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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
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example_title: Tiger |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
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example_title: Teapot |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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example_title: Palace |
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--- |
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# RegNetModel |
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RegNetModel model was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). |
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Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet |
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## Intended uses & limitations |
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You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for |
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fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model: |
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```python |
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>>> from transformers import AutoFeatureExtractor, RegNetModel |
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>>> import torch |
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>>> from datasets import load_dataset |
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>>> dataset = load_dataset("huggingface/cats-image") |
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>>> image = dataset["test"]["image"][0] |
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>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") |
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>>> model = RegNetModel.from_pretrained("zuppif/regnet-y-040") |
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>>> inputs = feature_extractor(image, return_tensors="pt") |
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>>> with torch.no_grad(): |
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... outputs = model(**inputs) |
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>>> last_hidden_states = outputs.last_hidden_state |
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>>> list(last_hidden_states.shape) |
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[1, 1088, 7, 7] |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet). |
