# 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.

- [`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