Multi-modal and Multi-domain Image-to-Image translation

This work aims at developing deep learning models to perform multi-modal and multi-domain image translations between different weather domains. The work propose different deep learning model configurations that combine several state-of-the art ideas in performing image translations. The ideas from GANs and VAEs are utilized in building these configurations to achieve high quality and diversity translations. See the file MODELCONFIG.md for the architecture of these configurations.

Example Translations

Dependencies

You can install all the dependencies by

pip install requirements.txt

or manually install the dependencies listed below:

numpy >= 1.21.5
python >=3.7
pytorch >= 1.9 with cuda 11.3
torchvision >= 0.8.2
tensorboard >= 2.7.0
Pillow = 8.1.2 (doesn't support python 3.10)
pytorch-fid = 0.2.1 (if you want to compute FID values)
lpips = 0.1.4 (if you want to computer LPIPS scores)

Getting Started

Datasets

The training datasets containing weather images can be downloaded from the link: Image2Weather

Usage

Generate sample images

• Directly run the script to translate images using AdaINModel configuration.
• Edit the --model option to choose between BaseModel or AdaINModel configurations to play around with different configurations.

bash ./sample.sh

Train the model

Run python train.py -h for more information on commandline options

bash ./train.sh

Translations grid