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---
license: mit
task_categories:
- image-classification
- feature-extraction
language:
- en
tags:
- code
pretty_name: Vi-Backbones
size_categories:
- n<1K
---
# 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
```bash
git clone git@hf.co:datasets/monetjoe/cv_backbones
cd cv_backbones
```
## Usage
### ImageNet V1
```python
from datasets import load_dataset
backbones = load_dataset("monetjoe/cv_backbones", name="default", split="train")
for weights in backbones:
print(weights)
```
### ImageNet V2
```python
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><br>
[2] <https://pytorch.org/vision/main/models.html> |