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# VAN

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

<Tip warning={true}>

This model is in maintenance mode only, we don't accept any new PRs changing its code.

If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.

</Tip>

## Overview

The VAN model was proposed in [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.

This paper introduces a new attention layer based on convolution operations able to capture both local and distant relationships. This is done by combining normal and large kernel convolution layers. The latter uses a dilated convolution to capture distant correlations.

The abstract from the paper is the following:

*While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc. Code is available at [this https URL](https://github.com/Visual-Attention-Network/VAN-Classification).*

Tips:

- VAN does not have an embedding layer, thus the `hidden_states` will have a length equal to the number of stages.

The figure below illustrates the architecture of a Visual Attention Layer. Taken from the [original paper](https://arxiv.org/abs/2202.09741).

<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/van_architecture.png"/>

This model was contributed by [Francesco](https://huggingface.co/Francesco). The original code can be found [here](https://github.com/Visual-Attention-Network/VAN-Classification).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with VAN.

<PipelineTag pipeline="image-classification"/>

- [`VanForImageClassification`] 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.

## VanConfig

[[autodoc]] VanConfig

## VanModel

[[autodoc]] VanModel
    - forward

## VanForImageClassification

[[autodoc]] VanForImageClassification
    - forward

