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CalorieCamV2

★ 25 Open GitHub ↗

Food Calorie Estimation using Deep Learning and AR

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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](https://www.youtube.com/watch?v=4fPdq_9fAYw)

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

## Publications
<a id='1'>
[1]Y. Kawano and K. Yanai. Food Image Recognition with
Deep Convolutional Features. In ACM International Joint
Conference on Pervasive and Ubiquitous Computing, 2014.
 
<a id='2'>
[2] J. Chen and C.-W. Ngo. Deep-based Ingredient Recognition
for Cooking Recipe Retrieval. In ACM Multimedia, 2016.
 
<a id='3'>
[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.
 
<a id='4'>
[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.
 
<a id='5'>
[5]J. Chen and C.-W. Ngo. Deep-based Ingredient Recognition
for Cooking Recipe Retrieval. In ACM Multimedia, 2016.
 
<a id='6'>
[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.
 
<a id='7'>
[7] J. Chen and C.-W. Ngo. Deep-based Ingredient Recognition
for Cooking Recipe Retrieval. In ACM Multimedia, 2016.
 
<a id='8'>
[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.
 
<a id='9'>
[9]S. Zagoruyko and N. Komodakis. Wide Residual Networks.
In British Machine Vision Conference, 2016.
 
<a id='10'>
[10]Ma. Niki, G. L. Foresti, and C. Micheloni. Wide-Slice Residual Networks for Food Recognition, arXiv preprint arXiv:1612.06543, 2016.
 
<a id='11'>
[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.
 
<a id='12'>
[12]B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le. Learning Transferable Architectures for Scalable Image Recognition, arXiv preprint arXiv:1707.07012, 2017.

## Thanks
- [Ruler](https://github.com/TBXark/Ruler)
- [Food101 for CoreML](https://github.com/ph1ps/Food101-CoreML)

CalorieCamV2

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