Substantially fewer computational resources to train. Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring When pre-trained on large amounts ofĭata and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Sequences of image patches can perform very well on image classification tasks. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to In vision, attention is either applied in conjunction withĬonvolutional networks, or used to replace certain components of convolutional networks while keeping their overall While the Transformer architecture has become the de-facto standard for natural language processing tasks, itsĪpplications to computer vision remain limited. The abstract from the paper is the following: Very good results compared to familiar convolutional architectures.
It’s the first paper that successfully trains a Transformer encoder on ImageNet, attaining Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob The Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image RecognitionĪt Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk
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