Migrate doc files to Markdown. (#24376)

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/model_doc/vit_msn.md

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+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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+
+# ViTMSN
+
+## Overview
+
+The ViTMSN model was proposed in [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. The paper 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.
+
+The abstract from the paper is the following:
+
+*We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our
+approach matches the representation of an image view containing randomly masked patches to the representation of the original
+unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the
+unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures,
+while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance,
+on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy,
+and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark.*
+
+Tips:
+
+- MSN (masked siamese networks) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training
+objective is to match the prototypes assigned to the unmasked views of the images to that of the masked views of the same images.
+- The authors have only released pre-trained weights of the backbone (ImageNet-1k pre-training). So, to use that on your own image classification dataset,
+use the [`ViTMSNForImageClassification`] class which is initialized from [`ViTMSNModel`]. Follow
+[this notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb) for a detailed tutorial on fine-tuning.
+- MSN is particularly useful in the low-shot and extreme low-shot regimes. Notably, it achieves 75.7% top-1 accuracy with only 1% of ImageNet-1K
+labels when fine-tuned.
+
+
+<img src="https://i.ibb.co/W6PQMdC/Screenshot-2022-09-13-at-9-08-40-AM.png" alt="drawing" width="600"/> 
+
+<small> MSN architecture. Taken from the <a href="https://arxiv.org/abs/2204.07141">original paper.</a> </small>
+
+This model was contributed by [sayakpaul](https://huggingface.co/sayakpaul). The original code can be found [here](https://github.com/facebookresearch/msn). 
+
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT MSN.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`ViTMSNForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+- See also: [Image classification task guide](../tasks/image_classification)
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
+## ViTMSNConfig
+
+[[autodoc]] ViTMSNConfig
+
+
+## ViTMSNModel
+
+[[autodoc]] ViTMSNModel
+    - forward
+
+
+## ViTMSNForImageClassification
+
+[[autodoc]] ViTMSNForImageClassification
+    - forward