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ICCV paper of DualGAN

DualGAN: unsupervised dual learning for image-to-image translation

please cite the paper, if the codes has been used for your research.

architecture of DualGAN

architecture

How to setup

Prerequisites

  • Linux

  • Python (2.7 or later)

  • numpy

  • scipy

  • NVIDIA GPU + CUDA 8.0 + CuDNN v5.1

  • TensorFlow 1.0 or later

Getting Started

steps

  • clone this repo:
git clone https://github.com/duxingren14/DualGAN.git

cd DualGAN
  • download datasets (e.g., sketch-photo), run:
bash ./datasets/download_dataset.sh sketch-photo
  • download pre-trained model (e.g., sketch-photo), run:
bash ./checkpoint/download_ckpt.sh sketch-photo
  • train the model:
python main.py --phase train --dataset_name sketch-photo --image_size 256 --lambda_A 1000.0 --lambda_B 1000.0 --epoch 100
  • test the model:
python main.py --phase test --dataset_name sketch-photo --image_size 256 --lambda_A 1000.0 --lambda_B 1000.0 --epoch 100

optional

Similarly, run experiments on facades dataset with the following commands:

bash ./datasets/download_dataset.sh facades

python main.py --phase train --dataset_name facades --lambda_A 1000.0 --lambda_B 1000.0 --epoch 100

python main.py --phase test --dataset_name facades --lambda_A 1000.0 --lambda_B 1000.0 --epoch 100

For thoese who cannot download datasets or pretrained models using the scripts, please try manual downloading from the link as below:

all datasets from google drive

pretrained models from google drive

Experimental results:

day2night da2ni la2ph ph2la sk2ph ph2sk ch2oi oi2ch

Acknowledgments

Codes are built on the top of pix2pix-tensorflow and DCGAN-tensorflow. Thanks for their precedent contributions!


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