.. rst-class:: table-weights
.. table::
    :widths: 110 18 18 18 18 10 

    ==================================================================================  ===========  ===========  ============  ============  =========================================================================================================
    **Weight**                                                                            **Acc@1**    **Acc@5**  **Params**      **GFLOPS**  **Recipe**
    ==================================================================================  ===========  ===========  ============  ============  =========================================================================================================
    :class:`AlexNet_Weights.IMAGENET1K_V1 <AlexNet_Weights>`                                 56.522       79.066  61.1M                 0.71  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`ConvNeXt_Base_Weights.IMAGENET1K_V1 <ConvNeXt_Base_Weights>`                     84.062       96.87   88.6M                15.36  `link <https://github.com/pytorch/vision/tree/main/references/classification#convnext>`__
    :class:`ConvNeXt_Large_Weights.IMAGENET1K_V1 <ConvNeXt_Large_Weights>`                   84.414       96.976  197.8M               34.36  `link <https://github.com/pytorch/vision/tree/main/references/classification#convnext>`__
    :class:`ConvNeXt_Small_Weights.IMAGENET1K_V1 <ConvNeXt_Small_Weights>`                   83.616       96.65   50.2M                 8.68  `link <https://github.com/pytorch/vision/tree/main/references/classification#convnext>`__
    :class:`ConvNeXt_Tiny_Weights.IMAGENET1K_V1 <ConvNeXt_Tiny_Weights>`                     82.52        96.146  28.6M                 4.46  `link <https://github.com/pytorch/vision/tree/main/references/classification#convnext>`__
    :class:`DenseNet121_Weights.IMAGENET1K_V1 <DenseNet121_Weights>`                         74.434       91.972  8.0M                  2.83  `link <https://github.com/pytorch/vision/pull/116>`__
    :class:`DenseNet161_Weights.IMAGENET1K_V1 <DenseNet161_Weights>`                         77.138       93.56   28.7M                 7.73  `link <https://github.com/pytorch/vision/pull/116>`__
    :class:`DenseNet169_Weights.IMAGENET1K_V1 <DenseNet169_Weights>`                         75.6         92.806  14.1M                 3.36  `link <https://github.com/pytorch/vision/pull/116>`__
    :class:`DenseNet201_Weights.IMAGENET1K_V1 <DenseNet201_Weights>`                         76.896       93.37   20.0M                 4.29  `link <https://github.com/pytorch/vision/pull/116>`__
    :class:`EfficientNet_B0_Weights.IMAGENET1K_V1 <EfficientNet_B0_Weights>`                 77.692       93.532  5.3M                  0.39  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_B1_Weights.IMAGENET1K_V1 <EfficientNet_B1_Weights>`                 78.642       94.186  7.8M                  0.69  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_B1_Weights.IMAGENET1K_V2 <EfficientNet_B1_Weights>`                 79.838       94.934  7.8M                  0.69  `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuning>`__
    :class:`EfficientNet_B2_Weights.IMAGENET1K_V1 <EfficientNet_B2_Weights>`                 80.608       95.31   9.1M                  1.09  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_B3_Weights.IMAGENET1K_V1 <EfficientNet_B3_Weights>`                 82.008       96.054  12.2M                 1.83  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_B4_Weights.IMAGENET1K_V1 <EfficientNet_B4_Weights>`                 83.384       96.594  19.3M                 4.39  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_B5_Weights.IMAGENET1K_V1 <EfficientNet_B5_Weights>`                 83.444       96.628  30.4M                10.27  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_B6_Weights.IMAGENET1K_V1 <EfficientNet_B6_Weights>`                 84.008       96.916  43.0M                19.07  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_B7_Weights.IMAGENET1K_V1 <EfficientNet_B7_Weights>`                 84.122       96.908  66.3M                37.75  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1>`__
