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Update README.md
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README.md
CHANGED
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@@ -30,5 +30,127 @@ for weights in backbones["IMAGENET1K_V2"]:
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print(weights)
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```
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## Reference
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<https://pytorch.org/vision/main/_modules>
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print(weights)
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```
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+
## Param count
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+
| Backbone | Params(M) |
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| 35 |
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| :--: | :--: |
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| 36 |
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| SqueezeNet1_0_Weights.IMAGENET1K_V1 | 1.2 |
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| 37 |
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| SqueezeNet1_1_Weights.IMAGENET1K_V1 | 1.2 |
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| 38 |
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| ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1 | 1.4 |
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| 39 |
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| MNASNet0_5_Weights.IMAGENET1K_V1 | 2.2 |
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| 40 |
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| ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1 | 2.3 |
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| 41 |
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| MobileNet_V3_Small_Weights.IMAGENET1K_V1 | 2.5 |
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| 42 |
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| MNASNet0_75_Weights.IMAGENET1K_V1 | 3.2 |
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| 43 |
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| MobileNet_V2_Weights.IMAGENET1K_V1 | 3.5 |
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| 44 |
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| MobileNet_V2_Weights.IMAGENET1K_V2 | 3.5 |
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| 45 |
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| ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1 | 3.5 |
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| 46 |
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| RegNet_Y_400MF_Weights.IMAGENET1K_V1 | 4.3 |
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| 47 |
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| RegNet_Y_400MF_Weights.IMAGENET1K_V2 | 4.3 |
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| 48 |
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| MNASNet1_0_Weights.IMAGENET1K_V1 | 4.4 |
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| 49 |
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| EfficientNet_B0_Weights.IMAGENET1K_V1 | 5.3 |
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| 50 |
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| MobileNet_V3_Large_Weights.IMAGENET1K_V1 | 5.5 |
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| 51 |
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| MobileNet_V3_Large_Weights.IMAGENET1K_V2 | 5.5 |
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| 52 |
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| RegNet_X_400MF_Weights.IMAGENET1K_V1 | 5.5 |
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| 53 |
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| RegNet_X_400MF_Weights.IMAGENET1K_V2 | 5.5 |
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| 54 |
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| MNASNet1_3_Weights.IMAGENET1K_V1 | 6.3 |
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| 55 |
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| RegNet_Y_800MF_Weights.IMAGENET1K_V1 | 6.4 |
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| 56 |
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| RegNet_Y_800MF_Weights.IMAGENET1K_V2 | 6.4 |
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| 57 |
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| GoogLeNet_Weights.IMAGENET1K_V1 | 6.6 |
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| 58 |
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| RegNet_X_800MF_Weights.IMAGENET1K_V1 | 7.3 |
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| 59 |
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| RegNet_X_800MF_Weights.IMAGENET1K_V2 | 7.3 |
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| 60 |
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| ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1 | 7.4 |
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| 61 |
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| EfficientNet_B1_Weights.IMAGENET1K_V1 | 7.8 |
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| 62 |
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| EfficientNet_B1_Weights.IMAGENET1K_V2 | 7.8 |
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| 63 |
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| DenseNet121_Weights.IMAGENET1K_V1 | 8 |
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| 64 |
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| EfficientNet_B2_Weights.IMAGENET1K_V1 | 9.1 |
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| 65 |
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| RegNet_X_1_6GF_Weights.IMAGENET1K_V1 | 9.2 |
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| 66 |
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| RegNet_X_1_6GF_Weights.IMAGENET1K_V2 | 9.2 |
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| 67 |
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| RegNet_Y_1_6GF_Weights.IMAGENET1K_V1 | 11.2 |
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| 68 |
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| RegNet_Y_1_6GF_Weights.IMAGENET1K_V2 | 11.2 |
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| 69 |
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| ResNet18_Weights.IMAGENET1K_V1 | 11.7 |
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| 70 |
