  While the Transformer architecture has become the de-facto standard for
natural language processing tasks, its applications to computer vision remain
limited. In vision, attention is either applied in conjunction with
convolutional networks, or used to replace certain components of convolutional
networks while keeping their overall structure in place. We show that this
reliance on CNNs is not necessary and a pure transformer applied directly to
sequences of image patches can perform very well on image classification tasks.
When pre-trained on large amounts of data and transferred to multiple mid-sized
or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision
Transformer (ViT) attains excellent results compared to state-of-the-art
convolutional networks while requiring substantially fewer computational
resources to train.
