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.tiny-imagenet
Dataset Card for tiny-imagenet
Dataset Summary
Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.
Languages
The class labels in the dataset are in English.
Dataset Structure
Data Instances
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190,
'label': 15
}… See the full description on the dataset page: https://huggingface.co/datasets/zh-plus/tiny-imagenet.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.TreasureEnglish | 中文
News
Our work has been accepted by ICML 2026 as a poster. 🎉🎉
The paper has released on ArXiv.
The code has released on Github.
Treasure Dataset
Treasure is the dataset companion to Fleet (Fleet: Few Shots Lead Effective AI-generated Images Detection), designed for AI-generated image detection in an open-world setting where generators evolve and distribution shift is central. Unlike saturated results on closed datasets, Treasure/Fleet emphasizes… See the full description on the dataset page: https://huggingface.co/datasets/ThreeLiu/Treasure.TreeOfLife-200M
Dataset Card for TreeOfLife-200M
If you are looking for the original release TreeOfLife-200M dataset, as used in training BioCLIP 2 and presented the paper, please see Revision a8f38b4. The dataset, as presented here, was used to train BioCLIP 2.5 Huge; it completes the dataset cleaning process and resolves an issue where Observation.org occurrences were not included in the training data.
With 233 million images representing 933,798 taxa across the tree of life, TreeOfLife-200M… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/TreeOfLife-200M.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.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.eurosat
Dataset Card for EuroSAT
Dataset Source
Paper with code
Usage
from datasets import load_dataset
dataset = load_dataset('tranganke/eurosat')
Data Fields
The dataset contains the following fields:
image: An image in RGB format.
label: The label for the image, which is one of 10 classes:
0: annual crop land
1: forest
2: brushland or shrubland
3: highway or road
4: industrial buildings or commercial buildings
5: pasture land
6: permanent crop land… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/eurosat.gtsrb
Dataset Card for German Traffic Sign Recognition Benchmark
This dataset contains images of 43 classes of traffic signs. It is intended for developing and benchmarking traffic sign recognition systems.
Dataset Details
Dataset Description
The German Traffic Sign Recognition Benchmark (GTSRB) is a multi-class classification dataset featuring 43 classes of traffic signs.
The images were cropped from a larger set of images to focus on the traffic sign and eliminate… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/gtsrb.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.imagenet-22k-wds
Dataset Summary
This is a copy of the full ImageNet dataset consisting of all of the original 21841 clases. It also contains labels in a separate field for the '12k' subset described at at (https://github.com/rwightman/imagenet-12k, https://huggingface.co/datasets/timm/imagenet-12k-wds)
This dataset is from the original fall11 ImageNet release which has been replaced by the winter21 release which removes close to 3000 synsets containing people, a number of these are of an offensive… See the full description on the dataset page: https://huggingface.co/datasets/timm/imagenet-22k-wds.mini-imagenet
Dataset Description
A mini version of ImageNet-1k with 100 of 1000 classes present.
Unlike some 'mini' variants this one includes the original images at their original sizes. Many such subsets downsample to 84x84 or other smaller resolutions.
Data Splits
Train
50000 samples from ImageNet-1k train split
Validation
10000 samples from ImageNet-1k train split
Test
5000 samples from ImageNet-1k validation split (all 50 samples per class)… See the full description on the dataset page: https://huggingface.co/datasets/timm/mini-imagenet.TN3K
TN3K: Thyroid Nodule Dataset for Segmentation and Classification
Overview
TN3K is a comprehensive open-access thyroid nodule dataset containing 3,493 thyroid ultrasound images with high-quality annotations for both segmentation and classification tasks. The dataset addresses the critical need for diverse, multi-center thyroid imaging data collected from various ultrasound devices and scanning views, reflecting real-world clinical scenarios.
Dataset Characteristics… See the full description on the dataset page: https://huggingface.co/datasets/haifan-gong/TN3K.eurosatRedistributed without modification from https://github.com/phelber/EuroSAT.
EuroSAT100 is a subset of EuroSATallBands containing only 100 images. It is intended for tutorials and demonstrations, not for benchmarking.
Brain-Tumour-MRI
Dataset Card for Brain Tumour MRI dataset
A collection of Brain scans covering three different types of tumours and as well as a control class.
Dataset Details
Dataset Description
The Dataset contains ~7000 MRI scans of the brain corresponding to 4 classes: glioma, meningioma, notumor & pituitary.
The dataset has already been split into train/test sets.
Dataset Creation
Source
This dataset was compiled and uploaded to Kaggle by Masoud… See the full description on the dataset page: https://huggingface.co/datasets/Kaynaaf/Brain-Tumour-MRI.MNIST_train
IllusionMNIST — Training Set
Dataset summary
This repository contains the training split of IllusionMNIST, introduced in Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions. The dataset is intended for training models to recognize MNIST digits embedded as visual illusions (pareidolia) in generated scenes and to reject images that contain no illusion.
