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--- |
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license: mit |
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task_categories: |
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- image-classification |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- code |
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pretty_name: Vi-Backbones |
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size_categories: |
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- n<1K |
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viewer: false |
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--- |
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# Dataset Card for "monet-joe/cv_backbones" |
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## Viewer |
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<https://huggingface.co/spaces/monet-joe/cv-backbones> |
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## Maintenance |
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```bash |
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GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/monet-joe/cv_backbones |
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``` |
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## Usage |
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```python |
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from datasets import load_dataset |
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backbones = load_dataset("monet-joe/cv_backbones") |
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for weights in backbones["IMAGENET1K_V1"]: |
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print(weights) |
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for weights in backbones["IMAGENET1K_V2"]: |
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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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| :--: | :--: | |
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| SqueezeNet1_0_Weights.IMAGENET1K_V1 | 1.2 | |
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| SqueezeNet1_1_Weights.IMAGENET1K_V1 | 1.2 | |
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| ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1 | 1.4 | |
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| MNASNet0_5_Weights.IMAGENET1K_V1 | 2.2 | |
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| ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1 | 2.3 | |
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| MobileNet_V3_Small_Weights.IMAGENET1K_V1 | 2.5 | |
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| MNASNet0_75_Weights.IMAGENET1K_V1 | 3.2 | |
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| MobileNet_V2_Weights.IMAGENET1K_V1 | 3.5 | |
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| MobileNet_V2_Weights.IMAGENET1K_V2 | 3.5 | |
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| ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1 | 3.5 | |
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| RegNet_Y_400MF_Weights.IMAGENET1K_V1 | 4.3 | |
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| RegNet_Y_400MF_Weights.IMAGENET1K_V2 | 4.3 | |
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| MNASNet1_0_Weights.IMAGENET1K_V1 | 4.4 | |
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| EfficientNet_B0_Weights.IMAGENET1K_V1 | 5.3 | |
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| MobileNet_V3_Large_Weights.IMAGENET1K_V1 | 5.5 | |
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| MobileNet_V3_Large_Weights.IMAGENET1K_V2 | 5.5 | |
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| RegNet_X_400MF_Weights.IMAGENET1K_V1 | 5.5 | |
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| RegNet_X_400MF_Weights.IMAGENET1K_V2 | 5.5 | |
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| MNASNet1_3_Weights.IMAGENET1K_V1 | 6.3 | |
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| RegNet_Y_800MF_Weights.IMAGENET1K_V1 | 6.4 | |
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| RegNet_Y_800MF_Weights.IMAGENET1K_V2 | 6.4 | |
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| GoogLeNet_Weights.IMAGENET1K_V1 | 6.6 | |
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| RegNet_X_800MF_Weights.IMAGENET1K_V1 | 7.3 | |
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| RegNet_X_800MF_Weights.IMAGENET1K_V2 | 7.3 | |
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| ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1 | 7.4 | |
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| EfficientNet_B1_Weights.IMAGENET1K_V1 | 7.8 | |
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| EfficientNet_B1_Weights.IMAGENET1K_V2 | 7.8 | |
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| DenseNet121_Weights.IMAGENET1K_V1 | 8 | |
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| EfficientNet_B2_Weights.IMAGENET1K_V1 | 9.1 | |
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| RegNet_X_1_6GF_Weights.IMAGENET1K_V1 | 9.2 | |
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| RegNet_X_1_6GF_Weights.IMAGENET1K_V2 | 9.2 | |
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| RegNet_Y_1_6GF_Weights.IMAGENET1K_V1 | 11.2 | |
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| RegNet_Y_1_6GF_Weights.IMAGENET1K_V2 | 11.2 | |
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| ResNet18_Weights.IMAGENET1K_V1 | 11.7 | |
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| EfficientNet_B3_Weights.IMAGENET1K_V1 | 12.2 | |
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| DenseNet169_Weights.IMAGENET1K_V1 | 14.1 | |
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| RegNet_X_3_2GF_Weights.IMAGENET1K_V1 | 15.3 | |
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| RegNet_X_3_2GF_Weights.IMAGENET1K_V2 | 15.3 | |
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| EfficientNet_B4_Weights.IMAGENET1K_V1 | 19.3 | |
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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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| DenseNet201_Weights.IMAGENET1K_V1 | 20 | |
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| EfficientNet_V2_S_Weights.IMAGENET1K_V1 | 21.5 | |
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| ResNet34_Weights.IMAGENET1K_V1 | 21.8 | |
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| ResNeXt50_32X4D_Weights.IMAGENET1K_V1 | 25 | |
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| ResNeXt50_32X4D_Weights.IMAGENET1K_V2 | 25 | |
