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@@ -371,10 +371,12 @@ All model architecture families include variants with pretrained weights. There
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* MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697
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* MetaFormer (PoolFormer-v2, ConvFormer, CAFormer) - https://arxiv.org/abs/2210.13452
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* MLP-Mixer - https://arxiv.org/abs/2105.01601
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* MobileCLIP - https://arxiv.org/abs/2311.17049
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* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
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* FBNet-V3 - https://arxiv.org/abs/2006.02049
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* HardCoRe-NAS - https://arxiv.org/abs/2102.11646
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* LCNet - https://arxiv.org/abs/2109.15099
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* MobileNetV4 - https://arxiv.org/abs/2404.10518
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* MobileOne - https://arxiv.org/abs/2206.04040
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* MobileViT - https://arxiv.org/abs/2110.02178
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* MobileViT-V2 - https://arxiv.org/abs/2206.02680
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* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
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* Visformer - https://arxiv.org/abs/2104.12533
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* Vision Transformer - https://arxiv.org/abs/2010.11929
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* ViTamin - https://arxiv.org/abs/2404.02132
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* VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112
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* VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
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* Xception - https://arxiv.org/abs/1610.02357

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