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+   9. Accepting Warranty or Additional Liability. While redistributing
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+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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diff --git a/README.md b/README.md
@@ -1,4 +1,69 @@
-# BPR
-code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`
+# Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation (CVPR 2021)
 
-Coming soon...
+
+
+## Introduction
+
+PBR is a conceptually simple yet effective post-processing refinement framework to improve the boundary quality of instance segmentation. Following the idea of looking closer to segment boundaries better, BPR extracts and refines a series of small boundary patches along the predicted instance boundaries. The proposed BPR framework (as shown below) yields significant improvements over the Mask R-CNN baseline on the Cityscapes benchmark, especially on the boundary-aware metrics. 
+
+
+<p align="center">
+<img src="framework.png" width="80%" alt="framework"/>
+</p>
+
+For more details, please refer to our [paper](https://arxiv.org/abs/2104.05239).
+
+## Installation
+
+Please refer to [INSTALL.md](docs/install.md).
+
+
+## Inference
+
+Suppose you have some instance segmentation results of Cityscapes dataset, as the following format:
+
+```
+maskrcnn_val
+- frankfurt_000001_064130_leftImg8bit_pred.txt
+- frankfurt_000001_064305_leftImg8bit_0_person.png
+- frankfurt_000001_064305_leftImg8bit_10_motorcycle.png
+- ...
+```
+
+We provide a script ([tools/inference.sh](tools/inference.sh)) to perform refinement operation, usage:
+
+```
+IOU_THRESH=0.55 \
+IMG_DIR=data/cityscapes/leftImg8bit/val \
+GT_JSON=data/cityscapes/annotations/instancesonly_filtered_gtFine_val.json \
+BPR_ROOT=. \
+GPUS=4 \
+sh tools/inference.sh configs/bpr/hrnet48_256.py ckpts/hrnet48_256.pth maskrcnn_val maskrcnn_val_refined
+```
+
+The refinement results will be save in `maskrcnn_val_refined/refined`
+
+
+## Models
+
+| Backbone | Dataset | Checkpoint |
+| :------: | :------: | :------: |
+| HRNet-18s | Cityscapes | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/a15da4d679654111ba89/?dl=1) |
+| HRNet-48 | Cityscapes | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/54d7c737540444b38b18/?dl=1) |
+
+## Acknowledgement
+
+This project is based on [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) code base.
+
+## Citation
+
+If you find this project useful in your research, please consider citing:
+
+```
+@article{tang2021look,
+  title={Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation},
+  author={Chufeng Tang and Hang Chen and Xiao Li and Jianmin Li and Zhaoxiang Zhang and Xiaolin Hu},
+  journal={arXiv preprint arXiv:2104.05239},
+  year={2021}
+}
+```
diff --git a/README_mmseg.md b/README_mmseg.md
@@ -0,0 +1,106 @@
+<div align="center">
+  <img src="resources/mmseg-logo.png" width="600"/>
+</div>
+<br />
+
+[![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/en/latest/)
+[![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation)
+[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
+
+Documentation: https://mmsegmentation.readthedocs.io/
+
+## Introduction
+
+MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
+It is a part of the OpenMMLab project.
+
+The master branch works with **PyTorch 1.3 to 1.6**.
+
+![demo image](resources/seg_demo.gif)
+
+### Major features
+
+- **Unified Benchmark**
+
+  We provide a unified benchmark toolbox for various semantic segmentation methods.
+
+- **Modular Design**
+
+  We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.
+
+- **Support of multiple methods out of box**
+
+  The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.
+
+- **High efficiency**
+
+  The training speed is faster than or comparable to other codebases.
+
+## License
+
+This project is released under the [Apache 2.0 license](LICENSE).
+
+## Changelog
+
+v0.7.0 was released in 07/10/2020.
+Please refer to [changelog.md](docs/changelog.md) for details and release history.
+
+## Benchmark and model zoo
+
+Results and models are available in the [model zoo](docs/model_zoo.md).
+
+Supported backbones:
+
+- [x] ResNet
+- [x] ResNeXt
+- [x] [HRNet](configs/hrnet/README.md)
+- [x] [ResNeSt](configs/resnest/README.md)
+- [x] [MobileNetV2](configs/mobilenet_v2/README.md)
+
+Supported methods:
+
+- [x] [FCN](configs/fcn)
+- [x] [PSPNet](configs/pspnet)
+- [x] [DeepLabV3](configs/deeplabv3)
+- [x] [PSANet](configs/psanet)
+- [x] [DeepLabV3+](configs/deeplabv3plus)
+- [x] [UPerNet](configs/upernet)
+- [x] [NonLocal Net](configs/nonlocal_net)
+- [x] [EncNet](configs/encnet)
+- [x] [CCNet](configs/ccnet)
+- [x] [DANet](configs/danet)
+- [x] [GCNet](configs/gcnet)
+- [x] [ANN](configs/ann)
+- [x] [OCRNet](configs/ocrnet)
+- [x] [Fast-SCNN](configs/fastscnn)
+- [x] [Semantic FPN](configs/sem_fpn)
+- [x] [PointRend](configs/point_rend)
+- [x] [EMANet](configs/emanet)
+- [x] [DNLNet](configs/dnlnet)
+- [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)
+
+## Installation
+
+Please refer to [INSTALL.md](docs/install.md) for installation and dataset preparation.
+
+## Get Started
+
+Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMSegmentation.
+There are also tutorials for [adding new dataset](docs/tutorials/new_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), and [adding new modules](docs/tutorials/new_modules.md).
+
+A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
+
+## Contributing
+
+We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
+
+## Acknowledgement
+
+MMSegmentation is an open source project that welcome any contribution and feedback.
+We wish that the toolbox and benchmark could serve the growing research
+community by providing a flexible as well as standardized toolkit to reimplement existing methods
+and develop their own new semantic segmentation methods.