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An implementation of CycleGan using TensorFlow (work in progress).

Original paper:

Results on test data

apple -> orange

Input Output Input Output Input Output
apple2orange_1 apple2orange_1 apple2orange_2 apple2orange_2 apple2orange_3 apple2orange_3

orange -> apple

Input Output Input Output Input Output
orange2apple_1 orange2apple_1 orange2apple_2 orange2apple_2 orange2apple_3 orange2apple_3


  • TensorFlow 1.0.0
  • Python 3.6.0

Data preparing

  • First, download a dataset, e.g. apple2orange
$ bash apple2orange
  • Write the dataset to tfrecords
$ python3

Check $ python3 --help for more details.


$ python3

If you want to change some default settings, you can pass those to the command line, such as:

$ python3  \
    --X=data/tfrecords/horse.tfrecords \

Here is the list of arguments:

usage: [-h] [--batch_size BATCH_SIZE] [--image_size IMAGE_SIZE]
                [--use_lsgan [USE_LSGAN]] [--nouse_lsgan]
                [--norm NORM] [--lambda1 LAMBDA1] [--lambda2 LAMBDA2]
                [--learning_rate LEARNING_RATE] [--beta1 BETA1]
                [--pool_size POOL_SIZE] [--ngf NGF] [--X X] [--Y Y]
                [--load_model LOAD_MODEL]

optional arguments:
  -h, --help            show this help message and exit
  --batch_size BATCH_SIZE
                        batch size, default: 1
  --image_size IMAGE_SIZE
                        image size, default: 256
  --use_lsgan [USE_LSGAN]
                        use lsgan (mean squared error) or cross entropy loss,
                        default: True
  --norm NORM           [instance, batch] use instance norm or batch norm,
                        default: instance
  --lambda1 LAMBDA1     weight for forward cycle loss (X->Y->X), default: 10.0
  --lambda2 LAMBDA2     weight for backward cycle loss (Y->X->Y), default:
  --learning_rate LEARNING_RATE
                        initial learning rate for Adam, default: 0.0002
  --beta1 BETA1         momentum term of Adam, default: 0.5
  --pool_size POOL_SIZE
                        size of image buffer that stores previously generated
                        images, default: 50
  --ngf NGF             number of gen filters in first conv layer, default: 64
  --X X                 X tfrecords file for training, default:
  --Y Y                 Y tfrecords file for training, default:
  --load_model LOAD_MODEL
                        folder of saved model that you wish to continue
                        training (e.g. 20170602-1936), default: None

Check TensorBoard to see training progress and generated images.

$ tensorboard --logdir checkpoints/${datetime}

If you halted the training process and want to continue training, then you can set the load_model parameter like this.

$ python3  \
    --load_model 20170602-1936

Here are some funny screenshots from TensorBoard when training orange -> apple:



  • If high constrast background colors between input and generated images are observed (e.g. black becomes white), you should restart your training!
  • Train several times to get the best models.

Export model

You can export from a checkpoint to a standalone GraphDef file as follow:

$ python3 --checkpoint_dir checkpoints/${datetime} \
                          --XtoY_model apple2orange.pb \
                          --YtoX_model orange2apple.pb \
                          --image_size 256


After exporting model, you can use it for inference. For example:

python3 --model pretrained/apple2orange.pb \
                     --input input_sample.jpg \
                     --output output_sample.jpg \
                     --image_size 256

Pretrained models

My pretrained models are available at


Please open an issue if you have any trouble or found anything incorrect in my code :)


This project is licensed under the MIT License - see the LICENSE file for details.