    :class:`EfficientNet_V2_L_Weights.IMAGENET1K_V1 <EfficientNet_V2_L_Weights>`             85.808       97.788  118.5M               56.08  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2>`__
    :class:`EfficientNet_V2_M_Weights.IMAGENET1K_V1 <EfficientNet_V2_M_Weights>`             85.112       97.156  54.1M                24.58  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2>`__
    :class:`EfficientNet_V2_S_Weights.IMAGENET1K_V1 <EfficientNet_V2_S_Weights>`             84.228       96.878  21.5M                 8.37  `link <https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2>`__
    :class:`GoogLeNet_Weights.IMAGENET1K_V1 <GoogLeNet_Weights>`                             69.778       89.53   6.6M                  1.5   `link <https://github.com/pytorch/vision/tree/main/references/classification#googlenet>`__
    :class:`Inception_V3_Weights.IMAGENET1K_V1 <Inception_V3_Weights>`                       77.294       93.45   27.2M                 5.71  `link <https://github.com/pytorch/vision/tree/main/references/classification#inception-v3>`__
    :class:`MNASNet0_5_Weights.IMAGENET1K_V1 <MNASNet0_5_Weights>`                           67.734       87.49   2.2M                  0.1   `link <https://github.com/1e100/mnasnet_trainer>`__
    :class:`MNASNet0_75_Weights.IMAGENET1K_V1 <MNASNet0_75_Weights>`                         71.18        90.496  3.2M                  0.21  `link <https://github.com/pytorch/vision/pull/6019>`__
    :class:`MNASNet1_0_Weights.IMAGENET1K_V1 <MNASNet1_0_Weights>`                           73.456       91.51   4.4M                  0.31  `link <https://github.com/1e100/mnasnet_trainer>`__
    :class:`MNASNet1_3_Weights.IMAGENET1K_V1 <MNASNet1_3_Weights>`                           76.506       93.522  6.3M                  0.53  `link <https://github.com/pytorch/vision/pull/6019>`__
    :class:`MaxVit_T_Weights.IMAGENET1K_V1 <MaxVit_T_Weights>`                               83.7         96.722  30.9M                 5.56  `link <https://github.com/pytorch/vision/tree/main/references/classification#maxvit>`__
    :class:`MobileNet_V2_Weights.IMAGENET1K_V1 <MobileNet_V2_Weights>`                       71.878       90.286  3.5M                  0.3   `link <https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2>`__
    :class:`MobileNet_V2_Weights.IMAGENET1K_V2 <MobileNet_V2_Weights>`                       72.154       90.822  3.5M                  0.3   `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning>`__
    :class:`MobileNet_V3_Large_Weights.IMAGENET1K_V1 <MobileNet_V3_Large_Weights>`           74.042       91.34   5.5M                  0.22  `link <https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small>`__
    :class:`MobileNet_V3_Large_Weights.IMAGENET1K_V2 <MobileNet_V3_Large_Weights>`           75.274       92.566  5.5M                  0.22  `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning>`__
    :class:`MobileNet_V3_Small_Weights.IMAGENET1K_V1 <MobileNet_V3_Small_Weights>`           67.668       87.402  2.5M                  0.06  `link <https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small>`__
    :class:`RegNet_X_16GF_Weights.IMAGENET1K_V1 <RegNet_X_16GF_Weights>`                     80.058       94.944  54.3M                15.94  `link <https://github.com/pytorch/vision/tree/main/references/classification#medium-models>`__
    :class:`RegNet_X_16GF_Weights.IMAGENET1K_V2 <RegNet_X_16GF_Weights>`                     82.716       96.196  54.3M                15.94  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_X_1_6GF_Weights.IMAGENET1K_V1 <RegNet_X_1_6GF_Weights>`                   77.04        93.44   9.2M                  1.6   `link <https://github.com/pytorch/vision/tree/main/references/classification#small-models>`__
    :class:`RegNet_X_1_6GF_Weights.IMAGENET1K_V2 <RegNet_X_1_6GF_Weights>`                   79.668       94.922  9.2M                  1.6   `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres>`__
