Create README.md (#1)

- Create README.md (086e2580f08dd6806d05857315c8d271223bc45f)


Co-authored-by: Sayak Paul <[email protected]>

README.md

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+---
+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}
+}
+```