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* docs(swin): Update Swin model card to standard format * docs(swin): Refine link to Microsoft organization for Swin models Apply suggestion from @stevhliu in PR #37628. This change updates the link pointing to the official Microsoft Swin Transformer checkpoints on the Hugging Face Hub. The link now directs users specifically to the Microsoft organization page, filtered for Swin models, providing a clearer and more canonical reference compared to the previous general search link. Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * docs(swin): Clarify padding description and link to backbone docs Apply suggestion from @stevhliu in PR #37628. This change introduces two improvements to the Swin model card: 1. Refines the wording describing how Swin handles input padding for better clarity. 2. Adds an internal documentation link to the general "backbones" page when discussing Swin's capability as a backbone model. These updates enhance readability and improve navigation within the Transformers documentation. Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * docs(swin): Change Swin paper link to huggingface.co/papers as suggested Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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Swin Transformer
Swin Transformer 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 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.
import torch
from transformers import pipeline
pipeline = pipeline(
task="image-classification",
model="microsoft/swin-tiny-patch4-window7-224",
torch_dtype=torch.float16,
device=0
)
pipeline(images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
import torch
import requests
from PIL import Image
from transformers import AutoModelForImageClassification, AutoImageProcessor
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="cuda"
)
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("cuda")
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}")
Notes
- Swin can pad the inputs for any input height and width divisible by
32. - Swin can be used as a backbone. When
output_hidden_states = True, it outputs bothhidden_statesandreshaped_hidden_states. Thereshaped_hidden_stateshave 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
TFSwinModel
autodoc TFSwinModel - call
TFSwinForMaskedImageModeling
autodoc TFSwinForMaskedImageModeling - call
TFSwinForImageClassification
autodoc transformers.TFSwinForImageClassification - call