pix2pix-pytorch-master_GaN_gan去噪_pix2pix去噪_去噪_pix2pix_

# pix2pix-pytorch PyTorch implementation of [Image-to-Image Translation Using Conditional Adversarial Networks](https://arxiv.org/pdf/1611.07004v1.pdf). Based on [pix2pix](https://phillipi.github.io/pix2pix/) by [Phillip Isola](https://github.com/phillipi) et al. The examples from the paper: <img src="examples.jpg" width = "766" height = "282" alt="examples" align=center /> ## Prerequisites + Linux + Python, Numpy, PIL + pytorch 0.4.0 + torchvision 0.2.1 ## Getting Started + Clone this repo: git clone git@github.com:mrzhu-cool/pix2pix-pytorch.git cd pix2pix-pytorch + Get dataset unzip dataset/facades.zip + Train the model: python train.py --dataset facades --cuda + Test the model: python test.py --dataset facades --cuda ## Acknowledgments This code is a simple implementation of [pix2pix](https://phillipi.github.io/pix2pix/). Easier to understand. Note that we use a downsampling-resblocks-upsampling structure instead of the unet structure in this code, therefore the results of this code may inconsistent with the results presented in the paper. Highly recommend the more sophisticated and organized code [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) by [Jun-Yan Zhu](https://github.com/junyanz).