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arXiv · 1907.07061

How much real data do we actually need: Analyzing object detection performance using synthetic and real data

Farzan Erlik Nowruzi·Prince Kapoor·Dhanvin Kolhatkar·Fahed Al Hassanat·Robert Laganiere·Julien Rebut
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Abstract

In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requires a large amount of data to be available. This ideal case is usually not tractable as the data annotation is a tremendously exhausting and costly task to perform. An alternative is to use synthetic data. In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data. We further analyze the effects of having a limited amount of real data. We use multiple synthetic and real datasets along with a simulation tool to create large amounts of cheaply annotated synthetic data. We analyze the domain similarity of each of these datasets. We provide insights about designing a methodological procedure for training deep networks using these datasets.

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Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar, Fahed Al Hassanat, Robert Laganiere, Julien Rebut. 2019-07-16. How much real data do we actually need: Analyzing object detection performance using synthetic and real data. https://arxiv.org/abs/1907.07061

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