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DualGAN-tensorflow: tensorflow implementation of DualGAN
| Date | Stars |
|---|---|
| 2026-07-24 | 268 |
| 2026-07-25 | 268 |
| 2026-07-28 | 268 |
| 2026-07-30 | 268 |
| 2026-08-06 | 268 |
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# ICCV paper of DualGAN <a href="https://arxiv.org/abs/1704.02510">DualGAN: unsupervised dual learning for image-to-image translation</a> please cite the paper, if the codes has been used for your research. # architecture of DualGAN  # 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: <a href="https://drive.google.com/drive/folders/1i7hvUocQ5-u9K1QcD_NjIEKgkTWB7QMh?usp=sharing">all datasets from google drive</a> <a href="https://drive.google.com/drive/folders/1H6t-JLe12_mP5T6bdwYxtXUQJD1WvL82?usp=sharing">pretrained models from google drive</a> # Experimental results:         # Acknowledgments Codes are built on the top of pix2pix-tensorflow and DCGAN-tensorflow. Thanks for their precedent contributions!
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