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| EfficientNet_B3_Weights.IMAGENET1K_V1 | 12.2 |
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| 71 |
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| DenseNet169_Weights.IMAGENET1K_V1 | 14.1 |
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| 72 |
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| RegNet_X_3_2GF_Weights.IMAGENET1K_V1 | 15.3 |
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| 73 |
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| RegNet_X_3_2GF_Weights.IMAGENET1K_V2 | 15.3 |
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| 74 |
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| EfficientNet_B4_Weights.IMAGENET1K_V1 | 19.3 |
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| 75 |
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| RegNet_Y_3_2GF_Weights.IMAGENET1K_V1 | 19.4 |
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| RegNet_Y_3_2GF_Weights.IMAGENET1K_V2 | 19.4 |
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| 77 |
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| DenseNet201_Weights.IMAGENET1K_V1 | 20 |
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| 78 |
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| EfficientNet_V2_S_Weights.IMAGENET1K_V1 | 21.5 |
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| 79 |
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| ResNet34_Weights.IMAGENET1K_V1 | 21.8 |
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| 80 |
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| ResNeXt50_32X4D_Weights.IMAGENET1K_V1 | 25 |
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| 81 |
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| ResNeXt50_32X4D_Weights.IMAGENET1K_V2 | 25 |
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| 82 |
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| ResNet50_Weights.IMAGENET1K_V1 | 25.6 |
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| ResNet50_Weights.IMAGENET1K_V2 | 25.6 |
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| 84 |
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| Inception_V3_Weights.IMAGENET1K_V1 | 27.2 |
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| 85 |
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| Swin_T_Weights.IMAGENET1K_V1 | 28.3 |
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| 86 |
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| Swin_V2_T_Weights.IMAGENET1K_V1 | 28.4 |
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| 87 |
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| ConvNeXt_Tiny_Weights.IMAGENET1K_V1 | 28.6 |
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| 88 |
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| DenseNet161_Weights.IMAGENET1K_V1 | 28.7 |
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| 89 |
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| EfficientNet_B5_Weights.IMAGENET1K_V1 | 30.4 |
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| 90 |
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| MaxVit_T_Weights.IMAGENET1K_V1 | 30.9 |
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| 91 |
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| RegNet_Y_8GF_Weights.IMAGENET1K_V1 | 39.4 |
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| 92 |
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| RegNet_Y_8GF_Weights.IMAGENET1K_V2 | 39.4 |
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| 93 |
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| RegNet_X_8GF_Weights.IMAGENET1K_V1 | 39.6 |
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| 94 |
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| RegNet_X_8GF_Weights.IMAGENET1K_V2 | 39.6 |
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| 95 |
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| EfficientNet_B6_Weights.IMAGENET1K_V1 | 43 |
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| ResNet101_Weights.IMAGENET1K_V1 | 44.5 |
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| ResNet101_Weights.IMAGENET1K_V2 | 44.5 |
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| 98 |
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| Swin_S_Weights.IMAGENET1K_V1 | 49.6 |
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| Swin_V2_S_Weights.IMAGENET1K_V1 | 49.7 |
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| ConvNeXt_Small_Weights.IMAGENET1K_V1 | 50.2 |
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| EfficientNet_V2_M_Weights.IMAGENET1K_V1 | 54.1 |
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| 102 |
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| RegNet_X_16GF_Weights.IMAGENET1K_V1 | 54.3 |
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| 103 |
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| RegNet_X_16GF_Weights.IMAGENET1K_V2 | 54.3 |
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| 104 |
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| ResNet152_Weights.IMAGENET1K_V1 | 60.2 |
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| 105 |
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| ResNet152_Weights.IMAGENET1K_V2 | 60.2 |
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| 106 |
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| AlexNet_Weights.IMAGENET1K_V1 | 61.1 |
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| 107 |
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| EfficientNet_B7_Weights.IMAGENET1K_V1 | 66.3 |
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| 108 |
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| Wide_ResNet50_2_Weights.IMAGENET1K_V1 | 68.9 |
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| 109 |
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| Wide_ResNet50_2_Weights.IMAGENET1K_V2 | 68.9 |
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| 110 |
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| ResNeXt101_64X4D_Weights.IMAGENET1K_V1 | 83.5 |
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| 111 |
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| RegNet_Y_16GF_Weights.IMAGENET1K_V1 | 83.6 |
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| 112 |
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| RegNet_Y_16GF_Weights.IMAGENET1K_V2 | 83.6 |
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| 113 |
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| RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 83.6 |
