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ANNOTATION

Data Annotation & Labeling

Our data annotation team supports AI, machine learning, computer vision and other data-driven projects with structured and accurate labeling — 3D LiDAR annotation, 2D annotation, image segmentation, polygon annotation, lane marking, polyline annotation, drawing and shape annotation, object detection, image classification, and customized annotation projects.

What’s included

Everything under Data Annotation & Labeling.

3D LiDAR & 2D annotation

Point-cloud and 2D annotation for autonomous, robotics and computer vision datasets.

Segmentation & polygon annotation

Image segmentation and polygon annotation for precise object boundaries.

Lane marking & polyline annotation

Lane marking and polyline annotation built for automotive and mobility datasets.

Object detection & classification

Object detection, image classification and drawing/shape annotation for your model.

Customized annotation projects

Taxonomies and workflows built around your exact labeling requirements, with a focus on accuracy, consistency and timely delivery.

At a glance

  • 3D LiDAR Annotation
  • 2D & Polygon Annotation
  • Image Segmentation
  • Lane Marking & Polyline Annotation
  • Object Detection & Classification
  • Customized Annotation Projects
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dataset_batch_0042.json
vehicle · 0.98
entity · ORG
speaker · A

Sample crops from labeled vision, text and audio data.

FAQ

Common questions about Data Annotation & Labeling.

What is data annotation?

Data annotation is labeling raw data — images, video, text or audio — with the tags a machine learning model needs in order to learn from it.

Which annotation types do you support?

3D LiDAR annotation, 2D annotation, image segmentation, polygon annotation, lane marking, polyline annotation, drawing and shape annotation, object detection, image classification and customized annotation projects.

How do you handle quality control?

We focus on annotation accuracy, consistency, quality control and timely project delivery on every batch.

Related

Pairs well with

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