CoAtNet

Overview

This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021.

👉 Check out MobileViT if you are interested in other Convolution + Transformer models.

Usage

import torch
from coatnet import coatnet_0

img = torch.randn(1, 3, 224, 224)
net = coatnet_0()
out = net(img)

Try out other block combinations mentioned in the paper:

from coatnet import CoAtNet

num_blocks = [2, 2, 3, 5, 2]            # L
channels = [64, 96, 192, 384, 768]      # D
block_types=['C', 'T', 'T', 'T']        # 'C' for MBConv, 'T' for Transformer

net = CoAtNet((224, 224), 3, num_blocks, channels, block_types=block_types)
out = net(img)

Citation

@article{dai2021coatnet,
  title={CoAtNet: Marrying Convolution and Attention for All Data Sizes},
  author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing},
  journal={arXiv preprint arXiv:2106.04803},
  year={2021}
}

Credits

Code adapted from MobileNetV2 and ViT.