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@ -267,10 +267,12 @@ All model architecture families include variants with pretrained weights. There
A full version of the list below with source links can be found in the [documentation](https://rwightman.github.io/pytorch-image-models/models/). A full version of the list below with source links can be found in the [documentation](https://rwightman.github.io/pytorch-image-models/models/).
* Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723 * Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
* BEiT - https://arxiv.org/abs/2106.08254
* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370 * Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
* Bottleneck Transformers - https://arxiv.org/abs/2101.11605 * Bottleneck Transformers - https://arxiv.org/abs/2101.11605
* CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239 * CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
* CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399 * CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
* ConvNeXt - https://arxiv.org/abs/2201.03545
* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697 * ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
* CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929 * CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
* DeiT (Vision Transformer) - https://arxiv.org/abs/2012.12877 * DeiT (Vision Transformer) - https://arxiv.org/abs/2012.12877
@ -288,11 +290,11 @@ A full version of the list below with source links can be found in the [document
* MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626 * MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
* MobileNet-V2 - https://arxiv.org/abs/1801.04381 * MobileNet-V2 - https://arxiv.org/abs/1801.04381
* Single-Path NAS - https://arxiv.org/abs/1904.02877 * Single-Path NAS - https://arxiv.org/abs/1904.02877
* TinyNet - https://arxiv.org/abs/2010.14819
* GhostNet - https://arxiv.org/abs/1911.11907 * GhostNet - https://arxiv.org/abs/1911.11907
* gMLP - https://arxiv.org/abs/2105.08050 * gMLP - https://arxiv.org/abs/2105.08050
* GPU-Efficient Networks - https://arxiv.org/abs/2006.14090 * GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
* Halo Nets - https://arxiv.org/abs/2103.12731 * Halo Nets - https://arxiv.org/abs/2103.12731
* HardCoRe-NAS - https://arxiv.org/abs/2102.11646
* HRNet - https://arxiv.org/abs/1908.07919 * HRNet - https://arxiv.org/abs/1908.07919
* Inception-V3 - https://arxiv.org/abs/1512.00567 * Inception-V3 - https://arxiv.org/abs/1512.00567
* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261 * Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
@ -300,7 +302,11 @@ A full version of the list below with source links can be found in the [document
* LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136 * LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
* MLP-Mixer - https://arxiv.org/abs/2105.01601 * MLP-Mixer - https://arxiv.org/abs/2105.01601
* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244 * MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
* FBNet-V3 - https://arxiv.org/abs/2006.02049
* HardCoRe-NAS - https://arxiv.org/abs/2102.11646
* LCNet - https://arxiv.org/abs/2109.15099
* NASNet-A - https://arxiv.org/abs/1707.07012 * NASNet-A - https://arxiv.org/abs/1707.07012
* NesT - https://arxiv.org/abs/2105.12723
* NFNet-F - https://arxiv.org/abs/2102.06171 * NFNet-F - https://arxiv.org/abs/2102.06171
* NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692 * NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
* PNasNet - https://arxiv.org/abs/1712.00559 * PNasNet - https://arxiv.org/abs/1712.00559
@ -326,6 +332,7 @@ A full version of the list below with source links can be found in the [document
* Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112 * Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
* TResNet - https://arxiv.org/abs/2003.13630 * TResNet - https://arxiv.org/abs/2003.13630
* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf * Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
* Visformer - https://arxiv.org/abs/2104.12533
* Vision Transformer - https://arxiv.org/abs/2010.11929 * Vision Transformer - https://arxiv.org/abs/2010.11929
* VovNet V2 and V1 - https://arxiv.org/abs/1911.06667 * VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
* Xception - https://arxiv.org/abs/1610.02357 * Xception - https://arxiv.org/abs/1610.02357