# Artwork recommendations guided by foundation models: survey and novel approach
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# Editing this README
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## Name
Choose a self-explaining name for your project.
## Description
## Description
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This repository contains the materials presented in the journal paper 'Artwork recommendations guided by foundation models: survey and novel approach'.
The code of notebook 'Retrieve_combined_features.ipynb' illustrates the combination of recommendations with the same weights of genre, style and artist criteria, it can be easily modified for using with different weights.
The code for fine-tuning a classification model is based on the <ahref="https://github.com/huggingface/peft/tree/main/examples/image_classification">example</a>.
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
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## Usage
# Table of content
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-[Overview](#description)
-[Dataset](#dataset)
-[Steps for reproducing the process of dataset creation](#steps)
-[Citation](#citation)
-[Acknowledgements](#acknowledgments)
## Support
## Dataset
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Roadmap
The WikiArt dataset used for fine-tuning could be found <ahref="https://huggingface.co/datasets/huggan/wikiart">here</a>
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
## Steps
State if you are open to contributions and what your requirements are for accepting them.
To reproduce the steps first you need to finetune models for genre, style and artist classification on WikiArt dataset or used fine-tuned adapters. Then, using fine-tuned models you need to calculate embeddings for the images from WikiArt dataset and create ANNOY indexes. These files are available upon a request.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
## Citation
```
@InProceedings{Yemelianenko_2024_MTA,
author = {Yemelianenko, Tetiana and Tkachenko, Iuliia and Masclef, Tess and Scuturici, Mihaela and Miguet, Serge},
title = {Artwork recommendations guided by foundation models: survey and novel approach},
booktitle = {},
month = {},
year = {},
pages = {}
}
```
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Acknowledgments
This work was funded by french national research agency with grant ANR-20-CE38-0017. We would like to thank the PAUSE ANR-Program: Ukrainian scientists support to support the scientific stay of T. Yemelianenko in LIRIS laboratory.
Show your appreciation to those who have contributed to the project.
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