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Multispectral conditional Generative Adversarial Nets

This repository is an implementation of "Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets".

Results

Requirements

I recommend Anaconda to manage your Python libraries.
Because it is easy to install some of the libraries necessary to prepare the data.

  • Python3 (tested with 3.5.4)
  • PyTorch (tested with 0.4.1)
  • TorchVision (tested with 0.2.1)
  • Numpy (tested with 1.14.2)
  • OpenCV (tested with 3.3.1)
  • Pillow (tested with 5.0.0)
  • tqdm (tested with 4.15.0)
  • PyYAML (tested with 3.12)

Preparing the data

Please refer to make_dataset/README.md.

How to train

You need set each parameters in config.yml.
When you run train.py, config.yml is automatically copied to a directory out_dir defined at config.yml.

python train.py

How to test

python predict.py --config <path_to_config.yml_in_the_out_dir> --test_dir <path_to_a_directory_stored_test_data> --out_dir <path_to_an_output_directory> --pretrained <path_to_a_pretrained_model> --cuda

Pre-trained model

You can download a pre-trained model from here. (200MB)

License

Academic use only.