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