File size: 6,102 Bytes
17fbb28 c51087c c880bb4 c51087c 70e3f22 c51087c dff7a72 17fbb28 18bce8b a039060 e09029c 18bce8b e09029c 18bce8b 9aab7d4 18bce8b 9aab7d4 52bbeff b3666fc 9aab7d4 52bbeff a60b344 ce78729 5782f7d e09029c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
---
license: mit
task_categories:
- image-classification
- feature-extraction
language:
- en
tags:
- code
pretty_name: Vi-Backbones
size_categories:
- n<1K
viewer: false
---
# Dataset Card for "monet-joe/cv_backbones"
## Viewer
<https://huggingface.co/spaces/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
| Backbone | Params(M) |
| :--: | :--: |
| SqueezeNet1_0_Weights.IMAGENET1K_V1 | 1.2 |
| SqueezeNet1_1_Weights.IMAGENET1K_V1 | 1.2 |
| ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1 | 1.4 |
| MNASNet0_5_Weights.IMAGENET1K_V1 | 2.2 |
| ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1 | 2.3 |
| MobileNet_V3_Small_Weights.IMAGENET1K_V1 | 2.5 |
| MNASNet0_75_Weights.IMAGENET1K_V1 | 3.2 |
| MobileNet_V2_Weights.IMAGENET1K_V1 | 3.5 |
| MobileNet_V2_Weights.IMAGENET1K_V2 | 3.5 |
| ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1 | 3.5 |
| RegNet_Y_400MF_Weights.IMAGENET1K_V1 | 4.3 |
| RegNet_Y_400MF_Weights.IMAGENET1K_V2 | 4.3 |
| MNASNet1_0_Weights.IMAGENET1K_V1 | 4.4 |
| EfficientNet_B0_Weights.IMAGENET1K_V1 | 5.3 |
| MobileNet_V3_Large_Weights.IMAGENET1K_V1 | 5.5 |
| MobileNet_V3_Large_Weights.IMAGENET1K_V2 | 5.5 |
| RegNet_X_400MF_Weights.IMAGENET1K_V1 | 5.5 |
| RegNet_X_400MF_Weights.IMAGENET1K_V2 | 5.5 |
| MNASNet1_3_Weights.IMAGENET1K_V1 | 6.3 |
| RegNet_Y_800MF_Weights.IMAGENET1K_V1 | 6.4 |
| RegNet_Y_800MF_Weights.IMAGENET1K_V2 | 6.4 |
| GoogLeNet_Weights.IMAGENET1K_V1 | 6.6 |
| RegNet_X_800MF_Weights.IMAGENET1K_V1 | 7.3 |
| RegNet_X_800MF_Weights.IMAGENET1K_V2 | 7.3 |
| ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1 | 7.4 |
| EfficientNet_B1_Weights.IMAGENET1K_V1 | 7.8 |
| EfficientNet_B1_Weights.IMAGENET1K_V2 | 7.8 |
| DenseNet121_Weights.IMAGENET1K_V1 | 8 |
| EfficientNet_B2_Weights.IMAGENET1K_V1 | 9.1 |
| RegNet_X_1_6GF_Weights.IMAGENET1K_V1 | 9.2 |
| RegNet_X_1_6GF_Weights.IMAGENET1K_V2 | 9.2 |
| RegNet_Y_1_6GF_Weights.IMAGENET1K_V1 | 11.2 |
| RegNet_Y_1_6GF_Weights.IMAGENET1K_V2 | 11.2 |
| ResNet18_Weights.IMAGENET1K_V1 | 11.7 |
| EfficientNet_B3_Weights.IMAGENET1K_V1 | 12.2 |
| DenseNet169_Weights.IMAGENET1K_V1 | 14.1 |
| RegNet_X_3_2GF_Weights.IMAGENET1K_V1 | 15.3 |
| RegNet_X_3_2GF_Weights.IMAGENET1K_V2 | 15.3 |
| EfficientNet_B4_Weights.IMAGENET1K_V1 | 19.3 |
| RegNet_Y_3_2GF_Weights.IMAGENET1K_V1 | 19.4 |
| RegNet_Y_3_2GF_Weights.IMAGENET1K_V2 | 19.4 |
| DenseNet201_Weights.IMAGENET1K_V1 | 20 |
| EfficientNet_V2_S_Weights.IMAGENET1K_V1 | 21.5 |
| ResNet34_Weights.IMAGENET1K_V1 | 21.8 |
| ResNeXt50_32X4D_Weights.IMAGENET1K_V1 | 25 |
| ResNeXt50_32X4D_Weights.IMAGENET1K_V2 | 25 |
| ResNet50_Weights.IMAGENET1K_V1 | 25.6 |
| ResNet50_Weights.IMAGENET1K_V2 | 25.6 |
| Inception_V3_Weights.IMAGENET1K_V1 | 27.2 |
| Swin_T_Weights.IMAGENET1K_V1 | 28.3 |
| Swin_V2_T_Weights.IMAGENET1K_V1 | 28.4 |
| ConvNeXt_Tiny_Weights.IMAGENET1K_V1 | 28.6 |
| DenseNet161_Weights.IMAGENET1K_V1 | 28.7 |
| EfficientNet_B5_Weights.IMAGENET1K_V1 | 30.4 |
