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Datasets:
monetjoe
/
cv_backbones

Tasks:
Image Classification
Feature Extraction
Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
code
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
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metadata
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

Reference

[1] https://pytorch.org/vision/main/_modules
[2] https://pytorch.org/vision/main/models.html