Training current AI models requires a lot of data. Our technology drastically reduces the amount and necessary quality for training current state-of-the-art networks for computer vision tasks, such as classification, detection, segmentation, and object pose estimation. We use a combination of rendering, simulation, modeling, self- and unsupervised learning, as well as statistical approaches to achieve competitive results with very little data. In addition we can drastically reduce the amount of manual labeling – in most cases needing no manual labeling at all.
Our technology reduces data requirements for AI model training in computer vision tasks by using rendering, simulation, modeling, self- and unsupervised learning, and statistical approaches, achieving competitive results with minimal data and reducing the need for manual labeling.
