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Image Annotation Services

Pixel-precise labeling for computer vision. From bounding boxes to semantic segmentation, our managed teams deliver the labeled image data your models need to perform in production.

Image annotation for computer vision

Precision Labeling for Every Vision Task

Our image annotation services cover the full spectrum of computer vision requirements. Whether you're building object detection models that need tight bounding boxes, autonomous driving systems requiring pixel-level segmentation, or medical imaging tools demanding sub-pixel keypoint accuracy, our trained annotators deliver consistent, high-quality labels at scale. We use AI-assisted pre-labeling to accelerate throughput while maintaining human-verified precision on every annotation.

  • Bounding boxes, oriented boxes, and 2D cuboids
  • Polygon and polyline annotation for irregular shapes
  • Semantic and instance segmentation (pixel-level masks)
  • Keypoint and landmark detection for pose estimation
  • OCR and text-in-image extraction
Capabilities

Image Annotation Methods

Specialized techniques for every computer vision challenge, from simple classification to complex multi-layer segmentation.

Bounding Box Annotation

Tight-fitting rectangular boxes around objects for detection models. We support axis-aligned and rotated boxes, with attribute tagging for classification, occlusion levels, and truncation metadata.

Polygon & Polyline

Precise contour tracing for irregularly shaped objects like vehicles, buildings, and organic forms. Our annotators achieve sub-pixel boundary precision critical for segmentation model training.

Semantic Segmentation

Every pixel classified into predefined categories. Ideal for autonomous driving scene understanding, medical imaging tissue classification, and satellite imagery land-use mapping.

Keypoint Detection

Anatomical landmarks, facial features, and skeletal joint positions annotated with sub-pixel accuracy. Used for human pose estimation, facial recognition, and gesture analysis models.

Instance Segmentation

Individual object masks that distinguish between overlapping instances of the same class. Critical for robotics pick-and-place, crowd counting, and multi-object tracking applications.

OCR & Text Extraction

Text region detection, character-level bounding boxes, and transcription for documents, street signs, license plates, and handwritten content across multiple scripts and languages.

FAQ

Frequently Asked Questions

We export in all standard formats including COCO JSON, Pascal VOC XML, YOLO TXT, VGG VIA, Cityscapes, and custom schemas. Segmentation masks can be delivered as PNG files, run-length encoded (RLE), or polygon coordinates depending on your pipeline requirements.
Our annotators follow z-order layering protocols to handle occlusion. Each object gets a separate mask or bounding box even when overlapping. We tag occlusion percentages and truncation status as metadata, giving your model full context about partially visible objects.
Throughput varies by annotation complexity. Simple bounding box tasks can exceed 2,000 images per annotator per day, while detailed semantic segmentation may average 50–100 images. We scale teams elastically and use AI pre-labeling to accelerate all annotation types by 40–60%.
Yes. We have dedicated teams trained in medical imaging (radiology, pathology, ophthalmology) and geospatial annotation. These teams operate under enhanced security protocols and receive domain-specific training from subject matter experts before starting any project.
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Start Labeling With Pixel-Level Precision

Send us a sample dataset and we'll return annotated results within 48 hours — free of charge. See the quality difference managed teams make.

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