    :class:`RegNet_X_32GF_Weights.IMAGENET1K_V1 <RegNet_X_32GF_Weights>`                     80.622       95.248  107.8M               31.74  `link <https://github.com/pytorch/vision/tree/main/references/classification#large-models>`__
    :class:`RegNet_X_32GF_Weights.IMAGENET1K_V2 <RegNet_X_32GF_Weights>`                     83.014       96.288  107.8M               31.74  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_X_3_2GF_Weights.IMAGENET1K_V1 <RegNet_X_3_2GF_Weights>`                   78.364       93.992  15.3M                 3.18  `link <https://github.com/pytorch/vision/tree/main/references/classification#medium-models>`__
    :class:`RegNet_X_3_2GF_Weights.IMAGENET1K_V2 <RegNet_X_3_2GF_Weights>`                   81.196       95.43   15.3M                 3.18  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_X_400MF_Weights.IMAGENET1K_V1 <RegNet_X_400MF_Weights>`                   72.834       90.95   5.5M                  0.41  `link <https://github.com/pytorch/vision/tree/main/references/classification#small-models>`__
    :class:`RegNet_X_400MF_Weights.IMAGENET1K_V2 <RegNet_X_400MF_Weights>`                   74.864       92.322  5.5M                  0.41  `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres>`__
    :class:`RegNet_X_800MF_Weights.IMAGENET1K_V1 <RegNet_X_800MF_Weights>`                   75.212       92.348  7.3M                  0.8   `link <https://github.com/pytorch/vision/tree/main/references/classification#small-models>`__
    :class:`RegNet_X_800MF_Weights.IMAGENET1K_V2 <RegNet_X_800MF_Weights>`                   77.522       93.826  7.3M                  0.8   `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres>`__
    :class:`RegNet_X_8GF_Weights.IMAGENET1K_V1 <RegNet_X_8GF_Weights>`                       79.344       94.686  39.6M                 8     `link <https://github.com/pytorch/vision/tree/main/references/classification#medium-models>`__
    :class:`RegNet_X_8GF_Weights.IMAGENET1K_V2 <RegNet_X_8GF_Weights>`                       81.682       95.678  39.6M                 8     `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_E2E_V1 <RegNet_Y_128GF_Weights>`          88.228       98.682  644.8M              374.57  `link <https://github.com/facebookresearch/SWAG>`__
    :class:`RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 <RegNet_Y_128GF_Weights>`       86.068       97.844  644.8M              127.52  `link <https://github.com/pytorch/vision/pull/5793>`__
    :class:`RegNet_Y_16GF_Weights.IMAGENET1K_V1 <RegNet_Y_16GF_Weights>`                     80.424       95.24   83.6M                15.91  `link <https://github.com/pytorch/vision/tree/main/references/classification#large-models>`__
    :class:`RegNet_Y_16GF_Weights.IMAGENET1K_V2 <RegNet_Y_16GF_Weights>`                     82.886       96.328  83.6M                15.91  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_E2E_V1 <RegNet_Y_16GF_Weights>`            86.012       98.054  83.6M                46.73  `link <https://github.com/facebookresearch/SWAG>`__
    :class:`RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 <RegNet_Y_16GF_Weights>`         83.976       97.244  83.6M                15.91  `link <https://github.com/pytorch/vision/pull/5793>`__
    :class:`RegNet_Y_1_6GF_Weights.IMAGENET1K_V1 <RegNet_Y_1_6GF_Weights>`                   77.95        93.966  11.2M                 1.61  `link <https://github.com/pytorch/vision/tree/main/references/classification#small-models>`__
    :class:`RegNet_Y_1_6GF_Weights.IMAGENET1K_V2 <RegNet_Y_1_6GF_Weights>`                   80.876       95.444  11.2M                 1.61  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_Y_32GF_Weights.IMAGENET1K_V1 <RegNet_Y_32GF_Weights>`                     80.878       95.34   145.0M               32.28  `link <https://github.com/pytorch/vision/tree/main/references/classification#large-models>`__
    :class:`RegNet_Y_32GF_Weights.IMAGENET1K_V2 <RegNet_Y_32GF_Weights>`                     83.368       96.498  145.0M               32.28  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_E2E_V1 <RegNet_Y_32GF_Weights>`            86.838       98.362  145.0M               94.83  `link <https://github.com/facebookresearch/SWAG>`__