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| ResNet50_Weights.IMAGENET1K_V1 | 25.6 | |
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| ResNet50_Weights.IMAGENET1K_V2 | 25.6 | |
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| Inception_V3_Weights.IMAGENET1K_V1 | 27.2 | |
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| Swin_T_Weights.IMAGENET1K_V1 | 28.3 | |
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| Swin_V2_T_Weights.IMAGENET1K_V1 | 28.4 | |
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| ConvNeXt_Tiny_Weights.IMAGENET1K_V1 | 28.6 | |
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| DenseNet161_Weights.IMAGENET1K_V1 | 28.7 | |
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| EfficientNet_B5_Weights.IMAGENET1K_V1 | 30.4 | |
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| MaxVit_T_Weights.IMAGENET1K_V1 | 30.9 | |
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| RegNet_Y_8GF_Weights.IMAGENET1K_V1 | 39.4 | |
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| RegNet_Y_8GF_Weights.IMAGENET1K_V2 | 39.4 | |
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| RegNet_X_8GF_Weights.IMAGENET1K_V1 | 39.6 | |
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| RegNet_X_8GF_Weights.IMAGENET1K_V2 | 39.6 | |
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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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| 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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| RegNet_X_16GF_Weights.IMAGENET1K_V1 | 54.3 | |
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| RegNet_X_16GF_Weights.IMAGENET1K_V2 | 54.3 | |
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| ResNet152_Weights.IMAGENET1K_V1 | 60.2 | |
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| ResNet152_Weights.IMAGENET1K_V2 | 60.2 | |
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| AlexNet_Weights.IMAGENET1K_V1 | 61.1 | |
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| EfficientNet_B7_Weights.IMAGENET1K_V1 | 66.3 | |
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| Wide_ResNet50_2_Weights.IMAGENET1K_V1 | 68.9 | |
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| Wide_ResNet50_2_Weights.IMAGENET1K_V2 | 68.9 | |
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| ResNeXt101_64X4D_Weights.IMAGENET1K_V1 | 83.5 | |
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| RegNet_Y_16GF_Weights.IMAGENET1K_V1 | 83.6 | |
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| RegNet_Y_16GF_Weights.IMAGENET1K_V2 | 83.6 | |
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| RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 83.6 | |
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| RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 83.6 | |
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| ViT_B_16_Weights.IMAGENET1K_V1 | 86.6 | |
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| ViT_B_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 86.6 | |
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| ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 86.9 | |
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| Swin_B_Weights.IMAGENET1K_V1 | 87.8 | |
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| Swin_V2_B_Weights.IMAGENET1K_V1 | 87.9 | |
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| ViT_B_32_Weights.IMAGENET1K_V1 | 88.2 | |
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| ConvNeXt_Base_Weights.IMAGENET1K_V1 | 88.6 | |
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| ResNeXt101_32X8D_Weights.IMAGENET1K_V1 | 88.8 | |
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| ResNeXt101_32X8D_Weights.IMAGENET1K_V2 | 88.8 | |
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| RegNet_X_32GF_Weights.IMAGENET1K_V1 | 107.8 | |
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| RegNet_X_32GF_Weights.IMAGENET1K_V2 | 107.8 | |
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| EfficientNet_V2_L_Weights.IMAGENET1K_V1 | 118.5 | |
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| Wide_ResNet101_2_Weights.IMAGENET1K_V1 | 126.9 | |
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| Wide_ResNet101_2_Weights.IMAGENET1K_V2 | 126.9 | |
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| VGG11_BN_Weights.IMAGENET1K_V1 | 132.9 | |
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| VGG11_Weights.IMAGENET1K_V1 | 132.9 | |
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| VGG13_Weights.IMAGENET1K_V1 | 133 | |
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| VGG13_BN_Weights.IMAGENET1K_V1 | 133.1 | |
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| VGG16_BN_Weights.IMAGENET1K_V1 | 138.4 | |
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| VGG16_Weights.IMAGENET1K_V1 | 138.4 | |
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| VGG16_Weights.IMAGENET1K_FEATURES | 138.4 | |
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| VGG19_BN_Weights.IMAGENET1K_V1 | 143.7 | |
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| VGG19_Weights.IMAGENET1K_V1 | 143.7 | |
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| RegNet_Y_32GF_Weights.IMAGENET1K_V1 | 145 | |
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| RegNet_Y_32GF_Weights.IMAGENET1K_V2 | 145 | |
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| RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 145 | |
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| RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 145 | |
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| ConvNeXt_Large_Weights.IMAGENET1K_V1 | 197.8 | |
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| ViT_L_16_Weights.IMAGENET1K_V1 | 304.3 | |
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| ViT_L_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 304.3 | |
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| ViT_L_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 305.2 | |
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| ViT_L_32_Weights.IMAGENET1K_V1 | 306.5 | |
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| ViT_H_14_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 632 | |
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| ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 | 633.5 | |
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| RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 644.8 | |
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| RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 644.8 | |
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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> |
