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Datasets

We used below four datasets in our experiments.

Bedroom Dataset

1355 train and 135 test bedroom images from Ade20k Dataset. [Citation]

Illustraion Dataset

To download illustration images, please refer to Ganilla. [Citation]

Cityscapes Dataset

2975 train and 500 test images from the Cityscapes training set. [Citation]

Coco Datasets

We shared coco elephant and sheep datasets in this GDrive folder

Your Own Datasets

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.

Paired Data Preparation

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, 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 (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.

Edge Map Preparation

In the scripts/hed/edges folder, we provide edge map extraction scripts.

  • First run batch_hed.py. Required steps and explanations are given in the top of that script.
  • Then, run postprocess_main.m. Again explanations are given in the top of that script.

Repeat that procedure for trainA and testA folders.