datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
typed_digital_signatures
Typed Digital Signatures Dataset
This comprehensive dataset contains synthetic digital signatures rendered across 30 different Google Fonts, specifically selected for their handwriting and signature-style characteristics. Each font contributes unique stylistic elements, making this dataset ideal for robust signature analysis and font recognition tasks.
Dataset Overview
Total Fonts: 30 different Google Fonts
Images per Font: 3,000 signatures
Total Dataset Size:… See the full description on the dataset page: https://huggingface.co/datasets/Benjy/typed_digital_signatures.PhysicalAI-SimReady-Warehouse-01
NVIDIA Physical AI SimReady Warehouse OpenUSD Dataset
Dataset Version: 1.1.0
Date: May 18, 2025
Author: NVIDIA, Corporation
License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
Contents
This dataset includes the following:
This README file
A CSV catalog that enumerates all of the OpenUSD assets that are part of this dataset including a sub-folder of images that showcase each 3D asset (physical_ai_simready_warehouse_01.csv). The CSV file is organized in… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-SimReady-Warehouse-01.stanford_cars
Stanford Cars Dataset
Dataset Overview
Splits:
Training: 8144 images used for model training.
Test: 8041 images used for evaluation.
Contrast: 8041 images with high contrast for robustness testing.
Gaussian Noise: 8041 images corrupted by Gaussian noise for robustness testing.
Impulse Noise: 8041 images corrupted by impulse noise for robustness testing.
JPEG Compression: 8041 compressed images for robustness testing.
Motion Blur: 8041 images with motion blur for… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/stanford_cars.game_character_skins
Game Character Skins Dataset
Summary
This comprehensive dataset contains game character skins and artwork from multiple popular mobile and PC games, providing a rich collection of character visual assets for computer vision research and game development applications. The dataset spans eight major game titles including Arknights, Azur Lane, Blue Archive, Fate/Grand Order, Genshin Impact, Girls' Frontline, Neural Cloud, Nikke, Path to Nowhere, and Honkai: Star Rail… See the full description on the dataset page: https://huggingface.co/datasets/deepghs/game_character_skins.imagenet1k-256-wdsThis is imagenet1k in webdataset format. Images are stored as jpg files. Every image has been resized to a maximum side length of 256. That means that if an image in the original dataset was 1000 by 500, the new size will be 256 by 128. Images with a maximum side length of under 256 were not resized.
The total size of all dataset files is 57.8 GB, there are 1,281,167 rows in the training split and 50,000 rows in the validation split.
sun397
SUN397 dataset
The database contains 397 categories subset from the SUN dataset for Scene Recognition used in the following paper.
The number of images varies across categories, but there are at least 100 images per category, and 108,754 images in total.
All images are in jpg format. The images provided here are for research purposes only.
The file ClassName.txt contains the name list for the 397 categories.
Please cite the following paper if you use this dataset in your research.… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/sun397.svhn
Dataset Card for Street View House Numbers
Dataset Summary
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem… See the full description on the dataset page: https://huggingface.co/datasets/ufldl-stanford/svhn.svg-benchmark
Rapidata Static SVG Generation Benchmark
Built by Rapidata.
This dataset contains 1,918,367 human responses, collected with the
Rapidata Python SDK, comparing how well 42 frontier LLMs generate
static SVGs from text prompts. Each row is a head-to-head comparison between two models' renders of
the same prompt, scored by human annotators on one of three questions (Preference, Coherence, Alignment).
The SVGs are produced as raw <svg> markup by the models, rasterized to 768×768 PNGs… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/svg-benchmark.STimage-1K4M
STimage-1K4M Dataset
Welcome to the STimage-1K4M Dataset repository. This dataset is designed to foster research in the field of spatial transcriptomics, combining high-resolution histopathology images with detailed gene expression data.
Update
Feb 12, 2025
We corrected a typo in meta file (changed "Human_Brain+Kidney_10X_02212023_Visium" to "Mouse_Brain+Kidney_10X_02212023_Visium"). Please refer to meta_all_gene02122025.csv for the newest meta data.… See the full description on the dataset page: https://huggingface.co/datasets/jiawennnn/STimage-1K4M.GUIGuard-Bench
GUIGuard-Bench (Public Ladder)
GUIGuard-Bench is a cross-platform GUI agent benchmark for studying privacy risks and privacy-preserving execution in multimodal GUI agents.
This public-ladder release contains 121 GUI interaction trajectories (68 Android + 53 PC) for benchmark evaluation, with 26,407 region-level privacy annotations across 2,002 screenshots.
For the anonymous review version of the evaluation toolkit, see GUIGaurd-Bench-CA4F.
Dataset Summary
GUI agents… See the full description on the dataset page: https://huggingface.co/datasets/ShaofantuoshuzhengzhiSha/GUIGuard-Bench.svgrepo
Dataset Card for SVGRepo Icons
Dataset Summary
This dataset contains a large collection of Scalable Vector Graphics (SVG) icons sourced from SVGRepo.com. The icons cover a wide range of categories and styles, suitable for user interfaces, web development, presentations, and potentially for training vector graphics or icon classification models. Each icon is provided under a specific open-source or permissive license, clearly indicated in its metadata. The SVG… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/svgrepo.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.American-Sign-Language-MNIST
Dataset Card for ASL-MNIST
This is a FiftyOne dataset with 34,627 samples of American Sign Language (ASL) alphabet images, converted from the original Kaggle Sign Language MNIST dataset into a format optimized for computer vision workflows.
