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README.md
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- n<1K
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# Dataset Card for "
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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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## Viewer
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<https://huggingface.co/spaces/
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### Data Fields
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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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### Splits
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| subsets |
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| :--: |
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| IMAGENET1K_V1 |
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| IMAGENET1K_V2 |
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## Maintenance
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```bash
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git clone [email protected]:datasets/
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```
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## Usage
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```python
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from datasets import load_dataset
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backbones = load_dataset("
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for weights in backbones["IMAGENET1K_V1"]:
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print(weights)
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for weights in backbones["IMAGENET1K_V2"]:
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print(weights)
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```
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## Param
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### IMAGENET1K_V1
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| Backbone | Params(M) |
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| :----------------: | :-------: |
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- n<1K
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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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## Viewer
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<https://huggingface.co/spaces/monetjoe/cv-backbones>
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### Data Fields
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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 [email protected]:datasets/monetjoe/cv_backbones
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```
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## Usage
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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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## 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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