FUNIT: Few-Shot Unsupervised Image-to-Image Translation

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Few-shot Unsueprvised Image-to-Image Translation
Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, and Jan Kautz.
In arXiv 2019.

License

The code is released for academic research use only. For commercial use, please contact researchinquiries@nvidia.com.

Installation

Clone this repo git clone https://github.com/NVlabs/FUNIT.git
Install CUDA10.1+
Install cuDNN7.5
Install Anaconda3
Install required python pakcages
- conda install -y pytorch torchvision cudatoolkit=10.0 -c pytorch

To reproduce the results reported in the paper, you would need an NVIDIA DGX1 machine with 8 V100 GPUs.

Dataset Preparation

Animal Face Dataset

We are releasing the Animal Face dataset. If you use this dataset in your publication, please cite the FUNIT paper.

The dataset consists of image crops of the ImageNet ILSVRC2012 training set. Download the dataset and untar the files

cd dataset
wget http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_train.tar
tar xvf ILSVRC2012_img_train.tar

The training images should be in datasets/ILSVRC/Data/CLS-LOC/train. Now, extract the animal face images by running

python tools/extract_animal_faces.py datasets/ILSVRC/Data/CLS-LOC/train --output_folder datasets/animals --coor_file datasets/animal_face_coordinates.txt

The animal face images should be in datasets/animals. Note there are 149 folders. Each folder contains images of one animal kind. The number of images of the dataset is 117,484.
We use 119 animal kinds for training and the ramining 30 animal kinds for evaluation.

Training New Models

Once the animal face dataset is prepared, you can train an animal face translation model by running

python train.py --config configs/funit_animals.yaml

For training a model for a different task, please create a new config file based on the example config.

Citation

If you use this code for your research, please cite our papers.

@inproceedings{liu2019few,
  title={Few-shot Unsueprvised Image-to-Image Translation},
  author={Ming-Yu Liu and Xun Huang and Arun Mallya and Tero Karras and Timo Aila and Jaakko Lehtinen and Jan Kautz.},
  booktitle={arxiv},
  year={2019}
}

HuitMahoon/FUNIT