    :class:`RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 <RegNet_Y_32GF_Weights>`         84.622       97.48   145.0M               32.28  `link <https://github.com/pytorch/vision/pull/5793>`__
    :class:`RegNet_Y_3_2GF_Weights.IMAGENET1K_V1 <RegNet_Y_3_2GF_Weights>`                   78.948       94.576  19.4M                 3.18  `link <https://github.com/pytorch/vision/tree/main/references/classification#medium-models>`__
    :class:`RegNet_Y_3_2GF_Weights.IMAGENET1K_V2 <RegNet_Y_3_2GF_Weights>`                   81.982       95.972  19.4M                 3.18  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_Y_400MF_Weights.IMAGENET1K_V1 <RegNet_Y_400MF_Weights>`                   74.046       91.716  4.3M                  0.4   `link <https://github.com/pytorch/vision/tree/main/references/classification#small-models>`__
    :class:`RegNet_Y_400MF_Weights.IMAGENET1K_V2 <RegNet_Y_400MF_Weights>`                   75.804       92.742  4.3M                  0.4   `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_Y_800MF_Weights.IMAGENET1K_V1 <RegNet_Y_800MF_Weights>`                   76.42        93.136  6.4M                  0.83  `link <https://github.com/pytorch/vision/tree/main/references/classification#small-models>`__
    :class:`RegNet_Y_800MF_Weights.IMAGENET1K_V2 <RegNet_Y_800MF_Weights>`                   78.828       94.502  6.4M                  0.83  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`RegNet_Y_8GF_Weights.IMAGENET1K_V1 <RegNet_Y_8GF_Weights>`                       80.032       95.048  39.4M                 8.47  `link <https://github.com/pytorch/vision/tree/main/references/classification#medium-models>`__
    :class:`RegNet_Y_8GF_Weights.IMAGENET1K_V2 <RegNet_Y_8GF_Weights>`                       82.828       96.33   39.4M                 8.47  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`ResNeXt101_32X8D_Weights.IMAGENET1K_V1 <ResNeXt101_32X8D_Weights>`               79.312       94.526  88.8M                16.41  `link <https://github.com/pytorch/vision/tree/main/references/classification#resnext>`__
    :class:`ResNeXt101_32X8D_Weights.IMAGENET1K_V2 <ResNeXt101_32X8D_Weights>`               82.834       96.228  88.8M                16.41  `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres>`__
    :class:`ResNeXt101_64X4D_Weights.IMAGENET1K_V1 <ResNeXt101_64X4D_Weights>`               83.246       96.454  83.5M                15.46  `link <https://github.com/pytorch/vision/pull/5935>`__
    :class:`ResNeXt50_32X4D_Weights.IMAGENET1K_V1 <ResNeXt50_32X4D_Weights>`                 77.618       93.698  25.0M                 4.23  `link <https://github.com/pytorch/vision/tree/main/references/classification#resnext>`__
    :class:`ResNeXt50_32X4D_Weights.IMAGENET1K_V2 <ResNeXt50_32X4D_Weights>`                 81.198       95.34   25.0M                 4.23  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`ResNet101_Weights.IMAGENET1K_V1 <ResNet101_Weights>`                             77.374       93.546  44.5M                 7.8   `link <https://github.com/pytorch/vision/tree/main/references/classification#resnet>`__
    :class:`ResNet101_Weights.IMAGENET1K_V2 <ResNet101_Weights>`                             81.886       95.78   44.5M                 7.8   `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`ResNet152_Weights.IMAGENET1K_V1 <ResNet152_Weights>`                             78.312       94.046  60.2M                11.51  `link <https://github.com/pytorch/vision/tree/main/references/classification#resnet>`__
    :class:`ResNet152_Weights.IMAGENET1K_V2 <ResNet152_Weights>`                             82.284       96.002  60.2M                11.51  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`ResNet18_Weights.IMAGENET1K_V1 <ResNet18_Weights>`                               69.758       89.078  11.7M                 1.81  `link <https://github.com/pytorch/vision/tree/main/references/classification#resnet>`__
