Despite the increasing visibility of fine-grained recognition in our field, "fine-grained'' has thus far lacked a precise definition. In this work, building upon clustering theory, we pursue a framework for measuring dataset granularity. We argue that dataset granularity should depend not only on the data samples and their labels, but also on the distance function we choose. We propose an axiomatic framework to capture desired properties for a dataset granularity measure and provide examples of measures that satisfy these properties. We assess each measure via experiments on datasets with hierarchical labels of varying granularity. When measuring granularity in commonly used datasets with our measure, we find that certain datasets that are widely considered fine-grained in fact contain subsets of considerable size that are substantially more coarse-grained than datasets generally regarded as coarse-grained. We also investigate the interplay between dataset granularity with a variety of factors and find that fine-grained datasets are more difficult to learn from, more difficult to transfer to, more difficult to perform few-shot learning with, and more vulnerable to adversarial attacks.

在该研究中，我们建立在聚类理论的基础上，提出了一种度量数据集粒度的框架，认为数据集的粒度不仅取决于数据样本和标签，还取决于我们选择的距离函数，我们评估了每种度量标准，并在具有分层标签的数据集上进行了实验。我们发现，细粒度数据集更难学习，更难迁移，更难进行小样本学习，更容易受到对抗攻击。

测量数据集粒度