ver
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5
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15 values
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224
600
url
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alexnet
224
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convnext_tiny
convnext
224
https://download.pytorch.org/models/convnext_tiny-983f1562.pth
convnext_small
convnext
224
https://download.pytorch.org/models/convnext_small-0c510722.pth
convnext_base
convnext
224
https://download.pytorch.org/models/convnext_base-6075fbad.pth
convnext_large
convnext
224
https://download.pytorch.org/models/convnext_large-ea097f82.pth
densenet121
densenet
224
https://download.pytorch.org/models/densenet121-a639ec97.pth
densenet161
densenet
224
https://download.pytorch.org/models/densenet161-8d451a50.pth
densenet169
densenet
224
https://download.pytorch.org/models/densenet169-b2777c0a.pth
densenet201
densenet
224
https://download.pytorch.org/models/densenet201-c1103571.pth
efficientnet_b0
efficientnet
224
https://download.pytorch.org/models/efficientnet_b0_rwightman-7f5810bc.pth
efficientnet_b1
efficientnet
240
https://download.pytorch.org/models/efficientnet_b1_rwightman-bac287d4.pth
efficientnet_b2
efficientnet
288
https://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pth
efficientnet_b3
efficientnet
300
https://download.pytorch.org/models/efficientnet_b3_rwightman-b3899882.pth
efficientnet_b4
efficientnet
380
https://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pth
efficientnet_b5
efficientnet
456
https://download.pytorch.org/models/efficientnet_b5_lukemelas-1a07897c.pth
efficientnet_b6
efficientnet
528
https://download.pytorch.org/models/efficientnet_b6_lukemelas-24a108a5.pth
efficientnet_b7
efficientnet
600
https://download.pytorch.org/models/efficientnet_b7_lukemelas-c5b4e57e.pth
efficientnet_v2_s
efficientnet
384
https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth
efficientnet_v2_m
efficientnet
480
https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth
efficientnet_v2_l
efficientnet
480
https://download.pytorch.org/models/efficientnet_v2_l-59c71312.pth
googlenet
googlenet
224
https://download.pytorch.org/models/googlenet-1378be20.pth
inception_v3
googlenet
299
https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth
maxvit_t
maxvit
224
https://download.pytorch.org/models/maxvit_t-bc5ab103.pth
mnasnet0_5
mnasnet
224
https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth
mnasnet0_75
mnasnet
224
https://download.pytorch.org/models/mnasnet0_75-7090bc5f.pth
mnasnet1_0
mnasnet
224
https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth
mnasnet1_3
mnasnet
224
https://download.pytorch.org/models/mnasnet1_3-a4c69d6f.pth
mobilenet_v2
mobilenet
224
https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
mobilenet_v3_large
mobilenet
224
https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth
mobilenet_v3_small
mobilenet
224
https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth
regnet_y_400mf
regnet
224
https://download.pytorch.org/models/regnet_y_400mf-c65dace8.pth
regnet_y_800mf
regnet
224
https://download.pytorch.org/models/regnet_y_800mf-1b27b58c.pth
regnet_y_1_6gf
regnet
224
https://download.pytorch.org/models/regnet_y_1_6gf-b11a554e.pth
regnet_y_3_2gf
regnet
224
https://download.pytorch.org/models/regnet_y_3_2gf-b5a9779c.pth
regnet_y_8gf
regnet
224
https://download.pytorch.org/models/regnet_y_8gf-d0d0e4a8.pth
regnet_y_16gf
regnet
224
https://download.pytorch.org/models/regnet_y_16gf-9e6ed7dd.pth
