art-fid 0.0.1

ArtFID: Quantitative Evaluation of Neural Style Transfer

ArtFID: Quantitative Evaluation of Neural Style Transfer (GCPR Oral 2022)

ArtFID: Quantitative Evaluation of Neural Style Transfer
Matthias Wright and Björn Ommer.

Installation

> pip install art-fid

Usage

CUDA_VISIBLE_DEVICES=0 python -m art_fid --style_images path/to/style-images --content_images path/to/content-images --stylized_images path/to/stylized-images

The content images and the corresponding stylized images are compared in pairs. In order to ensure that a content image is matched up with the correct stylized image, both the content images and the stylized images are processed in lexicographical order. A simple way of pairing the content images and the stylized images is to use the name of content image for the corresponding stylized image.

Arguments

--batch_size - Batch size for computing activations. --num_workers - Number of threads used for data loading. --mode - Evaluate ArtFID or ArtFID_infinity, choices = ['art_fid', 'art_fid_inf']. --content_metric - Content metric, choices = ['lpips', 'vgg', 'alexnet']. --device - Device to use, choices = ['cuda', 'cpu']. --style_images - Path to style images. --content_images - Path to content images. --stylized_images - Path to stylized images.

Data

The dataset is contained in artfid_dataset.csv. It consists of 250k labeled artworks.

Acknowledgments

• The implementation of the FID is based on mseitzer/pytorch-fid.
• The implementation of the FID_infinity is taken from mchong6/FID_IS_infinity.
• The implementation of the Inception network is taken from pytorch/vision.
• The checkpoint is hosted on the Huggingface Model Hub.

Citation

@article{wright_gcpr_2022,
    title={ArtFID: Quantitative Evaluation of Neural Style Transfer},
    author={Matthias Wright and Bj{\"o}rn Ommer},
    journal={GCPR},
    year={2022}
}