In this paper, we propose ProxyDR, a novel metric learning method for hyperspherical embeddings. Through the adoption of a distance ratio-based formulation, ProxyDR resolves the fundamental shortcomings of the conventional squared distance softmax formulation. Notably, our proposed method addresses the near-uniform positioning of class representatives that obstructs effective learning of semantic relationships among classes—a phenomenon demonstrated by our theoretical and experimental analyses. Moreover, by employing proxies as class representatives, our method can be effortlessly incorporated into established classification frameworks. We rigorously evaluate ProxyDR against conventional methods using diverse datasets, including CIFAR100 and NABirds, demonstrating superiority in capturing hierarchical structures while maintaining conventional classification accuracy.