  Modern computer vision foundation models are trained on massive amounts of
data, incurring large economic and environmental costs. Recent research has
suggested that improving data quality can significantly reduce the need for
data quantity. But what constitutes data quality in computer vision? We posit
that the quality of a given dataset can be decomposed into distinct
sample-level and dataset-level constituents, and that the former have been more
extensively studied than the latter. We ablate the effects of two important
dataset-level constituents: label set design, and class balance. By monitoring
these constituents using key indicators we provide, researchers and
practitioners can better anticipate model performance, measured in terms of its
accuracy and robustness to distribution shifts.
