Skip manual labeling.
Produce perfectly annotated datasets in the browser, within hours, and at a fraction of the cost of annotation services or in-house teams.
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.
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.
Whether it is tough lighting conditions, adversarial materials, odd camera angles, or rare SKUs, you can augment your training dataset with difficult training examples and build robust models.
With synthetic data, you have full control of your training dataset.
YCB-Video is a large scale dataset developed by researchers at the University of Washington, based on the original YCB work.
We are hosting a free copy of this dataset on S3 to share it with the community and help address known distribution issues.
Vision powered robots will play a massive role in automation in the next decade.
SBX is building towards an end-to-end simulation solution so that these robots can be designed, tested and trained all within our offline simulator.
Led applied research projects at UberATG, Kindred AI, and SigOpt focusing on computer vision, robotics, and optimization for machine learning.
Built the tech and team behind Wish.com merchant marketplace, growing it to 100k+ active merchants and 45 engineers, PMs, and designers.
Yelp’s anti-fraud expert. Ran a team of 25 engineers & analysts to build machine learning pipelines detecting fraud, spam, and abuse.