Swin Base Patch4 Window7 224

🚀 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.

🚀 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

📚 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
# 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.

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.