regnet_y_32gf
regnet
224
https://download.pytorch.org/models/regnet_y_32gf-4dee3f7a.pth
regnet_x_400mf
regnet
224
https://download.pytorch.org/models/regnet_x_400mf-adf1edd5.pth
regnet_x_800mf
regnet
224
https://download.pytorch.org/models/regnet_x_800mf-ad17e45c.pth
regnet_x_1_6gf
regnet
224
https://download.pytorch.org/models/regnet_x_1_6gf-e3633e7f.pth
regnet_x_3_2gf
regnet
224
https://download.pytorch.org/models/regnet_x_3_2gf-f342aeae.pth
regnet_x_8gf
regnet
224
https://download.pytorch.org/models/regnet_x_8gf-03ceed89.pth
regnet_x_16gf
regnet
224
https://download.pytorch.org/models/regnet_x_16gf-2007eb11.pth
regnet_x_32gf
regnet
224
https://download.pytorch.org/models/regnet_x_32gf-9d47f8d0.pth
resnet18
resnet
224
https://download.pytorch.org/models/resnet18-f37072fd.pth
resnet34
resnet
224
https://download.pytorch.org/models/resnet34-b627a593.pth
resnet50
resnet
224
https://download.pytorch.org/models/resnet50-0676ba61.pth
resnet101
resnet
224
https://download.pytorch.org/models/resnet101-63fe2227.pth
resnet152
resnet
224
https://download.pytorch.org/models/resnet152-394f9c45.pth
resnext50_32x4d
resnet
224
https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
resnext101_32x8d
resnet
224
https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth
resnext101_64x4d
resnet
224
https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth
wide_resnet50_2
resnet
224
https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth
wide_resnet101_2
resnet
224
https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth
shufflenet_v2_x0_5
shufflenet
224
https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth
shufflenet_v2_x1_0
shufflenet
224
https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth
shufflenet_v2_x1_5
shufflenet
224
https://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pth
shufflenet_v2_x2_0
shufflenet
224
https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth
squeezenet1_0
squeezenet
224
https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth
squeezenet1_1
squeezenet
224
https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth
swin_t
swin_transformer
224
https://download.pytorch.org/models/swin_t-704ceda3.pth
swin_s
swin_transformer
224
https://download.pytorch.org/models/swin_s-5e29d889.pth
swin_b
swin_transformer
224
https://download.pytorch.org/models/swin_b-68c6b09e.pth
swin_v2_t
swin_transformer
256
https://download.pytorch.org/models/swin_v2_t-b137f0e2.pth
swin_v2_s
swin_transformer
256
https://download.pytorch.org/models/swin_v2_s-637d8ceb.pth
swin_v2_b
swin_transformer
256
https://download.pytorch.org/models/swin_v2_b-781e5279.pth
vgg11
vgg
224
https://download.pytorch.org/models/vgg11-8a719046.pth
vgg11_bn
vgg
224
https://download.pytorch.org/models/vgg11_bn-6002323d.pth
vgg13
vgg
224
https://download.pytorch.org/models/vgg13-19584684.pth
vgg13_bn
vgg
224
https://download.pytorch.org/models/vgg13_bn-abd245e5.pth
vgg16
vgg
224
https://download.pytorch.org/models/vgg16-397923af.pth
vgg16_bn
vgg
224
https://download.pytorch.org/models/vgg16_bn-6c64b313.pth
vgg19
vgg
224
https://download.pytorch.org/models/vgg19-dcbb9e9d.pth
vgg19_bn
vgg
224
https://download.pytorch.org/models/vgg19_bn-c79401a0.pth
vit_b_16
vit
224
https://download.pytorch.org/models/vit_b_16-c867db91.pth
vit_b_32
vit
224
https://download.pytorch.org/models/vit_b_32-d86f8d99.pth
vit_l_16
vit
224
https://download.pytorch.org/models/vit_l_16-852ce7e3.pth
vit_l_32
vit
224
https://download.pytorch.org/models/vit_l_32-c7638314.pth

