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.

现代计算机视觉基础模型是在大量数据的基础上进行训练的，这带来了巨大的经济和环境成本。最近的研究表明，提高数据质量可以显著减少对数据数量的需求。但是，在计算机视觉中，什么构成数据质量？我们认为，给定数据集的质量可以分解为不同的样本级和数据集级成分，而前者比后者得到了更深入的研究。我们通过监控这些成分，利用我们提供的关键指标，研究人员和实践者可以更好地预测模型的性能，以其准确性和对分布变化的鲁棒性来衡量。