When training any AI model, carefully labeled and diverse datasets are essential. These datasets typically contain thousands to tens of millions of elements, some of which may fall beyond the visible spectrum. In the real world, collecting and labeling such data is not only time-consuming but also costly. This can hinder the development of AI models and prolong the time required to find solutions.
Synthetic data is generated through computer simulation technology, including 2D images or text. It can be used in conjunction with real data to train AI models for computer vision workflows. Leveraging Synthetic Data Generation (SDG) technology can significantly reduce both the time and costs associated with training.
