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--- |
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license: mit |
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task_categories: |
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- image-classification |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- code |
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pretty_name: Vi-Backbones |
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size_categories: |
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- n<1K |
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--- |
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# Dataset Card for "monetjoe/cv_backbones" |
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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. |
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## Data structure |
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| ver | type | input_size | url | |
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| :-----------: | :-----------: | :--------------: | :-------------------------------: | |
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| backbone name | backbone type | input image size | url of pretrained model .pth file | |
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## Maintenance |
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```bash |
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git clone git@hf.co:datasets/monetjoe/cv_backbones |
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cd cv_backbones |
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``` |
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## Usage |
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### ImageNet V1 |
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```python |
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from datasets import load_dataset |
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backbones = load_dataset("monetjoe/cv_backbones", name="default", split="train") |
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for weights in backbones: |
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print(weights) |
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``` |
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### ImageNet V2 |
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```python |
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from datasets import load_dataset |
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backbones = load_dataset("monetjoe/cv_backbones", name="default", split="test") |
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for weights in backbones: |
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print(weights) |
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``` |
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## Param counts of different backbones |
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### IMAGENET1K_V1 |
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| Backbone | Params(M) | |
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| :----------------: | :-------: | |
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| SqueezeNet1_0 | 1.2 | |
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| SqueezeNet1_1 | 1.2 | |
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| ShuffleNet_V2_X0_5 | 1.4 | |
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| MNASNet0_5 | 2.2 | |
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| ShuffleNet_V2_X1_0 | 2.3 | |
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| MobileNet_V3_Small | 2.5 | |
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| MNASNet0_75 | 3.2 | |
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| MobileNet_V2 | 3.5 | |
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| ShuffleNet_V2_X1_5 | 3.5 | |
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| RegNet_Y_400MF | 4.3 | |
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| MNASNet1_0 | 4.4 | |
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| EfficientNet_B0 | 5.3 | |
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| MobileNet_V3_Large | 5.5 | |
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| RegNet_X_400MF | 5.5 | |
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| MNASNet1_3 | 6.3 | |
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| RegNet_Y_800MF | 6.4 | |
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| GoogLeNet | 6.6 | |
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| RegNet_X_800MF | 7.3 | |
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| ShuffleNet_V2_X2_0 | 7.4 | |
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| EfficientNet_B1 | 7.8 | |
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| DenseNet121 | 8 | |
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| EfficientNet_B2 | 9.1 | |
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| RegNet_X_1_6GF | 9.2 | |
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| RegNet_Y_1_6GF | 11.2 | |
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| ResNet18 | 11.7 | |
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| EfficientNet_B3 | 12.2 | |
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| DenseNet169 | 14.1 | |
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| RegNet_X_3_2GF | 15.3 | |
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| EfficientNet_B4 | 19.3 | |
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| RegNet_Y_3_2GF | 19.4 | |
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| DenseNet201 | 20 | |
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| EfficientNet_V2_S | 21.5 | |
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| ResNet34 | 21.8 | |
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| ResNeXt50_32X4D | 25 | |
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| ResNet50 | 25.6 | |
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| Inception_V3 | 27.2 | |
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| Swin_T | 28.3 | |
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| Swin_V2_T | 28.4 | |
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| ConvNeXt_Tiny | 28.6 | |
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| DenseNet161 | 28.7 | |
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| EfficientNet_B5 | 30.4 | |
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| MaxVit_T | 30.9 | |
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| RegNet_Y_8GF | 39.4 | |
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| RegNet_X_8GF | 39.6 | |
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| EfficientNet_B6 | 43 | |
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| ResNet101 | 44.5 | |
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| Swin_S | 49.6 | |
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| Swin_V2_S | 49.7 | |
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| ConvNeXt_Small | 50.2 | |
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| EfficientNet_V2_M | 54.1 | |
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| RegNet_X_16GF | 54.3 | |
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| ResNet152 | 60.2 | |
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| AlexNet | 61.1 | |
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| EfficientNet_B7 | 66.3 | |
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| Wide_ResNet50_2 | 68.9 | |
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| ResNeXt101_64X4D | 83.5 | |
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| RegNet_Y_16GF | 83.6 | |
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| ViT_B_16 | 86.6 | |
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| Swin_B | 87.8 | |
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| Swin_V2_B | 87.9 | |
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| ViT_B_32 | 88.2 | |
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| ConvNeXt_Base | 88.6 | |
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| ResNeXt101_32X8D | 88.8 | |
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| RegNet_X_32GF | 107.8 | |
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| EfficientNet_V2_L | 118.5 | |
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| Wide_ResNet101_2 | 126.9 | |
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| VGG11_BN | 132.9 | |
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| VGG11 | 132.9 | |
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| VGG13 | 133 | |
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| VGG13_BN | 133.1 | |
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| VGG16_BN | 138.4 | |
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| VGG16 | 138.4 | |
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| VGG19_BN | 143.7 | |
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| VGG19 | 143.7 | |
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| RegNet_Y_32GF | 145 | |
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| ConvNeXt_Large | 197.8 | |
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| ViT_L_16 | 304.3 | |
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| ViT_L_32 | 306.5 | |
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### IMAGENET1K_V2 |
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| Backbone | Params(M) | |
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| :----------------: | :-------: | |
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| MobileNet_V2 | 3.5 | |
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| RegNet_Y_400MF | 4.3 | |
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| MobileNet_V3_Large | 5.5 | |
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| RegNet_X_400MF | 5.5 | |
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| RegNet_Y_800MF | 6.4 | |
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| RegNet_X_800MF | 7.3 | |
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| EfficientNet_B1 | 7.8 | |
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| RegNet_X_1_6GF | 9.2 | |
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| RegNet_Y_1_6GF | 11.2 | |
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| RegNet_X_3_2GF | 15.3 | |
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| RegNet_Y_3_2GF | 19.4 | |
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| ResNeXt50_32X4D | 25 | |
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| ResNet50 | 25.6 | |
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| RegNet_Y_8GF | 39.4 | |
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| RegNet_X_8GF | 39.6 | |
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| ResNet101 | 44.5 | |
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| RegNet_X_16GF | 54.3 | |
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| ResNet152 | 60.2 | |
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| Wide_ResNet50_2 | 68.9 | |
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| RegNet_Y_16GF | 83.6 | |
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| ResNeXt101_32X8D | 88.8 | |
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| RegNet_X_32GF | 107.8 | |
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| Wide_ResNet101_2 | 126.9 | |
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| RegNet_Y_32GF | 145 | |
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## Mirror |
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<https://www.modelscope.cn/datasets/monetjoe/cv_backbones> |
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## Reference |
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[1] <https://pytorch.org/vision/main/_modules><br> |
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[2] <https://pytorch.org/vision/main/models.html> |