Update README.md

README.md

@@ -12,96 +12,187 @@ library_name: timm
 
 ### Model Description
 Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
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 BEiT - https://arxiv.org/abs/2106.08254
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 Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
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 Bottleneck Transformers - https://arxiv.org/abs/2101.11605
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 CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
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 CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
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 CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803
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 ConvNeXt - https://arxiv.org/abs/2201.03545
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 ConvNeXt-V2 - http://arxiv.org/abs/2301.00808
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 ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
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 CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
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 DeiT - https://arxiv.org/abs/2012.12877
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 DeiT-III - https://arxiv.org/pdf/2204.07118.pdf
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 DenseNet - https://arxiv.org/abs/1608.06993
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 DLA - https://arxiv.org/abs/1707.06484
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 DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
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 EdgeNeXt - https://arxiv.org/abs/2206.10589
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 EfficientFormer - https://arxiv.org/abs/2206.01191
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 EfficientNet (MBConvNet Family)
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 EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
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 EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
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 EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
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 EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
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 EfficientNet V2 - https://arxiv.org/abs/2104.00298
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 FBNet-C - https://arxiv.org/abs/1812.03443
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 MixNet - https://arxiv.org/abs/1907.09595
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 MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
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 MobileNet-V2 - https://arxiv.org/abs/1801.04381
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 Single-Path NAS - https://arxiv.org/abs/1904.02877
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 TinyNet - https://arxiv.org/abs/2010.14819
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 EVA - https://arxiv.org/abs/2211.07636
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 FlexiViT - https://arxiv.org/abs/2212.08013
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 GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
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 GhostNet - https://arxiv.org/abs/1911.11907
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 gMLP - https://arxiv.org/abs/2105.08050
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 GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
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 Halo Nets - https://arxiv.org/abs/2103.12731
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 HRNet - https://arxiv.org/abs/1908.07919
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 Inception-V3 - https://arxiv.org/abs/1512.00567
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 Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
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 Lambda Networks - https://arxiv.org/abs/2102.08602
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 LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
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 MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697
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 MLP-Mixer - https://arxiv.org/abs/2105.01601
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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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 MobileViT - https://arxiv.org/abs/2110.02178
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 MobileViT-V2 - https://arxiv.org/abs/2206.02680
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 MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526
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 NASNet-A - https://arxiv.org/abs/1707.07012
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 NesT - https://arxiv.org/abs/2105.12723
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 NFNet-F - https://arxiv.org/abs/2102.06171
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 NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
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 PNasNet - https://arxiv.org/abs/1712.00559
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 PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418
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 Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302
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 PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797
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 RegNet - https://arxiv.org/abs/2003.13678
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 RegNetZ - https://arxiv.org/abs/2103.06877
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 RepVGG - https://arxiv.org/abs/2101.03697
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 ResMLP - https://arxiv.org/abs/2105.03404
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 ResNet/ResNeXt
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 ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
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 ResNeXt - https://arxiv.org/abs/1611.05431
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 'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187
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 Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932
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 Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546
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 ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4
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 Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507
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 ResNet-RS - https://arxiv.org/abs/2103.07579
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 Res2Net - https://arxiv.org/abs/1904.01169
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 ResNeSt - https://arxiv.org/abs/2004.08955
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 ReXNet - https://arxiv.org/abs/2007.00992
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 SelecSLS - https://arxiv.org/abs/1907.00837
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 Selective Kernel Networks - https://arxiv.org/abs/1903.06586
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 Sequencer2D - https://arxiv.org/abs/2205.01972
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 Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725
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 Swin Transformer - https://arxiv.org/abs/2103.14030
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 Swin Transformer V2 - https://arxiv.org/abs/2111.09883
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 Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
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 TResNet - https://arxiv.org/abs/2003.13630
-Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
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+Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/abs/2104.13840
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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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 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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 Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611
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 Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611
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 XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681
 
 ### Installation