  Rotary Position Embedding (RoPE) performs remarkably on language models,
especially for length extrapolation of Transformers. However, the impacts of
RoPE on computer vision domains have been underexplored, even though RoPE
appears capable of enhancing Vision Transformer (ViT) performance in a way
similar to the language domain. This study provides a comprehensive analysis of
RoPE when applied to ViTs, utilizing practical implementations of RoPE for 2D
vision data. The analysis reveals that RoPE demonstrates impressive
extrapolation performance, i.e., maintaining precision while increasing image
resolution at inference. It eventually leads to performance improvement for
ImageNet-1k, COCO detection, and ADE-20k segmentation. We believe this study
provides thorough guidelines to apply RoPE into ViT, promising improved
backbone performance with minimal extra computational overhead. Our code and
pre-trained models are available at https://github.com/naver-ai/rope-vit
