deercoder / DeepFood

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

Guide

How to run the experiments using AlexNet/GoogLeNet on Food-101?

• clone this repo from scratch: git clone https://github.com/deercoder/DeepFood.git
• configure the environment according to the official tutorial. Minor changes have been applied in this repo.
• download pre-trained model(alexNet, googleNet), under the ./models folder
• download imagenet mean file, under data/ilsvrc12/ folder with the get_ilsvrc_aux.sh
• run the model from the caffe's root directory, with ./models/finetune-food101-alexNet/train_full.sh or ./models/finetune-food101-googlenet/train_full.sh, check results!

License and Citation

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}

Please cite DeepFood in your publications if it helps your research:

@inproceedings{liu2016deepfood,
  title={DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment},
  author={Liu, Chang and Cao, Yu and Luo, Yan and Chen, Guanling and Vokkarane, Vinod and Ma, Yunsheng},
  booktitle={International Conference on Smart Homes and Health Telematics},
  pages={37--48},
  year={2016},
  organization={Springer International Publishing}
}