readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  datasets:
  - imagenet-1k
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace
  ---
  
  # Swin Transformer (base-sized model) 
  
  Swin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 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.
  
  ## Model description
  
  The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png)
  
  [Source](https://paperswithcode.com/method/swin-transformer)
  
  ## Intended uses & limitations
  
  You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for
  fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
  
  ```python
  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-window12-384")
  model = SwinForImageClassification.from_pretrained("microsoft/swin-base-patch4-window12-384")
  
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits
  # model predicts one of the 1000 ImageNet classes
  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](https://huggingface.co/transformers/model_doc/swin.html#).
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @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}
  }
  ```