readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  
  datasets:
  - imagenet-1k
  
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace
  
  ---
  
  # RegNet
  
  RegNet model trained on imagenet-1k. It 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). 
  
  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.
  
  ## Model description
  
  The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet. This model is later finetuned on ImageNet
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png)
  
  ## Intended uses & limitations
  
  You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
  fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model:
  
  ```python
  >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
  >>> import torch
  >>> from datasets import load_dataset
  
  >>> dataset = load_dataset("huggingface/cats-image")
  >>> image = dataset["test"]["image"][0]
  
  >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
  >>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
  
  >>> inputs = feature_extractor(image, return_tensors="pt")
  
  >>> with torch.no_grad():
  ...     logits = model(**inputs).logits
  
  >>> # model predicts one of the 1000 ImageNet classes
  >>> predicted_label = logits.argmax(-1).item()
  >>> print(model.config.id2label[predicted_label])
  'tabby, tabby cat'
  ```
  
  
  
  For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).