How to Generate Synthetic 3D Data with Bifrost
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Company Updates and Technical Articles
Bifrost’s Generative AI approach to building performant Computer Vision Models
Diverse, well-labeled data has become the biggest bottleneck to building computer vision capabilities. It’s why more and more companies are turning to synthetic data and Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models. While synthe...
The differences between human vision and computer vision and why you need domain randomization
Most companies believe they can go outside, snap some pictures and train a robust Computer Vision (CV) model. As the autonomous car companies have shown, that is rarely the case. The reason is that computers learn to identify objects differently than humans.
How Your Data Collection Strategy Influences Your AI's Behavior
In this article, we explain how your choice of data collection method influences AI behavior, and list options for acquiring balanced, diverse data to train bias-free and performant AI systems.
It's 2022 and Data Labeling Still Sucks
You've heard it before. Labeling data for machine learning sucks. Labeling is laborious, time consuming and difficult to scale. Despite all the innovations in AI, data labeling has largely remained the same over the last decade. However, as the race to build AI heats up, compani...