README.md

---
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 |

## 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)
```

## Maintenance
```bash
git clone git@hf.co:datasets/monetjoe/cv_backbones
cd cv_backbones
```

### Update tool
<https://huggingface.co/spaces/monetjoe/cv_backbones>

## 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>

## References
 - <https://pytorch.org/vision/main/_modules>
 - <https://pytorch.org/vision/main/models.html>