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通过CycleGAN网络模型实现无匹配数据的风格迁移,将真实人脸图片转换为漫画头像

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Cai-Zi/CycleGANProject

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CycleGAN应用

使用CycleGAN网络模型实现无匹配数据的风格迁移,将真实人脸图片转换为漫画头像,主要参考了这篇博客

开发环境

名称 版本
GPU GeForce GTX 1080 Ti
CUDA 9.0
CUDNN 7.1.4
Python 3.6.4
tensorflow-gpu 1.9.0
torch 1.1.0
torchvision 0.3.0
face-alignment 1.0.0
dlib 19.17.0
numpy 1.18.4
opencv-python 4.1.0.25

训练方法

train.py进行模型的训练
test.py测试模型的转换效果

参考

junyanz/CycleGAN仓库
J. Zhu, T. Park, P. Isola and A. A. Efros, "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 2242-2251, doi: 10.1109/ICCV.2017.244.
https://blog.csdn.net/dqcfkyqdxym3f8rb0/article/details/106030098
https://blog.csdn.net/c9Yv2cf9I06K2A9E/article/details/79557699
https://blog.csdn.net/syysyf99/article/details/100120952
https://blog.csdn.net/liongxiong/article/details/80875885
https://blog.csdn.net/yxpandjay/article/details/90369947
https://blog.csdn.net/m0_37605642/article/details/98854753
https://blog.csdn.net/sunmingyang1987/article/details/102872658

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通过CycleGAN网络模型实现无匹配数据的风格迁移,将真实人脸图片转换为漫画头像

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