Swin Transformers were developed as a general-purpose backbone network for computer vision to address the quadratic computational complexity of standard self-attention with respect to image size. By reinstating convolution-like priors, Swin Transformers extend the applicability of the Transformer architecture beyond basic image classification, achieving state-of-the-art results across a wide range of computer vision tasks.