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Visual-Attention-Network
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van-base

Image Classification
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PyTorch
van
vision
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van-base
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---
license: apache-2.0
tags:
- vision
- image-classification

datasets:
- imagenet-1k

widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
  example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
  example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
  example_title: Palace

---

# Van

Van model trained on imagenet-1k. It was introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [this repository](https://github.com/Visual-Attention-Network/VAN-Classification). 

Disclaimer: The team releasing Van did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

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.

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/van_architecture.png)

## Intended uses & limitations

You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=van) to look for
fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model:

```python
>>> from transformers import AutoFeatureExtractor, VanForImageClassification
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> feature_extractor = AutoFeatureExtractor.from_pretrained("Visual-Attention-Network/van-base")
>>> model = VanForImageClassification.from_pretrained("Visual-Attention-Network/van-base")

>>> inputs = feature_extractor(image, return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
tabby, tabby cat
```



For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/van).