Swin Small Patch4 Window7 224

🚀 Swin Transformer (small-sized model)

A Swin Transformer model trained on ImageNet-1k at a resolution of 224x224, suitable for image classification tasks.

🚀 Quick Start

The Swin Transformer model presented here is trained on ImageNet-1k at a resolution of 224x224. It was first introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and initially released in this repository.

Disclaimer: The team that released the Swin Transformer did not create a model card for this model. This model card is 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 the input image size because self - attention is computed 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

📚 Documentation

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.

Intended uses & limitations

You can use the raw model for image classification. Check the model hub to find fine - tuned versions for tasks that interest you.

💻 Usage Examples

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-small-patch4-window7-224")
model = SwinForImageClassification.from_pretrained("microsoft/swin-small-patch4-window7-224")

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, 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}
}

📄 License

This model is licensed under the Apache 2.0 license.