datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OpenFake
Dataset Card for OpenFake
Known issues
Prompt–image misalignment in the synthetic split (reported November 2025, fix pending)
For five of the eighty generators, the prompt field attached to synthetic
images does not correspond to the prompt actually used to generate that image.
Affected generators:
flux-realism
sd-3.5
sdxl-realvis-v5
sd-1.5-dreamshaper
sd-1.5-epicdream
This affects approximately 19.77% of synthetic images. It was first reported in
discussion… See the full description on the dataset page: https://huggingface.co/datasets/ComplexDataLab/OpenFake.oxford-iiit-pet
The Oxford-IIIT Pet Dataset
Description
A 37 category pet dataset with roughly 200 images for each class. The images have a large variations in scale, pose and lighting.
This instance of the dataset uses standard label ordering and includes the standard train/test splits. Trimaps and bbox are not included, but there is an image_id field that can be used to reference those annotations from official metadata.
Website: https://www.robots.ox.ac.uk/~vgg/data/pets/… See the full description on the dataset page: https://huggingface.co/datasets/timm/oxford-iiit-pet.hyperspectral-orchard
Living Optics Orchard Dataset
Overview
This dataset contains 435 images of captured in one of the UK's largest orchards, using the Living Optics Camera.
The data consists of RGB images, sparse spectral samples and instance segmentation masks.
The dataset is derived from 44 unique raw files corresponding to 435 frames.
Therefore, multiple frames could originate from the same raw file.
This structure emphasized the need for a split strategy that avoided data leakage.
To… See the full description on the dataset page: https://huggingface.co/datasets/LivingOptics/hyperspectral-orchard.open-vision-banana-snvc-train-full
SNVC-50M v5_full — Multi-Task Vision Dataset
Description
This dataset is a curated subset of the SenseNova Vision Corpus 50M (SNVC-50M), containing 43,509 samples across 6 vision task families and 31 source datasets. Each sample follows a conversational format with interleaved <image> tokens, designed for training vision-language models (VLMs).
Coverage: 43,509 / 57,878 (75.2%) of the original sampling plan. 23 datasets at 100%, 8 partial, 12 unrecoverable… See the full description on the dataset page: https://huggingface.co/datasets/gatilin/open-vision-banana-snvc-train-full.amazon-berkeley-objects
Amazon Berkeley Objects (ABO)
A Hugging Face packaging of the Amazon Berkeley Objects (ABO) dataset. The
data content is the official CC BY 4.0 release from
https://amazon-berkeley-objects.s3.amazonaws.com/index.html. This mirror
changes only the packaging: files are grouped into typed Parquet shards, and
every original media file is preserved byte-for-byte and never transcoded.
Images use the datasets Image() feature, 3D product models use the native
Mesh() feature (original… See the full description on the dataset page: https://huggingface.co/datasets/suvadityamuk/amazon-berkeley-objects.oxford-flowers
Dataset Card for "oxford-flowers"
More Information needed
CommunityForensics-Small
Community Forensics: Using Thousands of Generators to Train Fake Image Detectors (CVPR 2025)
Paper / Project Page / Code (GitHub)
This is a small version of the Community Forensics dataset. It contains roughly 11% of the generated images of the base dataset and is paired with real data with redistributable license. This dataset is intended for easier prototyping as you do not have to download the corresponding real datasets separately.
We distribute this dataset with a… See the full description on the dataset page: https://huggingface.co/datasets/OwensLab/CommunityForensics-Small.food-dataset
Food Dataset
An image classification dataset of food photos organized into 201 categories (folders), with 35,046 images total (~924 MB).
Each top-level folder is a category (e.g. adana kebab, sushi, waffles, tiramisu, ...) containing JPEG images of that food/dish. This follows the standard Hugging Face imagefolder layout, so it loads directly with:
from datasets import load_dataset
ds = load_dataset("webbrain-one/food-dataset")
Structure
<category… See the full description on the dataset page: https://huggingface.co/datasets/webbrain-one/food-dataset.CommunityForensics
Community Forensics: Using Thousands of Generators to Train Fake Image Detectors (CVPR 2025)
Paper / Project Page / Code (GitHub)
Please also check our Community Forensics-Small dataset, which contains approximately 11% of the base dataset and is paired with real data with redistributable licenses.
Please check Community Forensics-Eval for the recommended 'comprehensive' evaluation set.
