Autonomous Vehicles

We teach cars how to see the world

Training data for autonomous vehicles

At Figure Eight, we work with Tier 1 and Tier 2 suppliers, OEMs, and more to create high-quality training data for self-driving car projects. We specialize in image annotation, doing everything from simple image classification to bounding boxes to semantic segmentation. So however you’re approaching the problem, we can help.

Our platform powers:

Bounding Boxes

Bounding boxes are often used to identify the most important classes in a computer vision model and our platform quality controls and baked-in machine learning make sure those boxes are accurate.

Lines & polygons

Lines and polygons are typically used to mark lane lines and other important road markers for self-driving algorithms. Our line tool can also be used to outline oddly shaped or occluded objects.

Pixel labeling semantic segmentation (PLSS)

More and more, autonomous vehicle algorithms are relying on exacting pixel-level labeling. We support dozens of classes per image and have the capacity to label massive datasets quickly–and most importantly–accurately.

Instance-based PLSS

Overlapping objects or myriad instances of one class in a single image can sometimes be confused for your models. That’s why we pioneered instance-based pixel labeling. Ask us if it can work for your use case.

Trusted by today’s leading brands

We needed a way to scalably measure search results quality. We needed to look at the impact of algorithms in search results relevancy and Figure Eight provides a way to do that. Our search results have gotten measurably better and we’ve been able to increase revenue, so it's been a real winner in terms of user experience and income.

James Rubenstein

Director of U.S. Search

Having tens of thousands of people at your disposal to read content and score it for sentiment and tone gives us the ability to inform our clients if there’s an issue. We can identify it quickly and put the campfire out before it becomes a forest fire.

Chris Lightner

Executive Vice President, Measurement and Insights

Machines don’t know music. Machines don’t feel the beat. Machines don’t feel happy. So we have to teach them. Teaching them involves two things: what do you put in, but also how do you grade your machines?

Henriette Cramer

Senior Research Lead



Introducing Instance-Based Pixel Labeling

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What We Learned Labeling 1 Million Images eBook

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