| MaxVit_T_Weights.IMAGENET1K_V1 | 30.9 |
| RegNet_Y_8GF_Weights.IMAGENET1K_V1 | 39.4 |
| RegNet_Y_8GF_Weights.IMAGENET1K_V2 | 39.4 |
| RegNet_X_8GF_Weights.IMAGENET1K_V1 | 39.6 |
| RegNet_X_8GF_Weights.IMAGENET1K_V2 | 39.6 |
| EfficientNet_B6_Weights.IMAGENET1K_V1 | 43 |
| ResNet101_Weights.IMAGENET1K_V1 | 44.5 |
| ResNet101_Weights.IMAGENET1K_V2 | 44.5 |
| Swin_S_Weights.IMAGENET1K_V1 | 49.6 |
| Swin_V2_S_Weights.IMAGENET1K_V1 | 49.7 |
| ConvNeXt_Small_Weights.IMAGENET1K_V1 | 50.2 |
| EfficientNet_V2_M_Weights.IMAGENET1K_V1 | 54.1 |
| RegNet_X_16GF_Weights.IMAGENET1K_V1 | 54.3 |
| RegNet_X_16GF_Weights.IMAGENET1K_V2 | 54.3 |
| ResNet152_Weights.IMAGENET1K_V1 | 60.2 |
| ResNet152_Weights.IMAGENET1K_V2 | 60.2 |
| AlexNet_Weights.IMAGENET1K_V1 | 61.1 |
| EfficientNet_B7_Weights.IMAGENET1K_V1 | 66.3 |
| Wide_ResNet50_2_Weights.IMAGENET1K_V1 | 68.9 |
| Wide_ResNet50_2_Weights.IMAGENET1K_V2 | 68.9 |
| ResNeXt101_64X4D_Weights.IMAGENET1K_V1 | 83.5 |
| RegNet_Y_16GF_Weights.IMAGENET1K_V1 | 83.6 |
| RegNet_Y_16GF_Weights.IMAGENET1K_V2 | 83.6 |
| RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 83.6 |
| RegNet_Y_16GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 83.6 |
| ViT_B_16_Weights.IMAGENET1K_V1 | 86.6 |
| ViT_B_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 86.6 |
| ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 86.9 |
| Swin_B_Weights.IMAGENET1K_V1 | 87.8 |
| Swin_V2_B_Weights.IMAGENET1K_V1 | 87.9 |
| ViT_B_32_Weights.IMAGENET1K_V1 | 88.2 |
| ConvNeXt_Base_Weights.IMAGENET1K_V1 | 88.6 |
| ResNeXt101_32X8D_Weights.IMAGENET1K_V1 | 88.8 |
| ResNeXt101_32X8D_Weights.IMAGENET1K_V2 | 88.8 |
| RegNet_X_32GF_Weights.IMAGENET1K_V1 | 107.8 |
| RegNet_X_32GF_Weights.IMAGENET1K_V2 | 107.8 |
| EfficientNet_V2_L_Weights.IMAGENET1K_V1 | 118.5 |
| Wide_ResNet101_2_Weights.IMAGENET1K_V1 | 126.9 |
| Wide_ResNet101_2_Weights.IMAGENET1K_V2 | 126.9 |
| VGG11_BN_Weights.IMAGENET1K_V1 | 132.9 |
| VGG11_Weights.IMAGENET1K_V1 | 132.9 |
| VGG13_Weights.IMAGENET1K_V1 | 133 |
| VGG13_BN_Weights.IMAGENET1K_V1 | 133.1 |
| VGG16_BN_Weights.IMAGENET1K_V1 | 138.4 |
| VGG16_Weights.IMAGENET1K_V1 | 138.4 |
| VGG16_Weights.IMAGENET1K_FEATURES | 138.4 |
| VGG19_BN_Weights.IMAGENET1K_V1 | 143.7 |
| VGG19_Weights.IMAGENET1K_V1 | 143.7 |
| RegNet_Y_32GF_Weights.IMAGENET1K_V1 | 145 |
| RegNet_Y_32GF_Weights.IMAGENET1K_V2 | 145 |
| RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 145 |
| RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 145 |
| ConvNeXt_Large_Weights.IMAGENET1K_V1 | 197.8 |
| ViT_L_16_Weights.IMAGENET1K_V1 | 304.3 |
| ViT_L_16_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 304.3 |
| ViT_L_16_Weights.IMAGENET1K_SWAG_E2E_V1 | 305.2 |
| ViT_L_32_Weights.IMAGENET1K_V1 | 306.5 |
| ViT_H_14_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 632 |
| ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 | 633.5 |
| RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_E2E_V1 | 644.8 |
| RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_LINEAR_V1 | 644.8 |
## Mirror
<https://www.modelscope.cn/datasets/monetjoe/cv_backbones>
## Reference
<https://pytorch.org/vision/main/_modules> |