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… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/American-Sign-Language-MNIST.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.mahjong_souls_tiles
Dataset Card for Mahjong Souls Tiles
Dataset Details
Dataset Description
This dataset, pjura/mahjong_souls_tiles, contains images of Mahjong tiles extracted from the game Mahjong Soul. It was created specifically for the purpose of training computer vision models to recognize Mahjong tiles within the game interface.
The dataset was used to fine-tune the google/vit-base-patch16-224-in21k model, resulting in the pjura/mahjong_vision model available on Hugging… See the full description on the dataset page: https://huggingface.co/datasets/pjura/mahjong_souls_tiles.ViT-FineTunezendo-synthetic-data
Zendo Synthetic Visual Reasoning Dataset
Synthetic Zendo-style scenes with associated rules and per-scene tensor
representations. Each scene either follows ("positive", label=1) or violates
("negative", label=0) a rule that is given in natural language and as a Prolog
query.
Splits
split
scenes
train
56475
test
3344
rules total
3439
Layout
images/<split>/<batch>/<rule_id>/<scene_id>.png — rendered scene… See the full description on the dataset page: https://huggingface.co/datasets/sophia1ch/zendo-synthetic-data.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.STONE
Dataset Card for STONE
STONE is a large-scale multi-modal dataset for off-road 3D traversability prediction, collected by autonomous ground vehicles across four outdoor environments in South Korea. It provides 7,000 keyframes with surround-view imagery from 6 cameras (1904×1200), 128-channel LiDAR scans (230K points), and voxel-level traversability annotations classifying terrain into free, traversable, potentially traversable, and non-traversable regions. Following the… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/STONE.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.stable-diffusion-v1-5-glazed
Dataset Card for Stable Diffusion v1.5 Glazed Samples
Dataset Description
Dataset Summary
This dataset contains image samples originally generated by runwayml/stable-diffusion-v1-5
and subsequently processed by Glaze tool.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information… See the full description on the dataset page: https://huggingface.co/datasets/hanamizuki-ai/stable-diffusion-v1-5-glazed.SimulationMetadata
PercepTax — Simulation Metadata
Supporting metadata for the PercepTax benchmark.
This repository holds the object taxonomy, object lists / placement rules, and
per-scene 3D annotations used to render and annotate the simulated split of PercepTax.
It is a collection of JSON/CSV/PNG files (not a load_dataset-style tabular dataset).
Contents
taxonomy/ # the physical-property taxonomy
├── taxonomy.json # full taxonomy… See the full description on the dataset page: https://huggingface.co/datasets/TaxonomyProject/SimulationMetadata.SUN397
Scene UNderstanding 397 — SUN397
Description
Scene categorization is a fundamental problem in computer vision.
However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories.
Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes.
In this paper we… See the full description on the dataset page: https://huggingface.co/datasets/1aurent/SUN397.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.StanfordDogs
Dataset Card for StanfordDogs
This is a FiftyOne dataset with 20580 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/StanfordDogs")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/StanfordDogs.smart-bin-detect
arudaev/smart-bin-detect
Training data for Smart Bin Recognition – a validator ("is there a bin?")
and an identifier ("which bin?"). The design lives in docs/04-ml-pipeline.md
in the project repo, which is private; the manifests here carry per-image
provenance and are the authoritative record of what this dataset contains.
Every image carries provenance: source, source URL, licence, region,
capture date, annotator where known, label origin (human / machine /
legacy /… See the full description on the dataset page: https://huggingface.co/datasets/arudaev/smart-bin-detect.Co-Spy-Bench
CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI (CVPR 2025)
With the rapid advancement of generative AI, it is now possible to synthesize high-quality images in a few seconds. Despite the power of these technologies, they raise significant concerns regarding misuse.
To address this, various synthetic image detectors have been proposed. However, many of them struggle to generalize across diverse generation parameters and emerging generative models.
In… See the full description on the dataset page: https://huggingface.co/datasets/ruojiruoli/Co-Spy-Bench.dog-food
Dataset Card for the Dog 🐶 vs. Food 🍔 (a.k.a. Dog Food) Dataset
Dataset Summary
This is a dataset for binary image classification, between 'dog' and 'food' classes.
The 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, and the 'food' class contains images of (you guessed it) fried chicken and muffins 😋
Supported Tasks and Leaderboards
TBC
Languages
The labels are in English (['dog'… See the full description on the dataset page: https://huggingface.co/datasets/sasha/dog-food.lagenda_split
LAGENDA Dataset
This is a community mirror of the LAGENDA dataset created by LayerTeam. It has been uploaded here for easier access and integration with the Hugging Face datasets library.
All credit, rights, and accolades belong to the original authors. Please see the citation section below.
Dataset Description
LAGENDA (Large Age and Gender Dataset) is a dataset designed for age and gender recognition tasks. It addresses common biases in existing datasets by ensuring a… See the full description on the dataset page: https://huggingface.co/datasets/uaebn/lagenda_split.softTissueTumorousLesions
AutoTrain Dataset for project: soft-tissue-tumor-species
Dataset Description
This dataset has been automatically processed by AutoTrain for project bone-tumor-species.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<512x512 RGB PIL image>",
"target": 16
},
{
"image": "<512x512 RGB PIL image>",
"target": 29
}]… See the full description on the dataset page: https://huggingface.co/datasets/itslogannye/softTissueTumorousLesions.