Swin Tiny Patch4 Window7 224

🚀 Swin Transformer (tiny-sized model)

A Swin Transformer model trained on ImageNet-1k at a resolution of 224x224, offering efficient image classification capabilities.

🚀 Quick Start

The Swin Transformer model presented here is trained on the ImageNet-1k dataset 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.

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

Model description

The Swin Transformer is a type of Vision Transformer. It constructs hierarchical feature maps by merging image patches (shown in gray) in deeper layers. Due to the computation of self - attention only within each local window (shown in red), it has linear computation complexity with respect to the input image size. As a result, it can serve as a general - purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers generate feature maps of a single low resolution and have quadratic computation complexity with respect to the input image size because of global self - attention computation.

Source

Intended uses & limitations

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

💻 Usage Examples

Basic Usage

Here's how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes:

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")

inputs = processor(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.

📚 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 released under the Apache - 2.0 license.