Dataset Card for "monetjoe/cv_backbones"

This repository consolidates the collection of backbone networks for pre-trained computer vision models available on the PyTorch official website. It mainly includes various Convolutional Neural Networks (CNNs) and Vision Transformer models pre-trained on the ImageNet1K dataset. The entire collection is divided into two subsets, V1 and V2, encompassing multiple classic and advanced versions of visual models. These pre-trained backbone networks provide users with a robust foundation for transfer learning in tasks such as image recognition, object detection, and image segmentation. Simultaneously, it offers a convenient choice for researchers and practitioners to flexibly apply these pre-trained models in different scenarios.

Data structure

ver type input_size url
backbone name backbone type input image size url of pretrained model .pth file

Maintenance

git clone git@hf.co:datasets/monetjoe/cv_backbones
cd cv_backbones

Usage

ImageNet V1

from datasets import load_dataset

backbones = load_dataset("monetjoe/cv_backbones", name="default", split="train")
for weights in backbones:
    print(weights)

ImageNet V2

from datasets import load_dataset

backbones = load_dataset("monetjoe/cv_backbones", name="default", split="test")
for weights in backbones:
    print(weights)

Param counts of different backbones

IMAGENET1K_V1

Backbone Params(M)
SqueezeNet1_0 1.2
SqueezeNet1_1 1.2
ShuffleNet_V2_X0_5 1.4
MNASNet0_5 2.2
ShuffleNet_V2_X1_0 2.3
MobileNet_V3_Small 2.5
MNASNet0_75 3.2
MobileNet_V2 3.5
ShuffleNet_V2_X1_5 3.5
RegNet_Y_400MF 4.3
MNASNet1_0 4.4
EfficientNet_B0 5.3
MobileNet_V3_Large 5.5
RegNet_X_400MF 5.5
MNASNet1_3 6.3
RegNet_Y_800MF 6.4
GoogLeNet 6.6
RegNet_X_800MF 7.3
ShuffleNet_V2_X2_0 7.4
EfficientNet_B1 7.8
DenseNet121 8
EfficientNet_B2 9.1
RegNet_X_1_6GF 9.2
RegNet_Y_1_6GF 11.2
ResNet18 11.7
EfficientNet_B3 12.2
DenseNet169 14.1
RegNet_X_3_2GF 15.3
EfficientNet_B4 19.3
RegNet_Y_3_2GF 19.4
DenseNet201 20
EfficientNet_V2_S 21.5
ResNet34 21.8
ResNeXt50_32X4D 25
ResNet50 25.6
Inception_V3 27.2
Swin_T 28.3
Swin_V2_T 28.4
ConvNeXt_Tiny 28.6
DenseNet161 28.7
EfficientNet_B5 30.4
MaxVit_T 30.9
RegNet_Y_8GF 39.4
RegNet_X_8GF 39.6
EfficientNet_B6 43
ResNet101 44.5
Swin_S 49.6
Swin_V2_S 49.7
ConvNeXt_Small 50.2
EfficientNet_V2_M 54.1
RegNet_X_16GF 54.3
ResNet152 60.2
AlexNet 61.1
EfficientNet_B7 66.3
Wide_ResNet50_2 68.9
ResNeXt101_64X4D 83.5
RegNet_Y_16GF 83.6
ViT_B_16 86.6
Swin_B 87.8
Swin_V2_B 87.9
ViT_B_32 88.2
ConvNeXt_Base 88.6
ResNeXt101_32X8D 88.8
RegNet_X_32GF 107.8
EfficientNet_V2_L 118.5
Wide_ResNet101_2 126.9
VGG11_BN 132.9
VGG11 132.9
VGG13 133
VGG13_BN 133.1
VGG16_BN 138.4
VGG16 138.4
VGG19_BN 143.7
VGG19 143.7
RegNet_Y_32GF 145
ConvNeXt_Large 197.8
ViT_L_16 304.3
ViT_L_32 306.5

IMAGENET1K_V2

Backbone Params(M)
MobileNet_V2 3.5
RegNet_Y_400MF 4.3
MobileNet_V3_Large 5.5
RegNet_X_400MF 5.5
RegNet_Y_800MF 6.4
RegNet_X_800MF 7.3
EfficientNet_B1 7.8
RegNet_X_1_6GF 9.2
RegNet_Y_1_6GF 11.2
RegNet_X_3_2GF 15.3
RegNet_Y_3_2GF 19.4
ResNeXt50_32X4D 25
ResNet50 25.6
RegNet_Y_8GF 39.4
RegNet_X_8GF 39.6
ResNet101 44.5
RegNet_X_16GF 54.3
ResNet152 60.2
Wide_ResNet50_2 68.9
RegNet_Y_16GF 83.6
ResNeXt101_32X8D 88.8
RegNet_X_32GF 107.8
Wide_ResNet101_2 126.9
RegNet_Y_32GF 145

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

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