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*This model was released on 2022-09-29 and added to Hugging Face Transformers on 2022-11-18 and contributed by [alihassanijr](https://huggingface.co/alihassanijr).*

# Dilated Neighborhood Attention Transformer

[Dilated Neighborhood Attention Transformer](https://huggingface.co/papers/2209.15001) extends Neighborhood Attention (NA) by incorporating a Dilated Neighborhood Attention (DiNA) pattern, enhancing global context capture without additional computational cost. DiNAT combines local attention from NA with DiNA's sparse global attention, leading to significant performance improvements over models like NAT, Swin, and ConvNeXt. The large DiNAT variant achieves state-of-the-art results in various vision tasks, including COCO object detection, COCO instance segmentation, ADE20K semantic segmentation, and panoptic segmentation on both COCO and ADE20K datasets.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="shi-labs/dinat-mini-in1k-224", dtype="auto")
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
```

</hfoption>
<hfoption id="AutoModel">

```python
import torch
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224")
model = AutoModelForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224", dtype="auto")

inputs = image_processor(image, return_tensors="pt")

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

predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```

</hfoption>
</hfoptions>

## DinatConfig

[[autodoc]] DinatConfig

## DinatModel

[[autodoc]] DinatModel
    - forward

## DinatForImageClassification

[[autodoc]] DinatForImageClassification
    - forward

