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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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+ - image-classification
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+
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+ datasets:
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+ - imagenet-1k
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+
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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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+ ---
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+
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+ # RegNet
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+
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+ RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
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+
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+ Disclaimer: The team releasing RegNet 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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+
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+ ## Model description
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+
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+ The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
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+
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+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png)
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+
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+ ## Intended uses & limitations
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+
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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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+
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+ ### How to use
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+
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+ Here is how to use this model:
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+
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+ ```python
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+ >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
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+ >>> import torch
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+ >>> from datasets import load_dataset
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+
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+ >>> dataset = load_dataset("huggingface/cats-image")
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+ >>> image = dataset["test"]["image"][0]
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+
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+ >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
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+ >>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
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+
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+ >>> inputs = feature_extractor(image, return_tensors="pt")
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+
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+ >>> with torch.no_grad():
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+ ... logits = model(**inputs).logits
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+
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+ >>> # model predicts one of the 1000 ImageNet classes
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+ >>> predicted_label = logits.argmax(-1).item()
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+ >>> print(model.config.id2label[predicted_label])
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+ 'tabby, tabby cat'
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+ ```
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+
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+
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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).