The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: SyntaxError
Message: not a TIFF file (header b'Exif\x00\x00II' not valid)
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2061, in __iter__
batch = formatter.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2161, in decode_batch
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1419, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 194, in decode_example
if image.getexif().get(PIL.Image.ExifTags.Base.Orientation) is not None:
^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 1539, in getexif
self._exif.load(exif_info)
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3937, in load
self._info = TiffImagePlugin.ImageFileDirectory_v2(self.head)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/TiffImagePlugin.py", line 572, in __init__
raise SyntaxError(msg)
SyntaxError: not a TIFF file (header b'Exif\x00\x00II' not valid)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Harmful-Contents Dataset
A multi-label image dataset for harmful-content classification across eight PEGI-aligned categories.
The dataset consists of 5,153 rights-cleared images, split into train/validation/test sets and annotated with both binary labels and mask fields for controlled negative sampling.
Dataset Structure
Harmful-Contents/
csv/
train.csv
val.csv
test.csv
data/
train/*.jpg
val/*.jpg
test/*.jpg
Each CSV contains:
name,
alcohol,drugs,weapons,gambling,nudity,sexy,smoking,violence,
mask_alcohol,mask_drugs,mask_weapons,mask_gambling,
mask_nudity,mask_sexy,mask_smoking,mask_violence
Images are stored in data/{train,val,test}/ and referenced by name.
Categories
| Category | Unsafe Examples | Safe Examples |
|---|---|---|
| alcohol | Alcohol bottles/glasses, alcohol brand logos | Empty glasses, non-alcoholic drinks |
| drugs | Cannabis, cocaine, pills, paraphernalia | OTC medication, neutral plants |
| weapons | Firearms, combat/attack knives, explosives | Kitchen knives, fruit knives, toy props |
| gambling | Casinos, slot machines, gambling chips/coins | Money, clovers, normal playing cards |
| nudity | Nudity, explicit sexual acts, pornography | Non-explicit partially clothed persons |
| sexy | Lingerie/underwear, sexualized posing | Sportswear, non-sexual clothing |
| smoking | Cigarettes, cigars, active smoking | Cigarette-like objects, steam/steam unrelated to smoking |
| violence | Blood, fighting, visible injury, aggression | Red liquids, non-violent crowds, hugging |
Base Source (SIMAS)
The dataset is built using the SIMAS collection (Spam Images for Malicious Annotation Set) as the primary seed:
https://zenodo.org/records/15423637
Additional rights-cleared images were added to improve class balance, yielding the final 5,153-image dataset described in the associated thesis.
Loading With Hugging Face datasets
from datasets import load_dataset, Image
data_files = {
"train": "csv/train.csv",
"validation": "csv/val.csv",
"test": "csv/test.csv",
}
ds = load_dataset("csv", data_files=data_files)
def add_path(example, split):
return {"image_path": f"data/{split}/{example['name']}"}
for split in ["train", "validation", "test"]:
ds[split] = ds[split].map(lambda x, idx, s=split: add_path(x, s), with_indices=True)
ds[split] = ds[split].cast_column("image_path", Image())
License
Images are rights-cleared for research and non-commercial use.
Commercial usage requires independent rights verification.
Citation
If you use this dataset, please cite:
Ulusoy, O.
Evaluating and Fine-Tuning Vision Models for Keyword-Driven Content Filtering.
Bachelor Thesis, Flensburg University of Applied Sciences, 2025.
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