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