This application measures real area of food using AR technology mounted on iphone and applied it to estimate calorie content.
Youtube link is here
- Swift >= 4.0
- iOS >= 11.0
- Xcode >= 9.0
git clone https://github.com/negi111111/CalorieCamV2.gitpod install- Compile project with Xcode
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}},
}
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] |
[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.
MIT. Copyright (c) 2018 Ryosuke Tanno