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| 114 |
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| RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 83.6 |
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| 115 |
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| ViT_B_16_Weights.IMAGENET1K_V1 | 86.6 |
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| 116 |
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| ViT_B_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 86.6 |
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| 117 |
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| ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 86.9 |
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| 118 |
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| Swin_B_Weights.IMAGENET1K_V1 | 87.8 |
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| 119 |
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| Swin_V2_B_Weights.IMAGENET1K_V1 | 87.9 |
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| 120 |
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| ViT_B_32_Weights.IMAGENET1K_V1 | 88.2 |
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| 121 |
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| ConvNeXt_Base_Weights.IMAGENET1K_V1 | 88.6 |
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| 122 |
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| ResNeXt101_32X8D_Weights.IMAGENET1K_V1 | 88.8 |
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| 123 |
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| ResNeXt101_32X8D_Weights.IMAGENET1K_V2 | 88.8 |
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| 124 |
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| RegNet_X_32GF_Weights.IMAGENET1K_V1 | 107.8 |
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| 125 |
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| RegNet_X_32GF_Weights.IMAGENET1K_V2 | 107.8 |
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| 126 |
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| EfficientNet_V2_L_Weights.IMAGENET1K_V1 | 118.5 |
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| 127 |
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| Wide_ResNet101_2_Weights.IMAGENET1K_V1 | 126.9 |
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| 128 |
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| Wide_ResNet101_2_Weights.IMAGENET1K_V2 | 126.9 |
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| 129 |
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| VGG11_BN_Weights.IMAGENET1K_V1 | 132.9 |
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| 130 |
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| VGG11_Weights.IMAGENET1K_V1 | 132.9 |
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| 131 |
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| VGG13_Weights.IMAGENET1K_V1 | 133 |
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| 132 |
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| VGG13_BN_Weights.IMAGENET1K_V1 | 133.1 |
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| 133 |
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| VGG16_BN_Weights.IMAGENET1K_V1 | 138.4 |
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| 134 |
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| VGG16_Weights.IMAGENET1K_V1 | 138.4 |
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| 135 |
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| VGG16_Weights.IMAGENET1K_FEATURES | 138.4 |
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| 136 |
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| VGG19_BN_Weights.IMAGENET1K_V1 | 143.7 |
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| 137 |
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| VGG19_Weights.IMAGENET1K_V1 | 143.7 |
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| 138 |
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| RegNet_Y_32GF_Weights.IMAGENET1K_V1 | 145 |
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| 139 |
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| RegNet_Y_32GF_Weights.IMAGENET1K_V2 | 145 |
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| 140 |
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| RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 145 |
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| 141 |
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| RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 145 |
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| 142 |
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| ConvNeXt_Large_Weights.IMAGENET1K_V1 | 197.8 |
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| 143 |
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| ViT_L_16_Weights.IMAGENET1K_V1 | 304.3 |
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| 144 |
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| ViT_L_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 304.3 |
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| 145 |
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| ViT_L_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 305.2 |
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| 146 |
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| ViT_L_32_Weights.IMAGENET1K_V1 | 306.5 |
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| 147 |
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| ViT_H_14_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 632 |
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| 148 |
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| ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 | 633.5 |
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| 149 |
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| RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 644.8 |
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| 150 |
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| RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 644.8 |
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| 151 |
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## Mirror
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<https://www.modelscope.cn/datasets/monetjoe/cv_backbones>
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## Reference
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<https://pytorch.org/vision/main/_modules>
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