Computer Vision and Image Annotation

We provide the training data that teaches machines how to see the world

Machines need training data to learn how to see the world. But raw images aren’t enough. After all, people don’t have a problem identifying a can of soda or a mother pushing a stroller in a .jpg, but to an untrained machine, those are just collections of different pixels. Annotated images teach AI how to see the world.

We understand that different machine learning experts use different approaches for different initiatives and we support every major enrichment strategy. Our platform powers:

Image Annotation - Bounding Boxes for Computer Vision Models

Bounding Boxes

Bounding boxes are frequently used to identify objects in images from your pre-existing ontologies. Tell us what’s important and we’ll create the training data to power your models.

Image Annotation - Dots for Computer Vision Models


Dots help identify key points in images. They’re often used for training gesture or facial recognition models.

Image Annotation - Pixel Labeling Semantic Segmentation for Computer Vision Models

Pixel Labeling Semantic Segmentation (PLSS)

Pixel-level labeling helps a machine understand every part of an image. We understand what you need to train your models and our tool helps human annotators label every inch of your photo quickly and accurately.

Image Annotation - Video Annotation for Computer Vision Models

Machine Learning Assisted Video Object Tracking

Our video object tracking solution leverages both human and machine intelligence to annotate video up to 100 times faster than human-only approaches.
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Whether you’re looking to annotate the topology of the world from an orbiting satellite or label the cells from a microscopy image, we’ve got you covered. Our platform combines human and machine intelligence to power high-quality annotation for autonomous vehicles, medical imagery, consumer packaged goods, and more.

Figure Eight Machine Learning assisted Video Object Tracking

Learn how we combined the best of machine and human intelligence to create video training data faster and more accurately.

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Related Resources


Introducing Machine Learning Assisted Video Object Tracking

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eBook: What We Learned Labeling 1 Million Images

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Introducing Instance-Based Pixel Labeling

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