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 autonomous vehicles and 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.

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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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.

Trusted by today’s leading brands

Walking through the field, taking tallies on how many weeds we hit, how many we missed, and our accuracy was in the high 90s on that test. We’re really excited about bringing this technology to the farmer and using AI to really modernize farming.

Chris Padwick

Machine Learning Team Lead

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Learn how Home Depot uses machine learning and high quality data to make every customer experience, no matter how they’re shopping, a whole lot better.
When we changed to Figure Eight, within a few weeks, we saw that labeler accuracy go up to 88% and it stayed in the high 80s and 90s for us ever since, even across a large diversity of models. That’s been a really, really big win.

Etienne Manderscheid

VP Engineering, AI - Machine Learning

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Resources

FeaturedBlog

Introducing Machine Learning Assisted Video Object Tracking

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Blog

Introducing Instance-Based Pixel Labeling

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eBooks

eBook: What We Learned Labeling 1 Million Images

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See what Figure Eight can do for you