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.

Object detectors, much like humans, perform less well on small than on large objects. Because of this, the object size distribution of a dataset influences the average precision a network achieves on that dataset. Therefore, the object size/precision curve of a network might be a better way to compare convolutional object detectors than the average precision over an entire dataset. In this paper we measure the relationship between object size and accuracy
for a modern mobile convolutional object detector. We verify that this relationship holds for a different dataset, and that the dataset object size distribution influences the average precision over the entire dataset. We conclude that the object size/accuracy curve might contain more information about a network’s performance than the average precision over an entire dataset.