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+*This model was published in HF papers on 2021-11-18 and contributed to Hugging Face Transformers on 2022-07-27.*
+
+
+# Swin Transformer V2
+
+[Swin Transformer V2](https://huggingface.co/papers/2111.09883) is a 3B parameter model that focuses on how to scale a vision model to billions of parameters. It introduces techniques like residual-post-norm combined with cosine attention for improved training stability, log-spaced continuous position bias to better handle varying image resolutions between pre-training and fine-tuning, and a new pre-training method (SimMIM) to reduce the need for large amounts of labeled data. These improvements enable efficiently training very large models (up to 3 billion parameters) capable of processing high-resolution images.
+
+You can find official Swin Transformer V2 checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=swinv2) organization.
+
+> [!TIP]
+> Click on the Swin Transformer V2 models in the right sidebar for more examples of how to apply Swin Transformer V2 to vision tasks.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    task="image-classification",
+    model="microsoft/swinv2-tiny-patch4-window8-256",
+    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/swinv2-tiny-patch4-window8-256",
+)
+model = AutoModelForImageClassification.from_pretrained(
+    "microsoft/swinv2-tiny-patch4-window8-256",
+    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()
+predicted_class_label = model.config.id2label[predicted_class_id]
+print(f"The predicted class label is: {predicted_class_label}")
+```
+
+</hfoption>
+</hfoptions>
+
+## Notes
+
+- Swin Transformer V2 can pad the inputs for any input height and width divisible by `32`.
+- Swin Transformer V2 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)`.
+
+## Swinv2Config
+
+[[autodoc]] Swinv2Config
+
+## Swinv2Model
+
+[[autodoc]] Swinv2Model
+    - forward
+
+## Swinv2ForMaskedImageModeling
+
+[[autodoc]] Swinv2ForMaskedImageModeling
+    - forward
+
+## Swinv2ForImageClassification
+
+[[autodoc]] transformers.Swinv2ForImageClassification
+    - forward