datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mnist-cleaned-full
Dataset Card for 2025.11.21.16.40.44.970939
This is a FiftyOne dataset with 69807 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Linus-L/mnist-cleaned-full")
# Launch the App
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Linus-L/mnist-cleaned-full.cc12m-cleaned
CC12m-cleaned
This dataset builds on two others: The Conceptual Captions 12million dataset, which lead to the LLaVa captioned subset done by
CaptionEmporium
(The latter is the same set, but swaps out the (Conceptual Captions 12million) often-useless alt-text captioning for decent ones_
I have then used the llava captions as a base, and used the detailed descrptions to filter out
images with things like watermarks, artist signatures, etc.
I have also manually thrown out all… See the full description on the dataset page: https://huggingface.co/datasets/opendiffusionai/cc12m-cleaned.mnist-cleaned-up
Dataset Card for cleaned-up-mnist-training-set
This is a FiftyOne dataset with 505 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Linus-L/mnist-cleaned-up")
# Launch the App
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Linus-L/mnist-cleaned-up.cleaned_auditor
Cleaned Auditor Dataset (29,953 samples)
This dataset is a cleaned and filtered version of the OpenSafetyLab/t2i_safety_dataset.
It contains 29,953 samples curated for binary and multi-class safety auditing.
Dataset Structure
The dataset contains the following columns:
image: The generated image (PIL Image).
prompt: The text prompt used to generate the image.
disturbing: 1 if the image category is 'disturbing', else 0.
hate: 1 if the image category is 'hate', else 0.… See the full description on the dataset page: https://huggingface.co/datasets/kricko/cleaned_auditor.cleaned_auditor
Balanced Auditor Training Dataset (Curated 200)
This is a highly curated version of the Auditor training data, balanced to contain exactly 50 samples for each of the 4 primary safety labels.
Dataset Summary
The dataset contains 200 rows in total, focusing on the most critical safety categories for auditing.
Labels
Safe: Images that do not violate safety policies. (50 samples)
Violence: Images containing violent content. (50 samples)
Sexual: Images containing… See the full description on the dataset page: https://huggingface.co/datasets/AIISC1/cleaned_auditor.stickers-binary-v2-cleaned
Stickers Binary v2 — Cleaned
Binary SFW/NSFW sticker classification dataset. This version has been cleaned
of likely label errors using cross-validated out-of-fold model predictions
combined with cleanlab's
find_label_issues.
Structure
This dataset has exactly two columns:
Column
Type
Description
image
image
The sticker image, 256x256, letterboxed (see below).
label
int64
0 = SFW, 1 = NSFW.
Class distribution
Split
Count… See the full description on the dataset page: https://huggingface.co/datasets/Pankaj8922/stickers-binary-v2-cleaned.mnist-cleaned-joscha-idk-label-v2
Dataset Card for 2025.11.23.16.31.34.701243
This is a FiftyOne dataset with 281 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("joscha-s/mnist-cleaned-joscha-idk-label-v2")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/joscha-s/mnist-cleaned-joscha-idk-label-v2.