đ Swin Transformer (base-sized model)
A Swin Transformer model trained on ImageNet - 1k at a resolution of 224x224, which can be used for image classification tasks.
đ Quick Start
The Swin Transformer model is trained on ImageNet - 1k at a resolution of 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin - Transformer).
Disclaimer: The team releasing Swin Transformer did not write a model card for this model, so this model card has been written by the Hugging Face team.
⨠Features
- Hierarchical Feature Maps: The Swin Transformer builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers.
- Linear Computation Complexity: It has linear computation complexity to input image size due to the computation of self - attention only within each local window (shown in red).
- General - Purpose Backbone: It can serve as a general - purpose backbone for both image classification and dense recognition tasks.

Source
đ Documentation
Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for fine - tuned versions on a task that interests you.
How to use
Basic Usage
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
from transformers import AutoFeatureExtractor, SwinForImageClassification
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 = AutoFeatureExtractor.from_pretrained("microsoft/swin-base-patch4-window7-224")
model = SwinForImageClassification.from_pretrained("microsoft/swin-base-patch4-window7-224")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
For more code examples, we refer to the documentation.
BibTeX entry and citation info
@article{DBLP:journals/corr/abs-2103-14030,
author = {Ze Liu and
Yutong Lin and
Yue Cao and
Han Hu and
Yixuan Wei and
Zheng Zhang and
Stephen Lin and
Baining Guo},
title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
journal = {CoRR},
volume = {abs/2103.14030},
year = {2021},
url = {https://arxiv.org/abs/2103.14030},
eprinttype = {arXiv},
eprint = {2103.14030},
timestamp = {Thu, 08 Apr 2021 07:53:26 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
đ License
This model is licensed under the Apache - 2.0 license.
Property |
Details |
Model Type |
Swin Transformer (base - sized model) |
Training Data |
ImageNet - 1k |
Tags |
vision, image - classification |
Widget Examples |
Tiger, Teapot, Palace |