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_namefacades: 400 images from CMP Facades dataset. [Citation]cityscapes: 2975 images from the Cityscapes training set. [Citation]maps: 1096 training images scraped from Google Mapsedges2shoes: 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 aday2nightpix2pix 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/dataThis will combine each pair of images (A,B) into a single image file, ready for training.