license: mit
task_categories:
- image-classification
- feature-extraction
language:
- en
tags:
- code
pretty_name: Vi-Backbones
size_categories:
- n<1K
viewer: false
Dataset Card for "monet-joe/cv_backbones"
Viewer
https://huggingface.co/spaces/monet-joe/cv-backbones
Maintenance
git clone [email protected]:datasets/monet-joe/cv_backbones
Usage
from datasets import load_dataset
backbones = load_dataset("monet-joe/cv_backbones")
for weights in backbones["IMAGENET1K_V1"]:
print(weights)
for weights in backbones["IMAGENET1K_V2"]:
print(weights)
Param count
| Backbone | Params(M) | | :--: | :--: | | SqueezeNet1_0_Weights.IMAGENET1K_V1 | 1.2 | | SqueezeNet1_1_Weights.IMAGENET1K_V1 | 1.2 | | ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1 | 1.4 | | MNASNet0_5_Weights.IMAGENET1K_V1 | 2.2 | | ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1 | 2.3 | | MobileNet_V3_Small_Weights.IMAGENET1K_V1 | 2.5 | | MNASNet0_75_Weights.IMAGENET1K_V1 | 3.2 | | MobileNet_V2_Weights.IMAGENET1K_V1 | 3.5 | | MobileNet_V2_Weights.IMAGENET1K_V2 | 3.5 | | ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1 | 3.5 | | RegNet_Y_400MF_Weights.IMAGENET1K_V1 | 4.3 | | RegNet_Y_400MF_Weights.IMAGENET1K_V2 | 4.3 | | MNASNet1_0_Weights.IMAGENET1K_V1 | 4.4 | | EfficientNet_B0_Weights.IMAGENET1K_V1 | 5.3 | | MobileNet_V3_Large_Weights.IMAGENET1K_V1 | 5.5 | | MobileNet_V3_Large_Weights.IMAGENET1K_V2 | 5.5 | | RegNet_X_400MF_Weights.IMAGENET1K_V1 | 5.5 | | RegNet_X_400MF_Weights.IMAGENET1K_V2 | 5.5 | | MNASNet1_3_Weights.IMAGENET1K_V1 | 6.3 | | RegNet_Y_800MF_Weights.IMAGENET1K_V1 | 6.4 | | RegNet_Y_800MF_Weights.IMAGENET1K_V2 | 6.4 | | GoogLeNet_Weights.IMAGENET1K_V1 | 6.6 | | RegNet_X_800MF_Weights.IMAGENET1K_V1 | 7.3 | | RegNet_X_800MF_Weights.IMAGENET1K_V2 | 7.3 | | ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1 | 7.4 | | EfficientNet_B1_Weights.IMAGENET1K_V1 | 7.8 | | EfficientNet_B1_Weights.IMAGENET1K_V2 | 7.8 | | DenseNet121_Weights.IMAGENET1K_V1 | 8 | | EfficientNet_B2_Weights.IMAGENET1K_V1 | 9.1 | | RegNet_X_1_6GF_Weights.IMAGENET1K_V1 | 9.2 | | RegNet_X_1_6GF_Weights.IMAGENET1K_V2 | 9.2 | | RegNet_Y_1_6GF_Weights.IMAGENET1K_V1 | 11.2 | | RegNet_Y_1_6GF_Weights.IMAGENET1K_V2 | 11.2 | | ResNet18_Weights.IMAGENET1K_V1 | 11.7 | | EfficientNet_B3_Weights.IMAGENET1K_V1 | 12.2 | | DenseNet169_Weights.IMAGENET1K_V1 | 14.1 | | RegNet_X_3_2GF_Weights.IMAGENET1K_V1 | 15.3 | | RegNet_X_3_2GF_Weights.IMAGENET1K_V2 | 15.3 | | EfficientNet_B4_Weights.IMAGENET1K_V1 | 19.3 | | RegNet_Y_3_2GF_Weights.IMAGENET1K_V1 | 19.4 | | RegNet_Y_3_2GF_Weights.IMAGENET1K_V2 | 19.4 | | DenseNet201_Weights.IMAGENET1K_V1 | 20 | | EfficientNet_V2_S_Weights.IMAGENET1K_V1 | 21.5 | | ResNet34_Weights.IMAGENET1K_V1 | 21.8 | | ResNeXt50_32X4D_Weights.IMAGENET1K_V1 | 25 | | ResNeXt50_32X4D_Weights.IMAGENET1K_V2 | 25 | | ResNet50_Weights.IMAGENET1K_V1 | 25.6 | | ResNet50_Weights.IMAGENET1K_V2 | 25.6 | | Inception_V3_Weights.IMAGENET1K_V1 | 27.2 | | Swin_T_Weights.IMAGENET1K_V1 | 28.3 | | Swin_V2_T_Weights.IMAGENET1K_V1 | 28.4 | | ConvNeXt_Tiny_Weights.IMAGENET1K_V1 | 28.6 | | DenseNet161_Weights.IMAGENET1K_V1 | 28.7 | | EfficientNet_B5_Weights.IMAGENET1K_V1 | 30.4 | | MaxVit_T_Weights.IMAGENET1K_V1 | 30.9 | | RegNet_Y_8GF_Weights.IMAGENET1K_V1 | 39.4 | | RegNet_Y_8GF_Weights.IMAGENET1K_V2 | 39.4 | | RegNet_X_8GF_Weights.IMAGENET1K_V1 | 39.6 | | RegNet_X_8GF_Weights.IMAGENET1K_V2 | 39.6 | | EfficientNet_B6_Weights.IMAGENET1K_V1 | 43 | | ResNet101_Weights.IMAGENET1K_V1 | 44.5 | | ResNet101_Weights.IMAGENET1K_V2 | 44.5 | | Swin_S_Weights.IMAGENET1K_V1 | 49.6 | | Swin_V2_S_Weights.IMAGENET1K_V1 | 49.7 | | ConvNeXt_Small_Weights.IMAGENET1K_V1 | 50.2 | | EfficientNet_V2_M_Weights.IMAGENET1K_V1 | 54.1 | | RegNet_X_16GF_Weights.IMAGENET1K_V1 | 54.3 | | RegNet_X_16GF_Weights.IMAGENET1K_V2 | 54.3 | | ResNet152_Weights.IMAGENET1K_V1 | 60.2 | | ResNet152_Weights.IMAGENET1K_V2 | 60.2 | | AlexNet_Weights.IMAGENET1K_V1 | 61.1 | | EfficientNet_B7_Weights.IMAGENET1K_V1 | 66.3 | | Wide_ResNet50_2_Weights.IMAGENET1K_V1 | 68.9 | | Wide_ResNet50_2_Weights.IMAGENET1K_V2 | 68.9 | | ResNeXt101_64X4D_Weights.IMAGENET1K_V1 | 83.5 | | RegNet_Y_16GF_Weights.IMAGENET1K_V1 | 83.6 | | RegNet_Y_16GF_Weights.IMAGENET1K_V2 | 83.6 | | RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 83.6 | | RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 83.6 | | ViT_B_16_Weights.IMAGENET1K_V1 | 86.6 | | ViT_B_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 86.6 | | ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 86.9 | | Swin_B_Weights.IMAGENET1K_V1 | 87.8 | | Swin_V2_B_Weights.IMAGENET1K_V1 | 87.9 | | ViT_B_32_Weights.IMAGENET1K_V1 | 88.2 | | ConvNeXt_Base_Weights.IMAGENET1K_V1 | 88.6 | | ResNeXt101_32X8D_Weights.IMAGENET1K_V1 | 88.8 | | ResNeXt101_32X8D_Weights.IMAGENET1K_V2 | 88.8 | | RegNet_X_32GF_Weights.IMAGENET1K_V1 | 107.8 | | RegNet_X_32GF_Weights.IMAGENET1K_V2 | 107.8 | | EfficientNet_V2_L_Weights.IMAGENET1K_V1 | 118.5 | | Wide_ResNet101_2_Weights.IMAGENET1K_V1 | 126.9 | | Wide_ResNet101_2_Weights.IMAGENET1K_V2 | 126.9 | | VGG11_BN_Weights.IMAGENET1K_V1 | 132.9 | | VGG11_Weights.IMAGENET1K_V1 | 132.9 | | VGG13_Weights.IMAGENET1K_V1 | 133 | | VGG13_BN_Weights.IMAGENET1K_V1 | 133.1 | | VGG16_BN_Weights.IMAGENET1K_V1 | 138.4 | | VGG16_Weights.IMAGENET1K_V1 | 138.4 | | VGG16_Weights.IMAGENET1K_FEATURES | 138.4 | | VGG19_BN_Weights.IMAGENET1K_V1 | 143.7 | | VGG19_Weights.IMAGENET1K_V1 | 143.7 | | RegNet_Y_32GF_Weights.IMAGENET1K_V1 | 145 | | RegNet_Y_32GF_Weights.IMAGENET1K_V2 | 145 | | RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 145 | | RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 145 | | ConvNeXt_Large_Weights.IMAGENET1K_V1 | 197.8 | | ViT_L_16_Weights.IMAGENET1K_V1 | 304.3 | | ViT_L_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 304.3 | | ViT_L_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 305.2 | | ViT_L_32_Weights.IMAGENET1K_V1 | 306.5 | | ViT_H_14_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 632 | | ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 | 633.5 | | RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 644.8 | | RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 644.8 |
Mirror
https://www.modelscope.cn/datasets/monetjoe/cv_backbones