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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_hybrid.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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+
+# Hybrid Vision Transformer (ViT Hybrid)
+
+## Overview
+
+The hybrid Vision Transformer (ViT) model was proposed in [An Image is Worth 16x16 Words: Transformers for Image Recognition
+at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk
+Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob
+Uszkoreit, Neil Houlsby. It's the first paper that successfully trains a Transformer encoder on ImageNet, attaining
+very good results compared to familiar convolutional architectures. ViT hybrid is a slight variant of the [plain Vision Transformer](vit),
+by leveraging a convolutional backbone (specifically, [BiT](bit)) whose features are used as initial "tokens" for the Transformer.
+
+
+The abstract from the paper is the following:
+
+*While the Transformer architecture has become the de-facto standard for natural language processing tasks, its
+applications to computer vision remain limited. In vision, attention is either applied in conjunction with
+convolutional networks, or used to replace certain components of convolutional networks while keeping their overall
+structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to
+sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of
+data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.),
+Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring
+substantially fewer computational resources to train.*
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code (written in JAX) can be
+found [here](https://github.com/google-research/vision_transformer).
+
+
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT Hybrid.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`ViTHybridForImageClassification`] 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.
+
+
+## ViTHybridConfig
+
+[[autodoc]] ViTHybridConfig
+
+## ViTHybridImageProcessor
+
+[[autodoc]] ViTHybridImageProcessor
+    - preprocess
+
+## ViTHybridModel
+
+[[autodoc]] ViTHybridModel
+    - forward
+
+## ViTHybridForImageClassification
+
+[[autodoc]] ViTHybridForImageClassification
+    - forward