    :class:`ResNet34_Weights.IMAGENET1K_V1 <ResNet34_Weights>`                               73.314       91.42   21.8M                 3.66  `link <https://github.com/pytorch/vision/tree/main/references/classification#resnet>`__
    :class:`ResNet50_Weights.IMAGENET1K_V1 <ResNet50_Weights>`                               76.13        92.862  25.6M                 4.09  `link <https://github.com/pytorch/vision/tree/main/references/classification#resnet>`__
    :class:`ResNet50_Weights.IMAGENET1K_V2 <ResNet50_Weights>`                               80.858       95.434  25.6M                 4.09  `link <https://github.com/pytorch/vision/issues/3995#issuecomment-1013906621>`__
    :class:`ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1 <ShuffleNet_V2_X0_5_Weights>`           60.552       81.746  1.4M                  0.04  `link <https://github.com/ericsun99/Shufflenet-v2-Pytorch>`__
    :class:`ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1 <ShuffleNet_V2_X1_0_Weights>`           69.362       88.316  2.3M                  0.14  `link <https://github.com/ericsun99/Shufflenet-v2-Pytorch>`__
    :class:`ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1 <ShuffleNet_V2_X1_5_Weights>`           72.996       91.086  3.5M                  0.3   `link <https://github.com/pytorch/vision/pull/5906>`__
    :class:`ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1 <ShuffleNet_V2_X2_0_Weights>`           76.23        93.006  7.4M                  0.58  `link <https://github.com/pytorch/vision/pull/5906>`__
    :class:`SqueezeNet1_0_Weights.IMAGENET1K_V1 <SqueezeNet1_0_Weights>`                     58.092       80.42   1.2M                  0.82  `link <https://github.com/pytorch/vision/pull/49#issuecomment-277560717>`__
    :class:`SqueezeNet1_1_Weights.IMAGENET1K_V1 <SqueezeNet1_1_Weights>`                     58.178       80.624  1.2M                  0.35  `link <https://github.com/pytorch/vision/pull/49#issuecomment-277560717>`__
    :class:`Swin_B_Weights.IMAGENET1K_V1 <Swin_B_Weights>`                                   83.582       96.64   87.8M                15.43  `link <https://github.com/pytorch/vision/tree/main/references/classification#swintransformer>`__
    :class:`Swin_S_Weights.IMAGENET1K_V1 <Swin_S_Weights>`                                   83.196       96.36   49.6M                 8.74  `link <https://github.com/pytorch/vision/tree/main/references/classification#swintransformer>`__
    :class:`Swin_T_Weights.IMAGENET1K_V1 <Swin_T_Weights>`                                   81.474       95.776  28.3M                 4.49  `link <https://github.com/pytorch/vision/tree/main/references/classification#swintransformer>`__
    :class:`Swin_V2_B_Weights.IMAGENET1K_V1 <Swin_V2_B_Weights>`                             84.112       96.864  87.9M                20.32  `link <https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2>`__
    :class:`Swin_V2_S_Weights.IMAGENET1K_V1 <Swin_V2_S_Weights>`                             83.712       96.816  49.7M                11.55  `link <https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2>`__
    :class:`Swin_V2_T_Weights.IMAGENET1K_V1 <Swin_V2_T_Weights>`                             82.072       96.132  28.4M                 5.94  `link <https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2>`__
    :class:`VGG11_BN_Weights.IMAGENET1K_V1 <VGG11_BN_Weights>`                               70.37        89.81   132.9M                7.61  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`VGG11_Weights.IMAGENET1K_V1 <VGG11_Weights>`                                     69.02        88.628  132.9M                7.61  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`VGG13_BN_Weights.IMAGENET1K_V1 <VGG13_BN_Weights>`                               71.586       90.374  133.1M               11.31  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`VGG13_Weights.IMAGENET1K_V1 <VGG13_Weights>`                                     69.928       89.246  133.0M               11.31  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`VGG16_BN_Weights.IMAGENET1K_V1 <VGG16_BN_Weights>`                               73.36        91.516  138.4M               15.47  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`VGG16_Weights.IMAGENET1K_V1 <VGG16_Weights>`                                     71.592       90.382  138.4M               15.47  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`VGG16_Weights.IMAGENET1K_FEATURES <VGG16_Weights>`                              nan          nan      138.4M               15.47  `link <https://github.com/amdegroot/ssd.pytorch#training-ssd>`__
