ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7\% on the ImageNet-1k val set. Performance gains are maintained when pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream tasks. State-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. ![]() We validate CvT by conducting extensive experiments, showing that this approach achieves Global context, and better generalization). To the ViT architecture (\ie shift, scale, and distortion invariance) while maintaining the merits of Transformers (\ie dynamic attention, These changes introduce desirable properties of convolutional neural networks (CNNs) Two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformerīlock leveraging a convolutional projection. In performance and efficiency by introducing convolutions into ViT to yield the best of both designs. We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) The abstract from the paper is the following: The Convolutional vision Transformer (CvT) improves the Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. The CvT model was proposed in CvT: Introducing Convolutions to Vision Transformers by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang.
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