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 # CycleGAN and pix2pix in PyTorch
 
-This is our ongoing PyTorch implementation for both unpaired and paired image-to-image translation. Check out the original [CycleGAN Torch](https://github.com/junyanz/CycleGAN) and [pix2pix Torch](https://github.com/phillipi/pix2pix)  if you would like to reproduce the exact same results in the paper.
+This is our ongoing PyTorch implementation for both unpaired and paired image-to-image translation.
 
-The code was written by [Jun-Yan Zhu](https://github.com/junyanz) and [Taesung Park](https://github.com/taesung89).  
+The code was written by [Jun-Yan Zhu](https://github.com/junyanz) and [Taesung Park](https://github.com/taesung89).
+
+Check out the original [CycleGAN Torch](https://github.com/junyanz/CycleGAN) and [pix2pix Torch](https://github.com/phillipi/pix2pix) code if you would like to reproduce the exact same results as in the papers.
 
 
 #### CycleGAN: [[Project]](https://junyanz.github.io/CycleGAN/) [[Paper]](https://arxiv.org/pdf/1703.10593.pdf) [[Torch]](https://github.com/junyanz/CycleGAN)
@@ -24,12 +26,12 @@ 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](http://web.mit.edu/phillipi/), [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 Using Conditional Adversarial Networks  
-[Phillip Isola](http://web.mit.edu/phillipi/), [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/)   
+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/)   
 In CVPR 2017.
 
 
@@ -99,12 +101,12 @@ bash ./datasets/download_cyclegan_dataset.sh dataset_name
 - `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 combination of 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 classe of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. 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.
 
-To train a model on your own datasets, you need to create a data folder with two subdirectories `trainA` and `trainB` that contain images from domain A and B. You can test your model on your training set by setting ``phase='train'`` in  `test.lua`. You can also create subdirectories like `testA` and `testB` if you have additional test data.
+To train a model on your own datasets, you need to create a data folder with two subdirectories `trainA` and `trainB` that contain images from domain A and B. You can test your model on your training set by setting ``phase='train'`` in  `test.lua`. You can also create subdirectories `testA` and `testB` if you have test data.
 
-You should **not** expect our method to work on any combination of two random datasets (e.g. `cats<->keyboards`). From our experiments, we find it works better if two datasets share similar visual content. For example, `landscape painting<->landscape photographs` works much better than `portrait painting <-> landscape photographs`. `zebras<->horses` achieves compelling results while `cats<->dogs` completely fails.  See the following section for more discussion.
+You should **not** expect our method to work on just any random combination of input and output datasets (e.g. `cats<->keyboards`). From our experiments, we find it works better if two datasets share similar visual content. For example, `landscape painting<->landscape photographs` works much better than `portrait painting <-> landscape photographs`. `zebras<->horses` achieves compelling results while `cats<->dogs` completely fails.
 
 ### pix2pix datasets
 Download the pix2pix datasets using the following script:
@@ -121,7 +123,7 @@ We provide a python script to generate pix2pix training data in the form of pair
 
 Create folder `/path/to/data` with subfolders `A` and `B`. `A` and `B` should each have their own subfolders `train`, `val`, `test`, etc. In `/path/to/data/A/train`, put training images in style A. In `/path/to/data/B/train`, put the corresponding images in style B. Repeat same for other data splits (`val`, `test`, etc).
 
-Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g. `/path/to/data/A/train/1.jpg` is considered to correspond to `/path/to/data/B/train/1.jpg`.
+Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g., `/path/to/data/A/train/1.jpg` is considered to correspond to `/path/to/data/B/train/1.jpg`.
 
 Once the data is formatted this way, call:
 ```bash
@@ -139,7 +141,7 @@ This will combine each pair of images (A,B) into a single image file, ready for
 
 ## Related Projects:
 [CycleGAN](https://github.com/junyanz/CycleGAN): Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks  
-[pix2pix](https://github.com/phillipi/pix2pix): Image-to-image translation using conditional adversarial nets  
+[pix2pix](https://github.com/phillipi/pix2pix): Image-to-image translation with conditional adversarial nets  
 [iGAN](https://github.com/junyanz/iGAN): Interactive Image Generation via Generative Adversarial Networks
 
 ## Cat Paper Collection