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CalorieCamV2

This application measures real area of food using AR technology mounted on iphone and applied it to estimate calorie content.

Demo

Youtube link is here

Dependencies

  • Swift >= 4.0
  • iOS >= 11.0
  • Xcode >= 9.0

Install

  1. git clone https://github.com/negi111111/CalorieCamV2.git
  2. pod install
  3. Compile project with Xcode

Citation

If you use this app in a publication, a link to or citation of this repository would be appreciated.

@misc{
  author = {Ryosuke Tanno},
  title = {CalorieCamV2},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/negi111111/CalorieCamV2}},
}

Recognition Performance

Top–1 and Top–5 performance on the UECFood100 dataset. First 4 rows show the results achieved by using methods adopting hand-crafted features. Next 11 rows show the performance obtained by deep learningbased approaches on the ground-truth cropped images. Last 2 rows depict the results obtained considering images having more than a single food class (i.e., no ground truth is exploited). Best results is highlighted in boldface font.

Method Top-1 Top-5 Publication
DeepFoodCam 72.26 92.00 UBICOMP2014[1]
AlexNet 75.62 92.43 ACMMM2016[2]
DeepFood 76.3 94.6 COST2016 [3]
FV+DeepFoodCam 77.35 94.85 UBICOMP2014[1]
DCNN-FOOD 78.77 95.15 ICME2015[4]
VGG 81.31 96.72 ACMMM2016[5]
Inception V3 81.45 97.27 ECCVW2016[6]
Arch-D 82.12 97.29 ACMMM2016[7]
ResNet-200 86.25 98.91 CVPR2016[8]
WRN 86.71 98.92 BMVC2016[9]
WISeR 89.58 99.23 arXiv[10]
Inception-V4 ??? ??? arXiv[11]
NASNet-A ??? ??? arXiv[12]

Publications

[1]Y. Kawano and K. Yanai. Food Image Recognition with Deep Convolutional Features. In ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014.
[2] J. Chen and C.-W. Ngo. Deep-based Ingredient Recognition for Cooking Recipe Retrieval. In ACM Multimedia, 2016.
[3]C. Liu, Y. Cao, Y. Luo, G. Chen, V. Vokkarane, and Y. Ma. Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment. In IEEE International Conference on Smart Homes and Health Telematics, volume 9677, pages 37–48, 2016.
[4]K. Yanai and Y. Kawano. Food image recognition using deep convolutional network with pre-training and fine-tuning. In International Conference on Multimedia & Expo Workshops, 2015.
[5]J. Chen and C.-W. Ngo. Deep-based Ingredient Recognition for Cooking Recipe Retrieval. In ACM Multimedia, 2016.
[6]H. Hassannejad, G. Matrella, P. Ciampolini, I. De Munari, M. Mordonini, and S. Cagnoni. Food Image Recognition Using Very Deep Convolutional Networks. In European Conference Computer Vision Workshops and Demonstrations, 2016.
[7] J. Chen and C.-W. Ngo. Deep-based Ingredient Recognition for Cooking Recipe Retrieval. In ACM Multimedia, 2016.
[8]K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. In International Conference on Computer Vision and Pattern Recognition, 2016.
[9]S. Zagoruyko and N. Komodakis. Wide Residual Networks. In British Machine Vision Conference, 2016.
[10]Ma. Niki, G. L. Foresti, and C. Micheloni. Wide-Slice Residual Networks for Food Recognition, arXiv preprint arXiv:1612.06543, 2016.
[11]C. Szegedy, S. Ioffe, V. Vanhoucke and A Alemi. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, arXiv preprint arXiv:1602.07261, 2016.
[12]B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le. Learning Transferable Architectures for Scalable Image Recognition, arXiv preprint arXiv:1707.07012, 2017.

Thanks

License

MIT. Copyright (c) 2018 Ryosuke Tanno