monet-joe/cv_backbones

---
license: mit
task_categories:
- image-classification
- feature-extraction
language:
- en
tags:
- code
pretty_name: Vi-Backbones
size_categories:
- n<1K
---

# Dataset Card for "monet-joe/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.

## Viewer
<https://huggingface.co/spaces/monet-joe/cv-backbones>
### Data Fields
| ver | type | input_size | url |
| :-----------: | :-----------: | :--------------: | :-------------------------------: |
| backbone name | backbone type | input image size | url of pretrained model .pth file |

### Splits
| subsets |
| :--: |
| IMAGENET1K_V1 |
| IMAGENET1K_V2 |

## Maintenance
```bash
git clone [e-mail]:datasets/monet-joe/cv_backbones
```

## Usage
```python
from datasets import load_dataset

backbones = load_dataset("monet-joe/cv_backbones")

for weights in backbones["IMAGENET1K_V1"]:
print(weights)

for weights in backbones["IMAGENET1K_V2"]:
print(weights)
```

## Param count
### 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>

原始信息汇总

数据集概述

数据集名称

• 名称: monet-joe/cv_backbones
• 别名: Vi-Backbones

数据集描述

• 内容: 该数据集整合了PyTorch官方网站上可用的预训练计算机视觉模型骨干网络。主要包含在ImageNet1K数据集上预训练的各种卷积神经网络（CNNs）和视觉Transformer模型。
• 用途: 提供用户在图像识别、对象检测和图像分割等任务中进行迁移学习的强大基础。
• 结构: 整个集合分为两个子集，V1和V2，涵盖了多种经典和先进的视觉模型。

数据集属性

• 许可证: MIT
• 任务类别:
  • 图像分类
  • 特征提取
• 语言: 英语
• 标签: 代码
• 大小类别: n<1K
• 查看器: 否

数据集详细信息

• 数据字段:

  ver	type	input_size	url
  backbone name	backbone type	input image size	预训练模型.pth文件的URL

• 分割:

  子集
  IMAGENET1K_V1
  IMAGENET1K_V2

参数计数

• 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