Changes: 10/18/25: Released the 'comprehensive' evaluation set used in paper. 10/06/25:… See the full description on the dataset page: https://huggingface.co/datasets/OwensLab/CommunityForensics.OpenSDI_trainThis repository contains the OpenSDI training dataset, presented in the paper OpenSDI: Spotting Diffusion-Generated Images in the Open World.
Code: https://github.com/iamwangyabin/OpenSDI
Obshazard-bench
ObsCrisis-Bench
A multimodal benchmark for evaluating large vision-language models on extreme weather event analysis tasks.
Dataset Description
ObsCrisis-Bench contains 4,202 VQA samples across 127 extreme weather events in 8 disaster categories, covering 61 countries. Each sample combines satellite multispectral imagery (AMSU-A, HIRS, MHS sensors) with optional weather station data, and requires models to perform risk assessment, type classification, timing… See the full description on the dataset page: https://huggingface.co/datasets/YYQ898/Obshazard-bench.Omni-Fake-SET
Omni-Fake-SET
Omni-Fake-SET is the in-distribution split of Omni-Fake, a unified multimodal deepfake dataset for social-media forensics. It covers image, audio, video, and audio–video talking-head (AV-TH) modalities. Each modality uses the same three-way label space: real, fully synthetic, and tampered. Pair with the held-out benchmark Omni-Fake-OOD for out-of-distribution evaluation.
Paper: arXiv:2605.01638
Project page: Omni-Fake
License: CC-BY-4.0
Video (hybrid… See the full description on the dataset page: https://huggingface.co/datasets/JamalLee/Omni-Fake-SET.OxfordFlowers102
Dataset Card for Oxford Flowers 102
This is a FiftyOne dataset with 8189 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/OxfordFlowers102")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/OxfordFlowers102.OpenSDI_test
OpenSDI: Spotting Diffusion-Generated Images in the Open World
This dataset is designed to address the OpenSDI challenge: spotting diffusion-generated images in realistic, open-world scenarios. It is described in the paper:
Project Page: https://iamwangyabin.github.io/OpenSDI/
OpenSDID Dataset Highlights:
User Diversity: Simulates a wide range of user intentions and creative styles using diverse text prompts generated by VLMs.
Model Innovation: Includes images from multiple… See the full description on the dataset page: https://huggingface.co/datasets/nebula/OpenSDI_test.IDLE-OO-Camera-Traps
Dataset Card for IDLE-OO Camera Traps
IDLE-OO Camera Traps is a 5-dataset benchmark of camera trap images from the Labeled Information Library of Alexandria: Biology and Conservation (LILA BC) with a total of 2,586 images for species classification. Each of the 5 benchmarks is balanced to have the same number of images for each species within it (between 310 and 1120 images), representing between 16 and 39 species.
Supported Tasks and Leaderboards
Image… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps.Office-Home
Dataset Card for Office-Home
This is a FiftyOne dataset with 15588 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Office-Home")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Office-Home.OmniFake
OmniFake
OmniFake is a large-scale, well-categorized synthetic image dataset introduced in Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples. It contains 1.17 million AI-generated images from 45 distinct generators, paired with 1.17 million real images, designed for research on AI-generated image (AIGI) detection and source attribution.
For usage instructions and experimental protocols, please refer to the OmniDFA GitHub repository.… See the full description on the dataset page: https://huggingface.co/datasets/MoeNew/OmniFake.gui-odyssey-train
Dataset Card for GUI Odyssey (Train Split)
⬆️ Test split shown above, but this also represents the train split.
This is a FiftyOne dataset with 89365 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("Voxel51/gui-odyssey-train")
# Launch… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/gui-odyssey-train.So-Fake-OOD
So-Fake-OOD-v3
So-Fake-OOD-v3 is the updated out-of-distribution evaluation split for So-Fake. It contains platform-native real images, fully synthetic images from held-out commercial generators, and locally tampered images with pixel-level masks.
Release Structure
The release is split according to platform redistribution constraints:
test_image: examples whose image content can be redistributed. This split includes Reddit, Tumblr, and Bluesky real images, all… See the full description on the dataset page: https://huggingface.co/datasets/saberzl/So-Fake-OOD.Omni-Fake-OOD
Omni-Fake-OOD
Omni-Fake-OOD is the out-of-distribution benchmark split of Omni-Fake. Samples come from held-out generators and platforms not included in training, for measuring cross-domain generalization. It covers image, audio, video, and audio–video talking-head (AV-TH) with the same three-class labels as Omni-Fake-SET: real, fully synthetic, and tampered. Use together with Omni-Fake-SET (in-distribution training data).
