THFOOD-50 / README.md
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---
license: afl-3.0
pretty_name: Fine-Grained Thai Food Image Classification Datasets.
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': BitterMelonSoup
'1': BooPadPongali
'2': CurriedFishCake
'3': Dumpling
'4': EggsStewed
'5': FriedChicken
'6': FriedKale
'7': FriedMusselPancakes
'8': GaengJued
'9': GaengKeawWan
'10': GaiYang
'11': GoongObWoonSen
'12': GoongPao
'13': GrilledQquid
'14': HoyKraeng
'15': HoyLaiPrikPao
'16': Joke
'17': KaiJeowMooSaap
'18': KaiThoon
'19': KaoManGai
'20': KaoMooDang
'21': KhanomJeenNamYaKati
'22': KhaoMokGai
'23': KhaoMooTodGratiem
'24': KhaoNiewMaMuang
'25': KkaoKlukKaphi
'26': KorMooYang
'27': KuaKling
'28': KuayJab
'29': KuayTeowReua
'30': LarbMoo
'31': MassamanGai
'32': MooSatay
'33': NamTokMoo
'34': PadPakBung
'35': PadPakRuamMit
'36': PadThai
'37': PadYordMala
'38': PhatKaphrao
'39': PorkStickyNoodles
'40': Roast_duck
'41': Roast_fish
'42': Somtam
'43': SonInLawEggs
'44': StewedPorkLeg
'45': Suki
'46': TomKhaGai
'47': TomYumGoong
'48': YamWoonSen
'49': Yentafo
splits:
- name: train
num_bytes: 1790570028.695
num_examples: 12065
- name: test
num_bytes: 394634675.44
num_examples: 2105
- name: val
num_bytes: 295187724.2
num_examples: 1600
download_size: 3125698089
dataset_size: 2480392428.3349996
---
# THFOOD-50
Fine-Grained Thai Food Image Classification Datasets
THFOOD-50 containing 15,770 images of 50 famous Thai dishes.
## Download:
[THFOOD-50 v1 on Google Drive](https://drive.google.com/file/d/1CuNO2e77ZTk7mDfv3XujYXuUwiMwlUQI/view?usp=sharing)
## License
THFOOD-50 for **non-commercial research/educational** use.
## Citation
If you use THFOOD-50 dataset in your research, please cite our paper:
@article{termritthikun2017nu,
title="{NU-InNet: Thai food image recognition using convolutional neural networks on smartphone}",
author={Termritthikun, Chakkrit and Muneesawang, Paisarn and Kanprachar, Surachet},
journal={Journal of Telecommunication, Electronic and Computer Engineering (JTEC)},
volume={9},
number={2-6},
pages={63--67},
year={2017}
}
@inproceedings{termritthikun2017accuracy,
title="{Accuracy improvement of Thai food image recognition using deep convolutional neural networks}",
author={Termritthikun, Chakkrit and Kanprachar, Surachet},
booktitle={2017 international electrical engineering congress (IEECON)},
pages={1--4},
year={2017},
organization={IEEE}
}
@article{termritthikun2018nu,
title="{Nu-ResNet: Deep residual networks for Thai food image recognition}",
author={Termritthikun, Chakkrit and Kanprachar, Surachet},
journal={Journal of Telecommunication, Electronic and Computer Engineering (JTEC)},
volume={10},
number={1-4},
pages={29--33},
year={2018}
}
## Paper
1. NU-InNet: Thai food image recognition using convolutional neural networks on smartphone [Paper](https://journal.utem.edu.my/index.php/jtec/article/download/2436/1521) [Code](https://github.com/chakkritte/NU-InNet)
2. Accuracy improvement of Thai food image recognition using deep convolutional neural networks [Paper](https://ieeexplore.ieee.org/abstract/document/8075874/)
3. Nu-resnet: Deep residual networks for thai food image recognition [Paper](https://journal.utem.edu.my/index.php/jtec/article/download/3572/2467) [Code](https://github.com/chakkritte/NU-ResNet)
#### Examples of Thai food images in the THFOOD-50 dataset
![imges](https://raw.githubusercontent.com/chakkritte/NU-InNet/master/images/THFOOD.png)
**NOTE**: I do not own this, but I took the liberty to upload this dataset to the community.