MNIST source-condition images were sampled and resized to 512 × 512 pixels, combined… See the full description on the dataset page: https://huggingface.co/datasets/VQA-Illusion/MNIST_train.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.FashionMnist_train
IllusionFashionMNIST — Training Set
Dataset summary
This repository contains the training split of IllusionFashionMNIST, one of the four datasets introduced in Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions. It is designed to train and evaluate models on the recognition of Fashion-MNIST categories embedded as visual illusions (pareidolia) in generated scenes.
The source-condition images are sampled from Fashion-MNIST and resized to… See the full description on the dataset page: https://huggingface.co/datasets/VQA-Illusion/FashionMnist_train.TAIX-Ray
TAIX-Ray Dataset
TAIX-Ray is a comprehensive dataset of approximately 200k bedside chest radiographs from around 50k intensive care patients at University Hospital Aachen, Germany, collected between 2010 and 2024.
Trained radiologists provided structured reports at the time of acquisition, assessing key findings such as cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis on an ordinal scale.
Code & Details
The code for data… See the full description on the dataset page: https://huggingface.co/datasets/TLAIM/TAIX-Ray.IllusionAnimals_train
IllusionAnimals — Training Set
Dataset summary
This repository contains the training split of IllusionAnimals, one of the four benchmarks introduced in Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions. It supports training models to identify animal categories embedded as visual illusions (pareidolia) in generated scenes and to recognize when no illusion is present.
The source-condition animal images were generated with SDXL-Lightning.… See the full description on the dataset page: https://huggingface.co/datasets/VQA-Illusion/IllusionAnimals_train.resisc45
Description
RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class.
The dataset does not have any default splits. Train, validation, and test splits were based on these definitions here… See the full description on the dataset page: https://huggingface.co/datasets/timm/resisc45.FashionMnist_test
IllusionFashionMNIST — Test Set
Dataset summary
This repository contains the public test split of IllusionFashionMNIST, introduced in Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions. Each metadata row identifies a Fashion-MNIST target and can be paired across five image conditions: source-condition, illusion, filtered illusion, illusionless control, and filtered illusionless control.
The source-condition images originate from… See the full description on the dataset page: https://huggingface.co/datasets/VQA-Illusion/FashionMnist_test.TreeOfLife-10M
Dataset Card for TreeOfLife-10M
Dataset Summary
With over 10 million images covering 454 thousand taxa in the tree of life, TreeOfLife-10M is the largest-to-date ML-ready dataset of images of biological organisms paired with their associated taxonomic labels. It expands on the foundation established by existing high-quality datasets, such as iNat21 and BIOSCAN-1M, by further incorporating newly curated images from the Encyclopedia of Life (eol.org), which supplies most of… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/TreeOfLife-10M.MNIST_test
IllusionMNIST — Test Set
Dataset summary
This repository contains the public test split of IllusionMNIST, introduced in Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions. Every indexed example can be compared across source-condition, illusion, filtered-illusion, illusionless-control, and filtered-illusionless-control images.
The source-condition images are sampled from MNIST and resized to 512 × 512 pixels. Illusion images were… See the full description on the dataset page: https://huggingface.co/datasets/VQA-Illusion/MNIST_test.imagenet-1k-wds
Dataset Summary
ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.
💡… See the full description on the dataset page: https://huggingface.co/datasets/timm/imagenet-1k-wds.tomato-map
Dataset Card for TomatoMAP
This is a FiftyOne dataset with 68,069 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/tomato-map")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/tomato-map.IllusionAnimals_test
IllusionAnimals — Test Set
Dataset summary
This repository contains the public test split of IllusionAnimals, introduced in Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions. Each annotated example is paired across source-condition, illusion, filtered-illusion, illusionless-control, and filtered-illusionless-control conditions.
The animal source-condition images were generated with SDXL-Lightning. English scene descriptions and… See the full description on the dataset page: https://huggingface.co/datasets/VQA-Illusion/IllusionAnimals_test.imagenet-12k-wds
Dataset Summary
This is a filtered copy of the full ImageNet dataset consisting of the top 11821 (of 21841) classes by number of samples. It has been used to pretrain a number of in12k models in timm.
The code and metadata for building this dataset from the original full ImageNet can be found at https://github.com/rwightman/imagenet-12k
NOTE: This subset was filtered from the original fall11 ImageNet release which has been replaced by the winter21 release which removes close to 3000… See the full description on the dataset page: https://huggingface.co/datasets/timm/imagenet-12k-wds.Describable-Textures-Dataset
Dataset Card for Describable Textures Dataset
This is a FiftyOne dataset with 5640 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/Describable-Textures-Dataset")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Describable-Textures-Dataset.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