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
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.
We use pytorch v0.5.0 and python 3.5.2 in our experiments.
You need to download the Recipe1M dataset from here first.
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.
Test the model:
CUDA_VISIBLE_DEVICES=0 python test.py
Pre-trained models can be downloaded from Google Drive.