  A number of studies have investigated the training of neural networks with
synthetic data for applications in the real world. The aim of this study is to
quantify how much real world data can be saved when using a mixed dataset of
synthetic and real world data. By modeling the relationship between the number
of training examples and detection performance by a simple power law, we find
that the need for real world data can be reduced by up to 70% without
sacrificing detection performance. The training of object detection networks is
especially enhanced by enriching the mixed dataset with classes
underrepresented in the real world dataset. The results indicate that mixed
datasets with real world data ratios between 5% and 20% reduce the need for
real world data the most without reducing the detection performance.
