Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label siamtech-food10@8648e26adba63fc15e71b1ffa8bfb3fa6c322767
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label siamtech-food10@8648e26adba63fc15e71b1ffa8bfb3fa6c322767Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SiamTech Food-10 — Thai Food Classification Workshop Dataset
A 10-class teaching subset of THFOOD-50, prepared for the one-day course "AI in the Real World" (Computer Engineering, Kasetsart University Kamphaeng Saen, August 2026 — instructor: Phumapiwat Chanyutthagorn). Images are center-cropped and resized to 256×256.
Contents: siamtech_food10.zip → train/<Class>/*.jpg (130/class) and test/<Class>/*.jpg (20–57/class), 1,590 images total, plus labels_th.json (Thai display names) and README.txt (license & citations).
Classes: FriedChicken (ไก่ทอด), GaengKeawWan (แกงเขียวหวาน), KaoManGai (ข้าวมันไก่), KhaoNiewMaMuang (ข้าวเหนียวมะม่วง), KuayTeowReua (ก๋วยเตี๋ยวเรือ), PadThai (ผัดไทย), PhatKaphrao (ผัดกะเพรา), Somtam (ส้มตำ), StewedPorkLeg (ข้าวขาหมู), TomYumGoong (ต้มยำกุ้ง)
License
THFOOD-50 and this subset are for non-commercial research and educational use only. Source: chakkritte/THFOOD-50 (15,770 images, 50 Thai dishes).
Citation
If you use this data, please cite the original THFOOD papers:
@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}
}
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