  Scalable training data generation is a critical problem in deep learning. We
propose PennSyn2Real - a photo-realistic synthetic dataset consisting of more
than 100,000 4K images of more than 20 types of micro aerial vehicles (MAVs).
The dataset can be used to generate arbitrary numbers of training images for
high-level computer vision tasks such as MAV detection and classification. Our
data generation framework bootstraps chroma-keying, a mature cinematography
technique with a motion tracking system, providing artifact-free and curated
annotated images where object orientations and lighting are controlled. This
framework is easy to set up and can be applied to a broad range of objects,
reducing the gap between synthetic and real-world data. We show that synthetic
data generated using this framework can be directly used to train CNN models
for common object recognition tasks such as detection and segmentation. We
demonstrate competitive performance in comparison with training using only real
images. Furthermore, bootstrapping the generated synthetic data in few-shot
learning can significantly improve the overall performance, reducing the number
of required training data samples to achieve the desired accuracy.
