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1 Pix2pix

1.1 Principle

Pix2pix uses paired images for image translation, which has two different styles of the same image as input, can be used for style transfer. Pix2pix is encouraged by cGAN, cGAN inputs a noisy image and a condition as the supervision information to the generation network, pix2pix uses another style of image as the supervision information input into the generation network, so the fake image is related to another style of image which is input as supervision information, thus realizing the process of image translation.

1.2 How to use

1.2.1 Prepare Datasets

Paired datasets used by Pix2pix can be download from here For example, the structure of facades is as following:

  facades
     ├── test
     ├── train
     └── val

You can download from wget, download facades from wget for example:

  wget http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/facades.tar.gz --no-check-certificate

1.2.2 Train/Test

Datasets used in example is facades, you can change it to your own dataset in the config file.

Train a model:

   python -u tools/main.py --config-file configs/pix2pix_facades.yaml

Test the model:

   python tools/main.py --config-file configs/pix2pix_facades.yaml --evaluate-only --load ${PATH_OF_WEIGHT}

1.3 Results

1.4 模型下载

模型 数据集 下载地址
Pix2Pix_cityscapes cityscapes Pix2Pix_cityscapes
Pix2Pix_facedes facades Pix2Pix_facades

2 CycleGAN

2.1 Principle

CycleGAN uses unpaired pictures for image translation, input two different images with different styles, and automatically perform style transfer. CycleGAN consists of two generators and two discriminators, generator A is inputting images of style A and outputting images of style B, generator B is inputting images of style B and outputting images of style A. The biggest difference between CycleGAN and pix2pix is that CycleGAN can realize image translation without establishing a one-to-one mapping between the source domain and the target domain.

2.2 How to use

2.2.1 Prepare Datasets

Unpair datasets used by CycleGAN can be download from here For example, the structure of cityscapes is as following:

  cityscapes
      ├── test
      ├── testA
      ├── testB
      ├── train
      ├── trainA
      └── trainB

You can download from wget, download facades from wget for example:

  wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/facades.zip --no-check-certificate

2.2.2 Train/Test

Datasets used in example is cityscapes, you can change it to your own dataset in the config file.

Train a model:

   python -u tools/main.py --config-file configs/cyclegan_cityscapes.yaml

Test the model:

   python tools/main.py --config-file configs/cyclegan_cityscapes.yaml --evaluate-only --load ${PATH_OF_WEIGHT}

2.3 Results

2.4 模型下载

模型 数据集 下载地址
CycleGAN_cityscapes cityscapes CycleGAN_cityscapes
CycleGAN_horse2zebra horse2zebra CycleGAN_horse2zebra

References

    1. Image-to-Image Translation with Conditional Adversarial Networks
    @inproceedings{isola2017image,
  title={Image-to-Image Translation with Conditional Adversarial Networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
  year={2017}
}
    1. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
    @inproceedings{CycleGAN2017,
title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss},
author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
year={2017}
}