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In recent years, deep learning models have resulted in a huge amount ofprogress in various areas, including computer vision. By nature, the supervisedtraining of deep models requires a large amount of data to be available. Thisideal case is usually not tractable as the data annotation is a tremendouslyexhausting 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 realdata with synthetic data. We further analyze the effects of having a limitedamount of real data. We use multiple synthetic and real datasets along with asimulation tool to create large amounts of cheaply annotated synthetic data. Weanalyze the domain similarity of each of these datasets. We provide insightsabout designing a methodological procedure for training deep networks usingthese datasets.

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