[Docs] Model_doc structure/clarity improvements (#26876)

* first batch of structure improvements for model_docs

* second batch of structure improvements for model_docs

* more structure improvements for model_docs

* more structure improvements for model_docs

* structure improvements for cv model_docs

* more structural refactoring

* addressed feedback about image processors

docs/source/en/model_doc/vit_msn.md

@@ -33,7 +33,13 @@ while producing representations of a high semantic level that perform competitiv
 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:
+<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). 
+
+## Usage 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.
@@ -43,13 +49,6 @@ use the [`ViTMSNForImageClassification`] class which is initialized from [`ViTMS
 - 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.
@@ -65,13 +64,11 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] ViTMSNConfig
 
-
 ## ViTMSNModel
 
 [[autodoc]] ViTMSNModel
     - forward
 
-
 ## ViTMSNForImageClassification
 
 [[autodoc]] ViTMSNForImageClassification