Update README

README.md

@@ -1,3 +1,5 @@
+[![License CC BY-NC-SA 4.0](https://img.shields.io/badge/license-CC4.0-blue.svg)](https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/LICENSE.md)
+
 # Learning Superpixels with Segmentation-Aware Affinity Loss
 [Learning Superpixels with Segmentation-Aware Affinity Loss](https://sites.google.com/site/wctu1009/cvpr18_superpixel) 
 
@@ -6,14 +8,22 @@ IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
 
 [Project](https://sites.google.com/site/wctu1009/cvpr18_superpixel) | [Paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Tu_Learning_Superpixels_With_CVPR_2018_paper.html)
 
+## License
+
+Copyright (C) 2018 NVIDIA Corporation.  All rights reserved.
+Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). 
+
 ## Getting Started
+
 In this repository, we provide the test code and the model trained on the BSDS500 dataset using the [ERS algorithm](https://github.com/mingyuliutw/EntropyRateSuperpixel) as the superpixel segmenter. We also provide the evaluation scripts used in our experiments.
 
 ### Prerequisites
+
 * Hardware: PC with NVIDIA GPU. We have tested the code with GeForce GTX 1080Ti and TitanXP.
 * Software: CUDA 9.1, PyTorch 0.4.1, OpenCV 3.4.2
 
 ### Data Format
+
 The superpixel labels are all integers, so we saved the superpixel labels as single-channel 16-bit png images.
 We can read such png files using OpenCV imread() with extra -1 flag:
 ```
@@ -23,12 +33,14 @@ In our experiments, we preprocess all the datasets so that the segmentation grou
 In the ```/data``` folder we sample some images from the BSDS500 test set and provide their corresponding ground-truth maps in the 16-bit png format for reference.
 
 ### Testing
+
 Go to ```/test``` and run ```test.py```.
 The file ```bsds500.pkl``` is the model trained on the BSDS500 dataset with the [ERS algorithm](https://github.com/mingyuliutw/EntropyRateSuperpixel).
 The ```ERSModule.so``` is a Python interface of the ERS algorithm.
 We modify the original ERS algorithm a bit so that it can take pixel affinities as input. See ```readme_ERS.pdf``` for more details.
 
 ### Evaluation
+
 We provide codes for computing the ASA (Achievable Segmentation Accuracy) and the BR (Boundary Recall) scores for superpixel evaluation.
 Go to ```/eval``` and run one of the two python scripts for evaluation. 
 Make sure the input or output folder paths has been specified correctly in the python scripts.
@@ -38,7 +50,8 @@ The core evaluation functions are written in C++. The file ```EvalSPModule.so```
 See ```readme_eval.pdf``` for more details.
 
 ## Bibtex
-If you find this repository useful in your research, please cite us:
+
+If you find this repository useful in your project, please cite us:
 ```
 @inproceedings{Tu-CVPR-2018,
     author = {Tu, Wei-Chih and Liu, Ming-Yu and Jampani, Varun and Sun, Deqing and Chien, Shao-Yi and Yang, Ming-Hsuan and Kautz, Jan},