We’ve labeled over 10 million images for the most ambitious computer vision projects in the world. We’d love to help you make your next project a success.
Our robust suite of computer vision solutions can handle everything from simple image classification to pixel labeling semantic segmentation. We combine human intelligence at scale with cutting edge machine learning to create the best training data in the industry.
Figure Eight can categorize images and photos at enterprise-scale. You choose the ontology and our platform will make sure everything gets labeled quickly and accurately. You can classify images by quality (detecting blurry images, for example), type (like product vs. lifestyle images), content (what’s actually in the image itself), or any other judgments you need to be made on your library of images.
Computer vision projects often need in-image labels. Our object detection solution has tooling for bounding boxes, polygons, and line labels, all with aggregation and quality controls to make sure you get the most exacting, accurate labels possible.
For images where you need to identify multiple classes and multiple instances of certain objects, our object tagging solution is a great fit. Here, our annotators will select a class from an ontology you create and label each instance according to your instructions. With fully customizable ontologies that support hundreds of classes, you can get your images labeled to your exact specifications.
Our semantic segmentation solution provides pixel-level labeling for computer vision projects that need exacting annotations. Our solution has a robust suite of tooling like that makes labeling easy for annotators, tools like our magic wand that labels groups of similar pixels and a whole lot more. We partner with expert contributor channels that produce the highest quality semantic segmentation labels for your computer vision models.
Our video object tracking tool leverages an ensemble machine learning model to label videos up to 100 times faster than human-only approaches. Human annotators label objects in the first frame and the model persists those annotations, following objects as they move through the video, and relying on annotators to simply tweak and amend labels instead of relabeling each object like other solutions in the marketplace. Want to learn a little more? Click here.
Figure Eight’s landmark tagging tool leverages dots to label and identify landmarks on objects. Typically used in facial recognition and robotics use cases, our dot tool gives you the ability to train models on key points in imagery. We also provide aggregation and powerful quality controls to ensure your landmark detection jobs return the best training data possible.
Similar to our object tagging solution, Figure Eight’s landmark tagging solution powers landmark tagging based on customizable ontologies. Typically used in facial recognition and robotics use cases, our dot tool gives you the ability to train models on key points in imagery in multiple classes from your own ontology.