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Update README: add references for additional supported models #2526

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10 changes: 10 additions & 0 deletions README.md
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Expand Up @@ -408,6 +408,8 @@ All model architecture families include variants with pretrained weights. There

* Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
* BEiT - https://arxiv.org/abs/2106.08254
* BEiT-V2 - https://arxiv.org/abs/2208.06366
* BEiT3 - https://arxiv.org/abs/2208.10442
* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
* Bottleneck Transformers - https://arxiv.org/abs/2101.11605
* CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
Expand All @@ -424,6 +426,7 @@ All model architecture families include variants with pretrained weights. There
* DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
* EdgeNeXt - https://arxiv.org/abs/2206.10589
* EfficientFormer - https://arxiv.org/abs/2206.01191
* EfficientFormer-V2 - https://arxiv.org/abs/2212.08059
* EfficientNet (MBConvNet Family)
* EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
* EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
Expand All @@ -440,12 +443,14 @@ All model architecture families include variants with pretrained weights. There
* EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027
* EVA - https://arxiv.org/abs/2211.07636
* EVA-02 - https://arxiv.org/abs/2303.11331
* FasterNet - https://arxiv.org/abs/2303.03667
* FastViT - https://arxiv.org/abs/2303.14189
* FlexiViT - https://arxiv.org/abs/2212.08013
* FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926
* GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
* GhostNet - https://arxiv.org/abs/1911.11907
* GhostNet-V2 - https://arxiv.org/abs/2211.12905
* GhostNet-V3 - https://arxiv.org/abs/2404.11202
* gMLP - https://arxiv.org/abs/2105.08050
* GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
* Halo Nets - https://arxiv.org/abs/2103.12731
Expand Down Expand Up @@ -501,14 +506,19 @@ All model architecture families include variants with pretrained weights. There
* SelecSLS - https://arxiv.org/abs/1907.00837
* Selective Kernel Networks - https://arxiv.org/abs/1903.06586
* Sequencer2D - https://arxiv.org/abs/2205.01972
* SHViT - https://arxiv.org/abs/2401.16456
* SigLIP (image encoder) - https://arxiv.org/abs/2303.15343
* SigLIP 2 (image encoder) - https://arxiv.org/abs/2502.14786
* StarNet - https://arxiv.org/abs/2403.19967
* SwiftFormer - https://arxiv.org/pdf/2303.15446
* Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725
* Swin Transformer - https://arxiv.org/abs/2103.14030
* Swin Transformer V2 - https://arxiv.org/abs/2111.09883
* TinyViT - https://arxiv.org/abs/2207.10666
* Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
* TResNet - https://arxiv.org/abs/2003.13630
* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
* VGG - https://arxiv.org/abs/1409.1556
* Visformer - https://arxiv.org/abs/2104.12533
* Vision Transformer - https://arxiv.org/abs/2010.11929
* ViTamin - https://arxiv.org/abs/2404.02132
Expand Down