boxes your model learns to find objects from
How our bounding box annotation works From your class list to boxed images in your training format
- Week 1Agree the box rulesTightness, cut-off objects, hidden objects, and the smallest object worth a box.
- Week 2Run a pilotYou check a pilot, and we fix the rules where boxes disagreed.
- From week 3Box at volumeAgents propose boxes, and annotators correct, add, and remove them.
- Every batchAudit and deliverA reviewer checks a sample against the rules before sign-off.
When 2D bounding boxes are the right annotation And when your model needs masks, polygons, or 3D cuboids
A good fit when
- Your model must find and count objects, not trace their outline
- Objects are roughly box-shaped, such as cars, signs, and products
- You need large volumes fast, at a low price per image
Look elsewhere when
- The model needs exact edges, which calls for segmentation or polygons
- The model needs depth and direction, which calls for 3D cuboids
- Objects are thin or long, such as lanes or cables
Bounding box annotation for computer vision in detail Where boxes are used, and how we keep thousands of them consistent
What 2D bounding box annotation is
A bounding box is the smallest rectangle that holds an object. Detection models learn where things are and what they are from thousands of them.
The task looks simple. Keeping every annotator's boxes equally tight across a hundred thousand images is the hard part.
Bounding boxes for autonomous vehicles and drones
Driving models learn to find traffic, signals, lanes, pedestrians, and potholes from boxed street images. Drone and aerial models learn the same way to spot vehicles, roofs, and crops from above.
Object detection and object localization
Detection says what is in the image, and localization says where it is. We box each object, give it a class, and follow your rules for objects that are partly hidden, cut off by the frame, or too small to matter.
Automatic tagging for retail and ecommerce
Retailers box clothing, shoes, and accessories in product and street photos, then tag each box with attributes such as color and style. Shoppers can then search by photo and find the product.
Bounding box annotation across industries
We box images for robotics, healthcare, retail, agriculture, insurance claims, and security. The images might be aerial photographs, satellite images, drone footage, or photos of damaged vehicles.
Questions about 2D bounding box annotation What teams ask before they outsource bounding boxes
What is 2D bounding box annotation used for?
It trains object detection models, such as driver assistance, drones, retail shelf checks, visual search, and robots that pick items. Any model that must find and count objects can learn from boxes.
How tight should a bounding box be?
Each edge should touch the object's outermost visible pixel. We agree with you how to box objects that are partly hidden or cut off, and a reviewer checks a sample of each batch against that rule.
What output formats do you deliver?
We deliver boxes as COCO JSON, Pascal VOC XML, YOLO TXT, or CSV, or in the format your pipeline reads.
How is bounding box annotation priced?
We price per box or per image. Dense scenes with many small objects cost more than product photos with one item. The price comes from a pilot on your own images.
Send us your classes and 50 images
We'll reply with the box rules we'd propose and the price per image.