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*This model was published in HF papers on 2021-03-25 and contributed to Hugging Face Transformers on 2022-01-21.*


# Swin Transformer

[Swin Transformer](https://huggingface.co/papers/2103.14030) is a hierarchical vision transformer. Images are processed in patches and windowed self-attention is used to capture local information. These windows are shifted across the image to allow for cross-window connections, capturing global information more efficiently. This hierarchical approach with shifted windows allows the Swin Transformer to process images effectively at different scales and achieve linear computational complexity relative to image size, making it a versatile backbone for various vision tasks like image classification and object detection.

You can find all official Swin Transformer checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=swin) organization.

> [!TIP]
> Click on the Swin Transformer models in the right sidebar for more examples of how to apply Swin Transformer to different image tasks.

The example below demonstrates how to classify an image with [`Pipeline`] or the [`AutoModel`] class.

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

```python
from transformers import pipeline


pipeline = pipeline(
    task="image-classification",
    model="microsoft/swin-tiny-patch4-window7-224",
    device=0
)
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
```

</hfoption>

<hfoption id="AutoModel">

```python
import requests
import torch
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForImageClassification


image_processor = AutoImageProcessor.from_pretrained(
    "microsoft/swin-tiny-patch4-window7-224",
    use_fast=True,
)
model = AutoModelForImageClassification.from_pretrained(
    "microsoft/swin-tiny-patch4-window7-224",
    device_map="auto"
)

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

with torch.no_grad():
  logits = model(**inputs).logits
predicted_class_id = logits.argmax(dim=-1).item()

class_labels = model.config.id2label
predicted_class_label = class_labels[predicted_class_id]
print(f"The predicted class label is: {predicted_class_label}")
```

</hfoption>
</hfoptions>

## Notes

- Swin can pad the inputs for any input height and width divisible by `32`.
- Swin can be used as a [backbone](../backbones). When `output_hidden_states = True`, it outputs both `hidden_states` and `reshaped_hidden_states`. The `reshaped_hidden_states` have a shape of `(batch, num_channels, height, width)` rather than `(batch_size, sequence_length, num_channels)`.

## SwinConfig

[[autodoc]] SwinConfig

## SwinModel

[[autodoc]] SwinModel
    - forward

## SwinForMaskedImageModeling

[[autodoc]] SwinForMaskedImageModeling
    - forward

## SwinForImageClassification

[[autodoc]] transformers.SwinForImageClassification
    - forward
