CycleGAN

An implementation of CycleGan for learning an image-to-image translation without input-output pairs using Keras.

• Original paper: https://arxiv.org/abs/1703.10593

• You can check the architecture of our models here: Generator, Discriminator

Results

1. Monet -> Photo

2. Photo -> Portrait

Requirement

$ pip install -r requirements.txt

Data preparing

• Download a dataset, e.g portrait2photo

$ ./bin/datasets.sh portrait2photo

Training

$ python3 train.py


Optoinal arguments:
 --img_rows                        default: 128,           image height
 --img_cols                        default: 128,           image width
 --input_nc                        default: 3,             input image channels
 --n_epochs                        default: 200,           help='number of training epochs
 --use_lr_decay                    default: True,          if True -> the model usus learning rate decay
 --decay_epoch                     default: 101,           epoch after wich start lr decay
 --dataset_name                    default: 'monet2photo', name of the dataset
 --batch_size                      default: 8,             size of the batch
 --lr                              default: 0.0002,        adam: learning rate
 --beta_1                          default: 0.5,           adam: beta_1
 --beta_2                          default: 0.999,         adam: beta_2
 --lambda_validation               default: 1.0,           are the images created by generator corresponding to the discriminator
 --lambda_reconstr                 default: 10.0,          if we use two generators one after the other (in both directions), will the original image work
 --lambda_id                       default: 5.0,           if we apply each generator to images from target area, will the image remain unchanged?
 --n_residual_blocks               default: 9,             number of residual blocks in generator
 --sample_interval t               default: 300,           interval to save models and val images
 --increase_generator_iteration    default: False,         increase the number of generator iterations in train loop
 --generator_iterations            default: 3,             number of generator iterations in train loop

Pretrained model

• You can download pre-trained generator use:

./bin/load_model.sh