  Transformers have attracted increasing interests in computer vision, but they
still fall behind state-of-the-art convolutional networks. In this work, we
show that while Transformers tend to have larger model capacity, their
generalization can be worse than convolutional networks due to the lack of the
right inductive bias. To effectively combine the strengths from both
architectures, we present CoAtNets(pronounced "coat" nets), a family of hybrid
models built from two key insights: (1) depthwise Convolution and
self-Attention can be naturally unified via simple relative attention; (2)
vertically stacking convolution layers and attention layers in a principled way
is surprisingly effective in improving generalization, capacity and efficiency.
Experiments show that our CoAtNets achieve state-of-the-art performance under
different resource constraints across various datasets: Without extra data,
CoAtNet achieves 86.0% ImageNet top-1 accuracy; When pre-trained with 13M
images from ImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching
ViT-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-1
accuracy on ImageNet, establishing a new state-of-the-art result.
