Dataloaders

To choose a dataloader please use the flag --dataset_mode dataloader_name.

Unaligned dataset

Name : unaligned You need to create two directories to host images from domain A /path/to/data/trainA and from domain B /path/to/data/trainB. Then you can train the model with the dataset flag --dataroot /path/to/data. Optionally, you can create hold-out test datasets at /path/to/data/testA and /path/to/data/testB to test your model on unseen images.

Unaligned and labeled dataset

Name : unaligned_labeled You need to create two directories to host images from domain A /path/to/data/trainA and from domain B /path/to/data/trainB. In trainA, you have to separate your data into directories, each directory belongs to a class. Then you can train the model with the dataset flag --dataroot /path/to/data. Optionally, you can create hold-out test datasets at /path/to/data/testA and /path/to/data/testB to test your model on unseen images.

Unaligned and labeled (with masks) dataset

Name : unaligned_labeled_mask For each domain A and B, you have to create a file :code:paths.txt` which each line gives paths to the image and to the mask, separated by space, e.g. path/to/image path/to/mask. You need two create two directories to host paths.txt from each domain A /path/to/data/trainA and from domain B /path/to/data/trainB. Then you can train the model with the dataset flag --dataroot /path/to/data. Optionally, you can create hold-out test datasets at /path/to/data/testA and /path/to/data/testB to test your model on unseen images.