More information coming soon. Please feel free to reach out to us if you have any questions.
First create the conda environment from the env.yaml file:
conda env create --name farm --file=env/env.yaml
source activate farm
Following T-Food, we use bootstrap.pytorch.
cd bootstrap.pytorch
pip install -e .
Install CLIP.
pip install git+https://github.com/openai/CLIP.git
We use the Recipe1M dataset in this work.
We adopt the evaluation code from T-Food. For the experiments with missing data, we use empty strings for the corresponding recipe components. Pre-trained models available in this link.
We would like to express our gratitude to T-Food for their incredible work on the original project. We use their code for training and evaluating the models. The code for the hyperbolic embedding loss has been adopted from UnHyperML.
If you find this method and/or code useful, please consider citing:
@inproceedings{wahed2024fine,
title={Fine-Grained Alignment for Cross-Modal Recipe Retrieval},
author={Wahed, Muntasir and Zhou, Xiaona and Yu, Tianjiao and Lourentzou, Ismini},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={5584--5593},
year={2024}
}