Get 1000x more training data for computer vision.

Skip manual labeling & iterate quickly.
Multiply 25 real-world images into 25,000 synthetic training images for deep learning.

"Our model trained on SBX data significantly outperformed one trained on data we collected."
- Tarik Kelestemur, Researcher
"The ability of models trained on SBX data to generalize on fairly diverse items is impressive."
- Marek Cygan, CTO
"SBX kickstarts projects far faster than real-world data acquisition and labeling."
- James Servos, Perception Team Manager
"SBX showed us synth data is viable for our AI needs. We look forward to working together."
- Daniel Grollman, Lead R&D Engineer, PhD

Our synthetic training data is a drop-in replacement for labeled data used to train computer vision systems.

We use video game engines to produce perfectly annotated training datasets for object detection, segmentation, and 6D pose estimation models in common formats like MS COCO. We simulate RGB cameras and RGB+Depth sensors.

What is synthetic training data?

RGB
Segmentation
Depth
Manual annotation is slow -- 10+ seconds per label

Synthetic data is faster and cheaper.

Setting up hardware and sensors to collect data, training a team of labelers, and pruning labeling errors adds months of R&D and weighs heavily on project budgets.

Once configured, our generator can produce 100,000+ perfectly labeled training images within hours, and scales in the cloud.

Don't worry about sim2real transfer -- we'll benchmark it!

SBX data is engineered to perform.

Instead of generating one dataset that “looks right”, we generate many competing versions and benchmark them against client data to learn what makes the best dataset for the problem.

Each SBX dataset is the product of iterative testing and optimization to achieve the best performance on real-world data.

Applications

E-grocery Fulfillment

Warehouse

Logistics

Agriculture Tech

Founding team

Ian Dewancker, CEO

Led applied research projects at UberATG, Kindred AI, and SigOpt focusing on computer vision, robotics, and optimization for machine learning.

Joshua Kuntz, Engineering

Built the tech and team behind Wish.com merchant marketplace, growing it to 100k+ active merchants and 45 engineers, PMs, and designers.

Artem Avdacev, Product

Yelp’s anti-fraud expert. Ran a team of 25 engineers & analysts to build machine learning pipelines detecting fraud, spam, and abuse.

1000x my data, please!

Share 25 images from your vision system, and we'll generate an optimized training set of 25,000 annotated synthetic images.
Max file size 10MB.
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