    :class:`VGG19_BN_Weights.IMAGENET1K_V1 <VGG19_BN_Weights>`                               74.218       91.842  143.7M               19.63  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`VGG19_Weights.IMAGENET1K_V1 <VGG19_Weights>`                                     72.376       90.876  143.7M               19.63  `link <https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg>`__
    :class:`ViT_B_16_Weights.IMAGENET1K_V1 <ViT_B_16_Weights>`                               81.072       95.318  86.6M                17.56  `link <https://github.com/pytorch/vision/tree/main/references/classification#vit_b_16>`__
    :class:`ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1 <ViT_B_16_Weights>`                      85.304       97.65   86.9M                55.48  `link <https://github.com/facebookresearch/SWAG>`__
    :class:`ViT_B_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 <ViT_B_16_Weights>`                   81.886       96.18   86.6M                17.56  `link <https://github.com/pytorch/vision/pull/5793>`__
    :class:`ViT_B_32_Weights.IMAGENET1K_V1 <ViT_B_32_Weights>`                               75.912       92.466  88.2M                 4.41  `link <https://github.com/pytorch/vision/tree/main/references/classification#vit_b_32>`__
    :class:`ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 <ViT_H_14_Weights>`                      88.552       98.694  633.5M             1016.72  `link <https://github.com/facebookresearch/SWAG>`__
    :class:`ViT_H_14_Weights.IMAGENET1K_SWAG_LINEAR_V1 <ViT_H_14_Weights>`                   85.708       97.73   632.0M              167.29  `link <https://github.com/pytorch/vision/pull/5793>`__
    :class:`ViT_L_16_Weights.IMAGENET1K_V1 <ViT_L_16_Weights>`                               79.662       94.638  304.3M               61.55  `link <https://github.com/pytorch/vision/tree/main/references/classification#vit_l_16>`__
    :class:`ViT_L_16_Weights.IMAGENET1K_SWAG_E2E_V1 <ViT_L_16_Weights>`                      88.064       98.512  305.2M              361.99  `link <https://github.com/facebookresearch/SWAG>`__
    :class:`ViT_L_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 <ViT_L_16_Weights>`                   85.146       97.422  304.3M               61.55  `link <https://github.com/pytorch/vision/pull/5793>`__
    :class:`ViT_L_32_Weights.IMAGENET1K_V1 <ViT_L_32_Weights>`                               76.972       93.07   306.5M               15.38  `link <https://github.com/pytorch/vision/tree/main/references/classification#vit_l_32>`__
    :class:`Wide_ResNet101_2_Weights.IMAGENET1K_V1 <Wide_ResNet101_2_Weights>`               78.848       94.284  126.9M               22.75  `link <https://github.com/pytorch/vision/pull/912#issue-445437439>`__
    :class:`Wide_ResNet101_2_Weights.IMAGENET1K_V2 <Wide_ResNet101_2_Weights>`               82.51        96.02   126.9M               22.75  `link <https://github.com/pytorch/vision/issues/3995#new-recipe>`__
    :class:`Wide_ResNet50_2_Weights.IMAGENET1K_V1 <Wide_ResNet50_2_Weights>`                 78.468       94.086  68.9M                11.4   `link <https://github.com/pytorch/vision/pull/912#issue-445437439>`__
    :class:`Wide_ResNet50_2_Weights.IMAGENET1K_V2 <Wide_ResNet50_2_Weights>`                 81.602       95.758  68.9M                11.4   `link <https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres>`__
    ==================================================================================  ===========  ===========  ============  ============  =========================================================================================================

