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Standard training datasets for deep learning often contain objects in commonsettings (e.g., "a horse on grass" or "a ship in water") since they are usuallycollected by randomly scraping the web. Uncommon and rare settings (e.g., "aplane on water", "a car in snowy weather") are thus severely under-representedin the training data. This can lead to an undesirable bias in model predictionstowards common settings and create a false sense of accuracy. In this paper, weintroduce FOCUS (Familiar Objects in Common and Uncommon Settings), a datasetfor stress-testing the generalization power of deep image classifiers. Byleveraging the power of modern search engines, we deliberately gather datacontaining objects in common and uncommon settings in a wide range oflocations, weather conditions, and time of day. We present a detailed analysisof the performance of various popular image classifiers on our dataset anddemonstrate a clear drop in performance when classifying images in uncommonsettings. By analyzing deep features of these models, we show that such errorscan be due to the use of spurious features in model predictions. We believethat our dataset will aid researchers in understanding the inability of deepmodels to generalize well to uncommon settings and drive future work onimproving their distributional robustness.

FOCUS: Familiar Objects in Common and Uncommon Settings