
# 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).
{"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"}]}
transformers pytorch van image-classification vision dataset:imagenet-1k arxiv:2202.09741 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Van

Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. 

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

## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model:





For more code examples, we refer to the documentation.
# Van

Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. 

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 ## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you. ### How to use

Here is how to use this model:





For more code examples, we refer to the documentation.
TAGS
#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Van

Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. 

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 ## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you. ### How to use

Here is how to use this model:





For more code examples, we refer to the documentation.
passage: TAGS
#transformers #pytorch #van #image-classification #vision #dataset-imagenet-1k #arxiv-2202.09741 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Van

Van model trained on imagenet-1k. It was introduced in the paper Visual Attention Network and first released in this repository. 

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## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.### How to use

Here is how to use this model:





For more code examples, we refer to the documentation.