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Adversarial Networks for Cross-Modal Food Retrieval

Codes of ACME (PyTorch)

Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
Wang Hao, Doyen Sahoo, Chenghao Liu, Ee-peng Lim, Steven C. H. Hoi
CVPR 2019

outline

If you find this code useful, please consider citing:

@inproceedings{wang2019learning,
  title={Learning Cross-Modal Embeddings With Adversarial Networks for Cooking Recipes and Food Images},
  author={Wang, Hao and Sahoo, Doyen and Liu, Chenghao and Lim, Ee-peng and Hoi, Steven CH},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={11572--11581},
  year={2019}
}

Our work is an extension of im2recipe, where you can borrow some food data pre-processing methods.

Installation

We use pytorch v0.5.0 and python 3.5.2 in our experiments.
You need to download the Recipe1M dataset from here first.

Training

Train the ACME model:

CUDA_VISIBLE_DEVICES=0 python train.py 

We did the experiments with batch size 64, which takes about 12 GB memory.

Model for Testing

Test the model:

CUDA_VISIBLE_DEVICES=0 python test.py

Pre-trained models can be downloaded from Google Drive.

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Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images

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