Giter Club home page Giter Club logo

art-fid's People

Contributors

comeeasy avatar matthias-wright avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar

art-fid's Issues

A Little Questions about Experiments in the Paper

Thanks for your excellent paper and well-organized codes. I have few questions about the experiment settings in the paper though.

In section 4.1 of the paper, you said that "Style images are sampled from the WikiArt dataset [70] and the BAM dataset [78]." and "we compute the ArtFID with samples of 50k images." and "For each style transfer method, the ArtFID is computed with 5 different samples containing 50k images each.".

I have few questions about the experiment settings.

  1. Are the style images sampled from the same style in WikiArt or do you use many styles seperately and calculate the mean ArtFID?
  2. What is the meaning of "50k images"? 50k content images or 50k style images? If it refers to the number of content images, then how many style images do you use?
  3. What is the meaning of "5 samples containing 50k images each"?

Thanks for your excellent paper again and I am hoping for your response!

Questions about ArtFID formula

Hi experts,
The e.q.2 is sum of LPIPS and FID_infinite.

  1. LPIPS between content and stylized images more higher is better, but opposite on e.q.2.
    ref:
    https://arxiv.org/pdf/2201.12543.pdf
    https://arxiv.org/pdf/2208.00921.pdf
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9244092
    https://arxiv.org/pdf/2006.01431.pdf

  2. FID between style and stylized images is not good for distribution of holistic statistic.
    something like context and structure difference from style and stylized images.
    for example: stylized animal dataset and original style image from Monet
    SIFID from SinGAN in which use single images pair distribution
    https://arxiv.org/pdf/1905.01164.pdf
    IS_infinite: The Inception score is the expectation of KL divergence distance between two sets of generated images
    https://arxiv.org/pdf/2006.01431.pdf

Do you have been some experiments about these two metric functions?

pip install art-fid is not working

Hi,
I tried "pip install art-fid" following your instruction, but it does not work. (screenshot uploaded)
Could you please check your version?
Thank you!

image

Inception network training code

Would it be possible to share the code used for training the Inception network? It would be really helpful.
Thank you in advance, and thank you for your contribution.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.