Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
| pretty_name: harmful-contents | |
| tags: | |
| - image-classification | |
| - multi-label-classification | |
| - computer-vision | |
| - content-moderation | |
| task_categories: | |
| - image-classification | |
| language: | |
| - en | |
| size_categories: | |
| - 1K<n<10K | |
| annotations_creators: | |
| - expert-generated | |
| source_datasets: | |
| - original | |
| license: other | |
| license_name: research-and-non-commercial-use | |
| license_link: https://huggingface.co/datasets/onullusoy/harmful-contents | |
| # 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` | |
| ```python | |
| 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. | |