docs/datasets.md

CycleGAN Datasets

Download the CycleGAN datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.

bash ./datasets/download_cyclegan_dataset.sh dataset_name

• facades: 400 images from the CMP Facades dataset. [Citation]
• cityscapes: 2975 images from the Cityscapes training set. [Citation]. Note: Due to license issue, we cannot directly provide the Cityscapes dataset. Please download the Cityscapes dataset from https://cityscapes-dataset.com and use the script ./datasets/prepare_cityscapes_dataset.py.
• maps: 1096 training images scraped from Google Maps.
• horse2zebra: 939 horse images and 1177 zebra images downloaded from ImageNet using keywords wild horse and zebra
• apple2orange: 996 apple images and 1020 orange images downloaded from ImageNet 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. 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.py. You can also create subdirectories testA and testB if you have test data.

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. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.

bash ./datasets/download_pix2pix_dataset.sh dataset_name

• facades: 400 images from CMP Facades dataset. [Citation]
• cityscapes: 2975 images from the Cityscapes training set. [Citation]
• maps: 1096 training images scraped from Google Maps
• edges2shoes: 50k training images from UT Zappos50K dataset. Edges are computed by HED edge detector + post-processing. [Citation]
• edges2handbags: 137K Amazon Handbag images from iGAN project. Edges are computed by HED edge detector + post-processing. [Citation]
• night2day: around 20K natural scene images from Transient Attributes dataset [Citation]. To train a day2night pix2pix model, you need to add --direction BtoA.

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:

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.

Once the data is formatted this way, call:

python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data

This will combine each pair of images (A,B) into a single image file, ready for training.