---
license: mit
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
---

# DiNAT (tiny variant)

DiNAT-Tiny trained on ImageNet-1K at 224x224 resolution. 
It was introduced in the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). 

## Model description

DiNAT is a hierarchical vision transformer based on Neighborhood Attention (NA) and its dilated variant (DiNA).
Neighborhood Attention is a restricted self attention pattern in which each token's receptive field is limited to its nearest neighboring pixels.
NA and DiNA are therefore sliding-window attention patterns, and as a result are highly flexible and maintain translational equivariance.

They come with PyTorch implementations through the [NATTEN](https://github.com/SHI-Labs/NATTEN/) package.


![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dilated-neighborhood-attention-pattern.jpg)

[Source](https://paperswithcode.com/paper/dilated-neighborhood-attention-transformer)

## Intended uses & limitations

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

### Example

Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes:

```python
from transformers import AutoImageProcessor, DinatForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = AutoImageProcessor.from_pretrained("shi-labs/dinat-tiny-in1k-224")
model = DinatForImageClassification.from_pretrained("shi-labs/dinat-tiny-in1k-224")

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```

For more examples, please refer to the [documentation](https://huggingface.co/transformers/model_doc/dinat.html#).

### Requirements
Other than transformers, this model requires the [NATTEN](https://shi-labs.com/natten) package.

If you're on Linux, you can refer to [shi-labs.com/natten](https://shi-labs.com/natten) for instructions on installing with pre-compiled binaries (just select your torch build to get the correct wheel URL).

You can alternatively use `pip install natten` to compile on your device, which may take up to a few minutes.
Mac users only have the latter option (no pre-compiled binaries).

Refer to [NATTEN's GitHub](https://github.com/SHI-Labs/NATTEN/) for more information.


### BibTeX entry and citation info

```bibtex
@article{hassani2022dilated,
	title        = {Dilated Neighborhood Attention Transformer},
	author       = {Ali Hassani and Humphrey Shi},
	year         = 2022,
	url          = {https://arxiv.org/abs/2209.15001},
	eprint       = {2209.15001},
	archiveprefix = {arXiv},
	primaryclass = {cs.CV}
}
```