Creating big datasets is often difficult or expensive which causes people to augment their dataset with rendered images. This often fails to significantly improve accuracy due to a difference in distribution between real and rendered datasets. This paper shows that the gap between synthetic and real-world image distributions can be closed by using GANs to convert the synthetic data to a dataset which has the same distribution as the real data. Training this GAN requires only a fraction of the dataset traditionally required to get a high classification accuracy. This converted data can subsequently be used to train a classifier with a higher accuracy than a classifier trained only on the real dataset.