Image & video annotation

Polygon Annotation Services for Precise Object Shapes

Point-by-point outlines that follow curves, gaps and odd angles, so your model learns the real shape instead of a rectangle. Traced by our in-house team from your class list and rules, and reviewed by a second person before delivery.

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

  • NDA on requestData used for your project only
  • Two-stage reviewAnnotator, then reviewer
  • Three shiftsWork moves around the clock
polygon · class: deer
Polygon annotation of a red deer stag: one closed outline follows the body, legs and every antler tine Polygon · antlers traced tine by tine
Example COCO label deer.json "segmentation": [[748,325, 707,110, 591,105, 481,292, 305,352, 262,593, …]]

one x,y pair per vertex · closed shape · no boxes

Sample work

Outlines from real segmentation projects

Drag to compare the raw frame with the delivered outlines.

Polygon Annotation Service Sample 1 Polygon Annotation Service Sample 2 BeforeAfter Drag to compare

Every rooftop, traced from above

A drone shot of a new housing estate. Each roof gets its own polygon that follows the L-shapes, extensions and porches, so the model learns real building footprints instead of rectangles that spill into gardens and driveways.

Classes
building
Objects
14 polygons, one per roof
Used for
Mapping, urban planning, insurance and solar estimates
What we trace

Outline detail agreed before the first vertex

Simple product shapes or crowded street scenes, with the level of detail fixed before we start.

Image polygon annotation

Closed outlines on single images, from a few large objects to hundreds of small ones per frame.

Video polygon annotation

Outlines tracked across frames with a stable ID per object, for tracking and video segmentation.

Instance segmentation

Every object gets its own polygon and ID, even when two of the same class touch or overlap.

AI pre-label correction

Send model-generated masks or polygons and we fix drifting edges, merged objects and misses.

Quality and data security

Every outline on the true edge

A loose polygon teaches a model the wrong shape, and the error repeats in every prediction. Fixed rules for every annotator, a reviewer on every batch, and your data kept under control throughout.

01

Points on the true edge

Vertices sit on the object boundary, not inside it and not out in the background.

02

Detail where the shape bends

More points on curves and corners, fewer on straight edges, so files stay light without losing shape.

03

Hidden parts agreed in writing

Occluded objects are traced on the visible part only, or as a full shape, as signed off on the pilot.

04

Checked at full zoom

A reviewer inspects outlines up close and returns any that drift from the edge or overlap a neighbour.

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 images traced and approved the quote.

  1. STEP 01

    Share samples and rules

    A sample link, your class list and any outline rules, or just what the model needs to segment.

  2. STEP 02

    Free pilot batch

    We trace it in your format and note every vertex-spacing and occlusion decision.

  3. STEP 03

    Fixed quote and schedule

    A per-object price and batch plan based on your real images, 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 tracing continues.

Project

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

Best for research sets and model launches

Ongoing stream

New data arrives daily or weekly and comes back traced 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
Pricedper objectThe rate depends on shape complexity, objects per image and guideline detail, and is fixed after the pilot.
COCO .jsonYOLO-seg .txtPascal VOC .xmlLabelMe / CVATYour schema

Used by teams in automotive, retail, agriculture, healthcare, aerial and satellite imaging, manufacturing and robotics.

Choosing a label type

Is a polygon the right label?

A polygon is a closed outline of points placed along an object’s edge and tagged with a class. It is the standard input for instance segmentation models such as Mask R-CNN and YOLO-seg, at a lower cost than full pixel masks.

If you only need to find and count objects, boxes are faster. If every pixel needs a class, choose segmentation. Not sure? We’ll recommend one on the pilot.

Label typeBest forDetailLink
Bounding boxDetection, counting, trackingRectangleView
Polygon THIS PAGEIrregular shapes, products, rooftopsFollows the outline
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.

Use polygons when the object is irregular, rotated or thin, or when the background inside a box would confuse the model. People, vehicles at an angle, plants, rooftops and products with odd shapes are common cases. If you only need to find and count objects, boxes are faster and cheaper.

We agree that on the free pilot. Most projects place more points on curves and fewer on straight edges, with a maximum allowed gap between the outline and the true edge. Your rules then apply to every image.

A polygon outlines each object separately, so two touching cars are two shapes with their own IDs. Semantic segmentation colors every pixel by class, so the same two cars become one car region. Many teams use polygons to build instance masks without paying for pixel-level work.

Yes. We trace objects across frames and keep the same ID for each object through the clip, so the data works for tracking and video segmentation models.

Polygons are priced per object. The rate depends on how complex the shapes are, how many objects each image holds and how detailed your guidelines are. We send a firm quote after the free pilot, before any work is billed.

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-object quote and delivery plan

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

No payment details. NDA available on request.