MLFcGAN
Xiaodong-Bran/MLFcGAN-Multilevel-Feature-Fusion-Based-Conditional-GAN-for-Underwater-Image-Color-Correction
Install / Use
/learn @Xiaodong-Bran/MLFcGANREADME
PyTorch implementation of MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction .
Based on pix2pix by Phillip Isola et al.
Prerequisites
- Linux
- Python, Numpy, PIL
- pytorch 1.2.0
- torchvision 0.4.0
Getting Started
-
Clone this repo:
git clone git@github.com:Xiaodong-Bran/MLFcGAN.git
cd MLFcGAN
-
download the pre-trained model: [google-dirve] (https://drive.google.com/open?id=1OREuAj6DplD0-ipQ3s37aZ6j9Q5kXvtO)
-
Prepare the underwater image dataset.The structure of the image folders should follows:
name_of_dataset/
└── source2target
├── test
│ ├── source
│ └── target
└── train
├── source
└── target
-
Modifiy the test_img_folder and test_output_path in test.sh
-
To test the model, please run:
sh test.sh
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To train the model, please run:
sh train.sh
Acknowledgments
This code is inspired by pix2pix.
Highly recommend the more sophisticated and organized code pytorch-CycleGAN-and-pix2pix by Jun-Yan Zhu.
