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:
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.
Dots help identify key points in images. They’re often used for training gesture or facial recognition models.
Pixel-level labeling help 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.
A row of cars or a cluster of cells often need to be labeled individually so a model can understand each instance of an object. We can help you create a robust labeling schema for your project so you can get the discrete annotations you need.
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.