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This is an unofficial PyTorch implementation of EdgeViT in "EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers", arXiv 2022.

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This is an unofficial PyTorch implementation of EdgeViT in "EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers", arXiv 2022.

Pretrained models will come soon.

Usage

from edgevit import EdgeViT_XXS, EdgeViT_XS, EdgeViT_S

model = EdgeViT_XXS()
inputs = torch.randn((1, 3, 224, 224))
print(model(inputs))

Citation

@article{pan2022edgevits,
  title={EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers},
  author={Pan, Junting and Bulat, Adrian and Tan, Fuwen and Zhu, Xiatian and Dudziak, Lukasz and Li, Hongsheng and Tzimiropoulos, Georgios and Martinez, Brais},
  journal={arXiv preprint arXiv:2205.03436},
  year={2022}
}

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This is an unofficial PyTorch implementation of EdgeViT in "EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers", arXiv 2022.

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