Image annotation.
Bounding boxes, segmentation, keypoints, and classification — labeled by trained annotators against your guidelines, with calibration and review at every stage.
Frame-level precision, from boxes to pose.
Only capabilities Ayta can currently deliver or is genuinely prepared to pilot are listed below.
Bounding boxes
Objects marked with rectangular boxes for detection and localization tasks.
Polygon segmentation
Irregular object boundaries traced with polygon outlines for precise shape data.
Semantic segmentation
Every pixel in an image classified by category, not just the objects boxed.
Instance segmentation
Individual object instances separated and labeled, even when the same class overlaps itself.
Keypoints
Specific points on an object or body marked for structure and orientation data.
Pose estimation
Body or object position and orientation labeled across a set of reference points.
Image classification
Whole images tagged against your category set for training and filtering pipelines.
Need the source imagery collected first? See Image Data Collection.
From guidelines to a labeled dataset.
Every annotation program follows the same operating sequence.
Guideline intake
Your taxonomy, edge cases, and labeling rules are built into the task design before annotation starts.
Annotator training
Annotators are trained and qualification-tested against your specific guidelines, not a generic label set.
Labeling
Images are labeled to spec — boxes, segmentation, keypoints, or classification, depending on your task.
QA & calibration
Second-pass review, inter-annotator agreement checks, and dispute resolution run on every batch.
Delivery
Labeled data packaged and delivered in the format your training pipeline expects.
Have a data requirement?
Give us the specification — we'll build the operation around it.
Start a Pilot →