This repository provides the PyTorch source code of the PARNet proposed in the following paper:
Mining Discriminative Food Regions for Accurate Food Recognition
Jianing Qiu, Frank P.-W. Lo, Yingnan Sun, Siyao Wang, Benny Lo
Spotlight at BMVC 2019
- opencv-python
- pytorch>=0.4.1
- matplotlib
- scikit-image
The code was initially written in pytorch 0.4.1, but has been tested recently and can run with pytorch 1.13.0 and CUDA 11.6 on ubuntu 20.04 as well.
Please use the link provided in the data folder to download the Sushi-50 dataset proposed in this paper, and put it in the data folder afterwards. For Food-101 and Vireo-172, please follow their respective instruction for downloading, and put them in the data folder as well.
If you find this code useful for your research, please consider citing:
@inProceedings{qiu2019mining,
title={Mining Discriminative Food Regions for Accurate Food Recognition},
author={Qiu, Jianing and Lo, Frank Po Wen and Sun, Yingnan and Wang, Siyao and Lo, Benny},
booktitle={British Machine Vision Conference (BMVC)},
year={2019}
}
This work is supported by the Innovative Passive Dietary Monitoring Project funded by the Bill & Melinda Gates Foundation (Opportunity ID: OPP1171395).