Image & video annotation

Bounding Box Annotation Services for Object Detection

Tight, consistently labeled boxes for images and video, drawn by our in-house team from your class list and edge-case rules. Every batch is reviewed by a second person before it reaches you.

Your own images, labeled free. NDA available on request.

  • NDA on requestData used for your project only
  • Two-stage reviewAnnotator, then reviewer
  • Three shiftsWork moves around the clock
sample_0412.jpg
Harvesting robot gripping a ripe tomato, with a bounding box drawn tightly around the tomato 1 object labeled
Example YOLO label tomato.txt 0 0.500 0.549 0.126 0.344

class · x_center · y_center · width · height

Sample work

Boxes from real detection projects

Drag to compare the raw frame with the delivered labels.

Bounding Box Annotation Service Sample 1 Bounding Box Annotation Service Sample 2 BeforeAfter Drag to compare

Pedestrians and vehicles

An overhead street scene for driver-assistance and traffic models. Every pedestrian and the car get their own box and class, including the people cut off at the edge of the frame.

Classes
pedestriancar
Objects
8 boxes
Used for
Autonomous driving, traffic analytics
What we label

Set up around your classes, rules and format

From a few hundred product photos to millions of video frames.

Image bounding boxes

2D boxes for detection and counting, any number of objects and classes per image.

Video bounding boxes

Frame-by-frame boxes for moving objects in traffic, retail, sports and security footage.

Long class lists

We follow your taxonomy exactly and ask when an object doesn’t fit any class.

AI pre-label correction

Send model-generated boxes and we fix the misses, drift and edge cases.

Quality and data security

The thousandth batch, labeled like the first

Fixed rules for every annotator, a reviewer on every batch, and your data kept under control from first sample to final delivery.

01

Tight to the visible edge

Boxes touch the outermost visible pixels. No padding, nothing cut off.

02

Edge cases agreed in writing

Occlusion, truncation and minimum size are signed off on the pilot, not guessed later.

03

One standard for the whole team

One class list and one set of examples, so image 10 and image 10,000 match.

04

Reviewed before delivery

A reviewer checks every batch. Anything off-spec is fixed at no extra cost.

How we handle your data

  • NDA available on request
  • Access limited to the people on your project
  • No personal devices. Files stay on company systems
  • Deleted after delivery when you ask
Ask for our security details
How a project starts

Pilot first. Commit after.

No contract or payment until you have seen your own data labeled and approved the quote.

  1. STEP 01

    Share samples and rules

    A sample link and your class list, or just what the model needs to detect.

  2. STEP 02

    Free pilot batch

    We label it in your format and note every edge-case decision we made.

  3. STEP 03

    Fixed quote and schedule

    A per-box price and batch plan based on your real data, not a guess.

  4. STEP 04

    Batches on schedule

    Reviewed files with progress updates from one project manager.

Ways to work with us

Built for one-off datasets and ongoing pipelines

Team size is planned around your volume and deadline. Large datasets ship in batches so training can start while labeling continues.

Project

A fixed dataset with a deadline. Scoped, labeled and delivered in batches.

Best for research sets and model launches

Ongoing stream

New data arrives daily or weekly and comes back labeled on a set turnaround.

Best for production retraining
LONG-TERM

Dedicated team

Annotators trained on your guidelines and kept on your work, across three shifts.

Best for long-term partners
Pricing from$0.02per box. Final rate fixed after the pilot, based on objects per image and guideline detail.
COCO .jsonYOLO .txtPascal VOC .xmlCSVYour schema

Used by teams in automotive, retail, agriculture, healthcare, manufacturing, security and robotics.

Choosing a label type

Is a bounding box the right label?

A bounding box is a rectangle drawn around each object and tagged with a class. It teaches a detector what an object is and where it sits, and works with YOLO, SSD and Faster R-CNN.

When the exact outline matters, polygons or pixel masks are the better fit. Not sure? We’ll recommend one on the pilot.

Label typeBest forDetailLink
Bounding box THIS PAGEDetection, counting, trackingRectangle
PolygonIrregular shapes, products, rooftopsFollows the outlineView
Semantic segmentationScene understanding, aerial, medicalEvery pixelView
Pre-label correctionLarge sets with model labels to fixSame as final typeView
FAQ

Before you send data

What AI teams ask us most before a pilot.

We are happy to sign an NDA on request. Access is limited to the people on your project, files stay off personal devices, and we delete your data after delivery when you ask.

Yes. Send your class list and rules, and we confirm how we will handle edge cases on the free pilot before the full project starts.

Every batch is checked by a reviewer before delivery. Anything that breaks your rules goes back for correction and is fixed at no extra cost before it reaches you.

Yes. We work on projects from 1,000 images to several million, and plan delivery in batches so you can start training early.

Prices start at $0.02 per box. The final rate depends on objects per image, the number of classes and how detailed your guidelines are. We fix the price after the free pilot.

Automotive and driver assistance, retail, agriculture, healthcare, manufacturing, security and robotics are the most common. Any project that needs to find and count objects can use it.

Start with a free pilot on your own data

Send a sample link and your class list. You get back:

  • Labeled samples in your format
  • Notes on every edge-case decision
  • A fixed per-box quote and delivery plan

Rather talk first? Email info@graphicsartssolution.com. We usually reply within 24 hours.

No payment details. NDA available on request.