readme: ---
  license: apache-2.0
  tags:
  - vision
  datasets:
  - imagenet-1k
  ---
  
  # Vision Transformer (base sized model) pre-trained with MSN (patch size of 4)
  
  Vision Transformer (ViT) model pre-trained using the MSN method. It was introduced in the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas and first released in [this repository](https://github.com/facebookresearch/msn). 
  
  Disclaimer: The team releasing MSN did not write a model card for this model so this model card has been written by the Hugging Face team.
  
  ## Model description
  
  The Vision Transformer (ViT) is a transformer encoder model (BERT-like). Images are presented to the model as a sequence of fixed-size patches.
  
  MSN presents a joint-embedding architecture to match the prototypes of masked patches with that of the unmasked patches. With this setup, their method yields excellent performance in the low-shot and extreme low-shot regimes.
  
  By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder.
  
  ## Intended uses & limitations
  
  You can use the raw model for downstream tasks like image classification. See the [model hub](https://huggingface.co/models?filter=vit_msn) to look for different versions of MSN pre-trained models that interest you. The model is particularly beneficial when you have a few labeled samples in your training set.
  
  ### How to use
  
  Here is how to use this backbone encoder:
  
  ```python
  from transformers import AutoFeatureExtractor, ViTMSNModel
  import torch
  from PIL import Image
  import requests
  
  url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-msn-base-4")
  model = ViTMSNModel.from_pretrained("facebook/vit-msn-base-4")
  inputs = feature_extractor(images=image, return_tensors="pt")
  with torch.no_grad():
      outputs = model(**inputs)
  last_hidden_states = outputs.last_hidden_state
  ```
  
  For fine-tuning on image classification use the `ViTMSNForImageClassification` class:
  
  ```python
  from transformers import AutoFeatureExtractor, ViTMSNForImageClassification
  import torch
  from PIL import Image
  import requests
  
  url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-msn-base-4")
  model = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-base-4")
  
  ...
  ```
  
  
  ### Citation
  
  ```bibtex
  @article{assran2022masked,
    title={Masked Siamese Networks for Label-Efficient Learning}, 
    author={Assran, Mahmoud, and Caron, Mathilde, and Misra, Ishan, and Bojanowski, Piotr, and Bordes, Florian and Vincent, Pascal, and Joulin, Armand, and Rabbat, Michael, and Ballas, Nicolas},
    journal={arXiv preprint arXiv:2204.07141},
    year={2022}
  }
  ```