datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
reef-guidance-system
Dataset Card for Reef Guidance System
This dataset provides imagery used for training and evaluation of models in the Reef Guidance System. All imagery was collected by the Australian Institute of Marine Science using the ReefScan™ Transom Marine Monitoring System.
If you use this dataset in your work, please cite the associated paper: AI-driven dispensing of coral reseeding devices for broad-scale restoration of the Great Barrier Reef (citations provided at bottom of this… See the full description on the dataset page: https://huggingface.co/datasets/QCR-Underwater-Perception/reef-guidance-system.casia-char-1
CASIA Character Sample Dataset
This dataset is adapted from CASIA Online and Offline Chinese Handwriting Databases,
but this only contains character level sample data (from the offline database). The first column is the ground truth label (single character from
GB2312 charset) and the second one is byte sequences of the decoded PNG files from the original .gnt files.
Conditions of Academic Use
Please refer to the official page for more information.
All samples in the… See the full description on the dataset page: https://huggingface.co/datasets/UndefinedCpp/casia-char-1.underworld_dataset_v2
_ _ _ _______ ___________ _ _ ___________ _ ______
| | | | \ | | _ \ ___| ___ \ | | || _ | ___ \ | | _ \
| | | | \| | | | | |__ | |_/ / | | || | | | |_/ / | | | | |
| | | | . ` | | | | __|| /| |/\| || | | | /| | | | | |
| |_| | |\ | |/ /| |___| |\ \\ /\ /\ \_/ / |\ \| |___| |/ /
\___/\_| \_/___/ \____/\_| \_|\/ \/ \___/\_| \_\_____/___/
Underworld Dataset v2
Generated with WEBXOS UNDERWORLD LANDSCAPE GENERATOR… See the full description on the dataset page: https://huggingface.co/datasets/webxos/underworld_dataset_v2.dermatology-dataset-acne-redness-and-bags-under-the-eyes
Skin Defects Dataset
The dataset contains images of individuals with various skin conditions: acne, skin redness, and bags under the eyes. Each person is represented by 3 images showcasing their specific skin issue. The dataset encompasses diverse demographics, age, ethnicities, and genders.
The dataset is created on the basis of Facial Skin Condition Dataset
Types of defects in the dataset: acne, skin redness & bags under the eyes
Acne photos: display different… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/dermatology-dataset-acne-redness-and-bags-under-the-eyes.underworld_dataset_v3
_ _ _ _______ ___________ _ _ ___________ _ ______
| | | | \ | | _ \ ___| ___ \ | | || _ | ___ \ | | _ \
| | | | \| | | | | |__ | |_/ / | | || | | | |_/ / | | | | |
| | | | . ` | | | | __|| /| |/\| || | | | /| | | | | |
| |_| | |\ | |/ /| |___| |\ \\ /\ /\ \_/ / |\ \| |___| |/ /
\___/\_| \_/___/ \____/\_| \_|\/ \/ \___/\_| \_\_____/___/
UNDERWORLD Dataset v3
Visualizes the Fast Inverse Square Root (FISR / Quake III)… See the full description on the dataset page: https://huggingface.co/datasets/webxos/underworld_dataset_v3.siglip-doc-understanding-classifier
SigLIP Doc Understanding — Unanswerable Question Detection Dataset
A mixed answerable / unanswerable benchmark dataset built from DocVQA and MP-DocVQA, used to
train and evaluate the siglip-doc-understanding-classifier
unanswerable-question detector.
Each row pairs a document image with a question. Half of the questions are the original,
answerable DocVQA/MP-DocVQA questions; the other half are corrupted versions of those same
questions — modified so the document image no longer… See the full description on the dataset page: https://huggingface.co/datasets/giacolees/siglip-doc-understanding-classifier.Safe_Unsafe_Test-Understanding-output-labels-qwen
Dataset Card for Safe-Unsafe-Video_Understanding
This is a FiftyOne dataset with 40 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("pjramg/Safe_Unsafe_Test-Understanding-output-labels-qwen")
# Launch the App
session =… See the full description on the dataset page: https://huggingface.co/datasets/pjramg/Safe_Unsafe_Test-Understanding-output-labels-qwen.