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@@ -26,24 +26,24 @@ Written by [Christopher Hesse](https://twitter.com/christophrhesse)
 If you use this code for your research, please cite:
 
 Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks  
-[Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz/)\*,  [Taesung Park](https://taesung.me/)\*, [Phillip Isola](https://people.eecs.berkeley.edu/~isola/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/)  
+[Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz/)\*,  [Taesung Park](https://taesung.me/)\*, [Phillip Isola](https://people.eecs.berkeley.edu/~isola/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros)  
 In arxiv, 2017. (* equal contributions)  
 
 
 Image-to-Image Translation with Conditional Adversarial Networks  
-[Phillip Isola](https://people.eecs.berkeley.edu/~isola/), [Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz/), [Tinghui Zhou](https://people.eecs.berkeley.edu/~tinghuiz/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/)   
+[Phillip Isola](https://people.eecs.berkeley.edu/~isola), [Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz), [Tinghui Zhou](https://people.eecs.berkeley.edu/~tinghuiz), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros)   
 In CVPR 2017.
 
 
 
 ## Prerequisites
-- Linux or OSX.
-- Python 2 or Python 3.
-- CPU or NVIDIA GPU + CUDA CuDNN.
+- Linux or macOS
+- Python 2 or 3
+- CPU or NVIDIA GPU + CUDA CuDNN
 
 ## Getting Started
 ### Installation
-- Install PyTorch and dependencies from http://pytorch.org/
+- Install PyTorch and dependencies from http://pytorch.org
 - Install Torch vision from the source.
 ```bash
 git clone https://github.com/pytorch/vision
@@ -118,11 +118,11 @@ Download the CycleGAN datasets using the following script. Some of the datasets
 ```bash
 bash ./datasets/download_cyclegan_dataset.sh dataset_name
 ```
-- `facades`: 400 images from the [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/). [[Citation](datasets/bibtex/facades.tex)]
-- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com/). [[Citation](datasets/bibtex/cityscapes.tex)]
+- `facades`: 400 images from the [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade). [[Citation](datasets/bibtex/facades.tex)]
+- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com). [[Citation](datasets/bibtex/cityscapes.tex)]
 - `maps`: 1096 training images scraped from Google Maps.
-- `horse2zebra`: 939 horse images and 1177 zebra images downloaded from [ImageNet](http://www.image-net.org/) using keywords `wild horse` and `zebra`
-- `apple2orange`: 996 apple images and 1020 orange images downloaded from [ImageNet](http://www.image-net.org/) using keywords `apple` and `navel orange`.
+- `horse2zebra`: 939 horse images and 1177 zebra images downloaded from [ImageNet](http://www.image-net.org) using keywords `wild horse` and `zebra`
+- `apple2orange`: 996 apple images and 1020 orange images downloaded from [ImageNet](http://www.image-net.org) using keywords `apple` and `navel orange`.
 - `summer2winter_yosemite`: 1273 summer Yosemite images and 854 winter Yosemite images were downloaded using Flickr API. See more details in our paper.
 - `monet2photo`, `vangogh2photo`, `ukiyoe2photo`, `cezanne2photo`: The art images were downloaded from [Wikiart](https://www.wikiart.org/). The real photos are downloaded from Flickr using the combination of the tags *landscape* and *landscapephotography*. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853.
 - `iphone2dslr_flower`: both classes of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. See more details in our paper.
@@ -136,10 +136,10 @@ Download the pix2pix datasets using the following script. Some of the datasets a
 ```bash
 bash ./datasets/download_pix2pix_dataset.sh dataset_name
 ```
-- `facades`: 400 images from [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/). [[Citation](datasets/bibtex/facades.tex)]
-- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com/). [[Citation](datasets/bibtex/cityscapes.tex)]
+- `facades`: 400 images from [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade). [[Citation](datasets/bibtex/facades.tex)]
+- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com). [[Citation](datasets/bibtex/cityscapes.tex)]
 - `maps`: 1096 training images scraped from Google Maps
-- `edges2shoes`: 50k training images from [UT Zappos50K dataset](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/shoes.tex)]
+- `edges2shoes`: 50k training images from [UT Zappos50K dataset](http://vision.cs.utexas.edu/projects/finegrained/utzap50k). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/shoes.tex)]
 - `edges2handbags`: 137K Amazon Handbag images from [iGAN project](https://github.com/junyanz/iGAN). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/handbags.tex)]
 
 We provide a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene. For example, these might be pairs {label map, photo} or {bw image, color image}. Then we can learn to translate A to B or B to A: