ver
stringlengths 5
18
| type
stringclasses 15
values | input_size
int64 224
600
| url
stringlengths 54
74
|
---|---|---|---|
alexnet | alexnet | 224 | https://download.pytorch.org/models/alexnet-owt-7be5be79.pth |
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 [email protected]: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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