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>.
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>.
# Table of content
-[Overview](#description)
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@@ -26,7 +26,7 @@ The code for fine-tuning a classification model is based on the <a href="https:/
The WikiArt dataset used for fine-tuning could be found <ahref="https://huggingface.co/datasets/huggan/wikiart">here</a>
## Steps
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.
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. Due to the relatively big size, these files are available upon a request.
