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README.md

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-# Project
+# Swin Transformer
 
-> This repo has been populated by an initial template to help get you started. Please
-> make sure to update the content to build a great experience for community-building.
+By [Ze Liu](https://github.com/zeliu98/)\*, [Yutong Lin](https://github.com/impiga)\*, [Yue Cao](http://yue-cao.me)\*, [Han Hu](https://sites.google.com/site/hanhushomepage/)\*, [Yixuan Wei](https://github.com/weiyx16), [Zheng Zhang](https://stupidzz.github.io/), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en) and [Baining Guo](https://www.microsoft.com/en-us/research/people/bainguo/).
 
-As the maintainer of this project, please make a few updates:
+This repo is the official implementation of ["Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"](https://arxiv.org/abs/2103.14030). The code will be coming soon.
 
-- Improving this README.MD file to provide a great experience
-- Updating SUPPORT.MD with content about this project's support experience
-- Understanding the security reporting process in SECURITY.MD
-- Remove this section from the README
+## Introduction
+
+**Swin Transformer** is initially described in [arxiv](https://arxiv.org/abs/2103.14030), which capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size.
+These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (86.4 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val).
+
+## Citing Swin Transformer
+
+```
+@article{liu2021Swin,
+  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
+  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
+  journal={arXiv preprint arXiv:2103.14030},
+  year={2021}
+}
+```
 
 ## Contributing