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Transformers have attracted increasing interests in computer vision, but theystill fall behind state-of-the-art convolutional networks. In this work, weshow that while Transformers tend to have larger model capacity, theirgeneralization can be worse than convolutional networks due to the lack of theright inductive bias. To effectively combine the strengths from botharchitectures, we present CoAtNets(pronounced "coat" nets), a family of hybridmodels built from two key insights: (1) depthwise Convolution andself-Attention can be naturally unified via simple relative attention; (2)vertically stacking convolution layers and attention layers in a principled wayis surprisingly effective in improving generalization, capacity and efficiency.Experiments show that our CoAtNets achieve state-of-the-art performance underdifferent resource constraints across various datasets: Without extra data,CoAtNet achieves 86.0% ImageNet top-1 accuracy; When pre-trained with 13Mimages from ImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matchingViT-huge pre-trained with 300M images from JFT-300M while using 23x less data;Notably, when we further scale up CoAtNet with JFT-3B, it achieves 90.88% top-1accuracy on ImageNet, establishing a new state-of-the-art result.

CoAtNet: Marrying Convolution and Attention for All Data Sizes