readme: ---
  license: apache-2.0
  tags:
  - object-detection
  - computer-vision
  - object-classification
  language:
  - en
  ---
  
  ### Model Description
  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
  
  Bottleneck Transformers - https://arxiv.org/abs/2101.11605
  
  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
  
  CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803
  
  ConvNeXt - https://arxiv.org/abs/2201.03545
  
  ConvNeXt-V2 - http://arxiv.org/abs/2301.00808
  
  ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
  
  CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
  
  DeiT - https://arxiv.org/abs/2012.12877
  
  DeiT-III - https://arxiv.org/pdf/2204.07118.pdf
  
  DenseNet - https://arxiv.org/abs/1608.06993
  
  DLA - https://arxiv.org/abs/1707.06484
  
  DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
  
  EdgeNeXt - https://arxiv.org/abs/2206.10589
  
  EfficientFormer - https://arxiv.org/abs/2206.01191
  
  EfficientNet (MBConvNet Family)
  
  EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
  
  EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
  
  EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
  
  EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
  
  EfficientNet V2 - https://arxiv.org/abs/2104.00298
  
  FBNet-C - https://arxiv.org/abs/1812.03443
  
  MixNet - https://arxiv.org/abs/1907.09595
  
  MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
  
  MobileNet-V2 - https://arxiv.org/abs/1801.04381
  
  Single-Path NAS - https://arxiv.org/abs/1904.02877
  
  TinyNet - https://arxiv.org/abs/2010.14819
  
  EVA - https://arxiv.org/abs/2211.07636
  
  FlexiViT - https://arxiv.org/abs/2212.08013
  
  GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
  
  GhostNet - https://arxiv.org/abs/1911.11907
  
  gMLP - https://arxiv.org/abs/2105.08050
  
  GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
  
  Halo Nets - https://arxiv.org/abs/2103.12731
  
  HRNet - https://arxiv.org/abs/1908.07919
  
  Inception-V3 - https://arxiv.org/abs/1512.00567
  
  Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
  
  Lambda Networks - https://arxiv.org/abs/2102.08602
  
  LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
  
  MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697
  
  MLP-Mixer - https://arxiv.org/abs/2105.01601
  
  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
  
  MobileViT - https://arxiv.org/abs/2110.02178
  
  MobileViT-V2 - https://arxiv.org/abs/2206.02680
  
  MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526
  
  NASNet-A - https://arxiv.org/abs/1707.07012
  
  NesT - https://arxiv.org/abs/2105.12723
  
  NFNet-F - https://arxiv.org/abs/2102.06171
  
  NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
  
  PNasNet - https://arxiv.org/abs/1712.00559
  
  PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418
  
  Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302
  
  PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797
  
  RegNet - https://arxiv.org/abs/2003.13678
  
  RegNetZ - https://arxiv.org/abs/2103.06877
  
  RepVGG - https://arxiv.org/abs/2101.03697
  
  ResMLP - https://arxiv.org/abs/2105.03404
  
  ResNet/ResNeXt
  
  ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
  
  ResNeXt - https://arxiv.org/abs/1611.05431
  
  'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187
  
  Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932
  
  Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546
  
  ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4
  
  Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507
  
  ResNet-RS - https://arxiv.org/abs/2103.07579
  
  Res2Net - https://arxiv.org/abs/1904.01169
  
  ResNeSt - https://arxiv.org/abs/2004.08955
  
  ReXNet - https://arxiv.org/abs/2007.00992
  
  SelecSLS - https://arxiv.org/abs/1907.00837
  
  Selective Kernel Networks - https://arxiv.org/abs/1903.06586
  
  Sequencer2D - https://arxiv.org/abs/2205.01972
  
  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
  
  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/abs/2104.13840
  
  Visformer - https://arxiv.org/abs/2104.12533
  
  Vision Transform