Paper: arXiv:2605.01638
Project page: Omni-Fake… See the full description on the dataset page: https://huggingface.co/datasets/JamalLee/Omni-Fake-OOD.OpenGameArt-CC0
Dataset Card for OpenGameArt-CC0
Dataset Summary
This dataset contains game artwork assets collected from OpenGameArt.org that are specifically released under the Creative Commons 0 (CC0) license, making them effectively public domain works. The dataset includes various types of game assets such as 2D art, 3D art, concept art, music, sound effects, textures, and documents along with their associated metadata.
Languages
The dataset is primarily monolingual:… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/OpenGameArt-CC0.gui-odyssey-test
Dataset Card for GUI Odyssey (Test Split)
This is a FiftyOne dataset with 29426 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("Voxel51/gui-odyssey-test")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/gui-odyssey-test.scanned-images-dataset-for-ocr-and-vlm-finetuning
Dataset Card for scanned_images_dataset
This is a FiftyOne dataset containing 3,482 scanned document images across 10 diverse document categories. Designed for OCR training and Vision-Language Model (VLM) fine-tuning, this dataset features real-world scanned documents with varied layouts, scanning quality, and document types.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/scanned-images-dataset-for-ocr-and-vlm-finetuning.plism-dataset-tiles
PLISM dataset
The Pathology Images of Scanners and Mobilephones (PLISM) dataset was created by (Ochi et al., 2024) for the evaluation of AI models’ robustness to inter-institutional domain shifts.
All histopathological specimens used in creating the PLISM dataset were sourced from patients who were diagnosed and underwent surgery at the University of Tokyo Hospital between 1955 and 2018.
PLISM-wsi consists in a group of consecutive slides digitized under 7 different scanners and… See the full description on the dataset page: https://huggingface.co/datasets/owkin/plism-dataset-tiles.bo_or_not
Dataset Card for bo-dataset
This is a FiftyOne dataset with 169 samples designed for binary classification of Bo (Barack Obama's Portuguese Water Dog) versus other pets.
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("Voxel51/bo_or_not")
#… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/bo_or_not.ImageNet-O
Dataset Card for ImageNet-O
This is a FiftyOne dataset with 2000 samples.
The recipe notebook for creating this dataset can be found here.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/ImageNet-O")
# Launch the App
session =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/ImageNet-O.Garbage_Bin_overflow_images
Garbage Bin Overflow Images Dataset
This dataset contains real-world images of overflowing garbage bins, compiled from the Roboflow Universe dataset by Mariswary Deepak.
Details
Total Images: 1974
Format: Flat folder containing Garbage_Bin_overflow_xxxxx.jpg
Use Case: Training object detection and classification models to identify overflowing trash cans and public waste management issues.
gym_exercices
Gym Exercises Dataset 💪
Bienvenue dans le Gym Exercises Dataset !
Ce dataset regroupe une collection d'exercices de sport et de musculation.
📌 Particularité du Dataset
Pour chaque exercice, vous trouverez deux fichiers correspondants :
🖼️ Une image fixe (.jpg) : Montrant l'exercice.
🎬 Une animation (.gif) : Montrant le mouvement complet de l'exercice pour bien comprendre l'exécution.
Les deux fichiers partagent exactement le même nom, ce qui permet de les… See the full description on the dataset page: https://huggingface.co/datasets/Nouira-Oussema/gym_exercices.planktonzilla-17M
Planktonzilla-17M Dataset
Overview
planktonzilla-17M is a large-scale, comprehensive dataset combining 17 million plankton images from all publicly available -to the best
of our knowledge- labeled plankton datasets. This unified collection enables researchers to train robust deep learning models for plankton
identification and classification across diverse imaging systems and oceanographic environments.
Each image includes a standardized taxonomic hierarchy… See the full description on the dataset page: https://huggingface.co/datasets/project-oceania/planktonzilla-17M.oxford-pets
Oxford-IIIT Pet Dataset
Images from The Oxford-IIIT Pet Dataset. Only images and labels have been pushed, segmentation annotations were ignored.
Homepage: https://www.robots.ox.ac.uk/~vgg/data/pets/
License:
Same as the original dataset.