datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cifar10
Dataset Card for CIFAR-10
Dataset Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar10.cifar100
Dataset Card for CIFAR-100
Dataset Summary
The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass).
Supported Tasks and Leaderboards
image-classification: The… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar100.food101
Dataset Card for Food-101
Dataset Summary
This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/ethz/food101.imagenet-100
Dataset Card for ImageNet-100
ImageNet-100 is a subset of the original ImageNet-1k dataset containing 100 randomly selected classes. In addition, the images have been resized to 160 pixels on the shorter side.
Homepage: https://github.com/HobbitLong/CMC
Paper: https://arxiv.org/abs/1906.05849
Dataset Structure
Data Instances
An example looks like below:
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=160x213>,
'label': 0
}… See the full description on the dataset page: https://huggingface.co/datasets/clane9/imagenet-100.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.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.Egocentric_10K_Evaluation
Dataset Card for Egocentric_10K_Evaluation
This is a FiftyOne dataset with 30000 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/Egocentric_10K_Evaluation")
# Launch the App
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Egocentric_10K_Evaluation.Lurcher_10x
Lurcher 10x Microscopy Dataset
Dataset overview
This dataset consists of 2-D microscopy images of histologically stained 3-D structures in tissue sections through the cerebellum of 21 mouse brains. Animals are grouped into wild-type controls (n = 10) and Lurcher mutant mice (n = 11). The classification task is to distinguish Lurcher mutant mice from wild-type controls.
All images were captured at low magnification (10x) and stained with Cresyl violet, a general… See the full description on the dataset page: https://huggingface.co/datasets/USF-CS-Microscopy-Image-Analysis/Lurcher_10x.DomainNetData downloaded from WILDS (Download, paper, project).
This dataset contains some copyrighted material whose use has not been specifically authorized by the copyright owners. In an effort to advance scientific research, we make this material available for academic research. We believe this constitutes a fair use of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, the material on this site is distributed… See the full description on the dataset page: https://huggingface.co/datasets/wltjr1007/DomainNet.Food101
Dataset Card for Food-101
This is a FiftyOne dataset with 35000 samples.
Note: This dataset is subset of the full Food101 dataset. 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 =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Food101.Camelyon17-WILDS
https://wilds.stanford.edu/datasets/#camelyon17
Center 0, 3, 4 - Source (If split=1, Validation (ID))
Center 1 - Validation (OOD)
Center 2 - Target (OOD)
cinic10
Dataset Card for CINIC-10
CINIC-10 has a total of 270,000 images equally split amongst three subsets: train, validate, and test. This means that CINIC-10 has 4.5 times as many samples than CIFAR-10.
Dataset Details
In each subset (90,000 images), there are ten classes (identical to CIFAR-10 classes). There are 9000 images per class per subset. Using the suggested data split (an equal three-way split), CINIC-10 has 1.8 times as many training samples as in CIFAR-10.… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/cinic10.DamageTriage-Bench
DamageTriage-Bench
DamageTriage-Bench is a footprint-conditioned benchmark for per-building damage
typing from single post-event aerial images. Its five classes distinguish roof
from structural damage and partial from total affected extent:
ID
Class
0
Undamaged
1
Partial Roof Damage
2
Total Roof Damage
3
Partial Structural Damage
4
Total Structural Collapse
Quick statistics
Item
Value
Tiles
7,472 (1024 × 1024 PNG)
Labeled… See the full description on the dataset page: https://huggingface.co/datasets/Ymx1025/DamageTriage-Bench.cifar100-enrichedThe CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass).document-haystack-10pages
Dataset Card for document-haystack-10pages
This is a FiftyOne dataset with 250 samples. It's the 10-page subset of the full dataset.
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/document-haystack-10pages")
# Launch the App… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/document-haystack-10pages.cifar10
Dataset Card for CIFAR-10
Dataset Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cifar10.artelingo-dummyArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures. It is an extension of ArtEmis, which is a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo expands this dataset by adding 790,000 annotations in Arabic and Chinese. The purpose of these additional annotations is to evaluate the performance of "cultural-transfer" in AI systems.
The dataset in ArtELingo… See the full description on the dataset page: https://huggingface.co/datasets/youssef101/artelingo-dummy.caltech101
Dataset Card for Caltech 101
This dataset contains images of objects from 101 distinct categories, with each category comprising approximately 40 to 800 images. The majority of categories include around 50 images each. The images were collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc’Aurelio Ranzato. Each image has an approximate resolution of 300 x 200 pixels.
Dataset Sources
Website: https://data.caltech.edu/records/mzrjq-6wc02
Use in FL… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/caltech101.galaxy10_decals
Dataset Card for "galaxy10_decals"
Galaxy10 DECals is a dataset contains 17736 256x256 pixels colored galaxy images separated in 10 classes.
Galaxy10 DECals images come from DESI Legacy Imaging Surveys which includes the Beijing-Arizona Sky Survey (BASS), the DECam Legacy Survey (DECaLS) and the Mayall z-band Legacy Survey and labels come from Galaxy Zoo.
Dataset create from
https://astronn.readthedocs.io/en/latest/galaxy10.html
https://github.com/henrysky/Galaxy10… See the full description on the dataset page: https://huggingface.co/datasets/matthieulel/galaxy10_decals.vlmn_tartandrive100_scand50_coda25_spot100_sub5_full_augmentation_processed_10
Trajectory Ranking Dataset
This dataset contains trajectory ranking results for autonomous navigation scenarios.
Dataset Statistics
Total examples: 39558
Chunks processed: 40
Upload date: 2025-09-13T00:44:30.335177
Features
Image data with terrain analysis
Trajectory rankings and reasoning
Quality and diversity analysis
Terrain and trajectory descriptions
TTA-CIFAR-100-C
TTA-CIFAR-100-C
Mirror of CIFAR-100-C (Hendrycks & Dietterich, ICLR 2019) with a
revision pin for reproducible test-time adaptation evaluation.
Upstream: Zenodo record 3555552
License: CC BY 4.0 (matches upstream)
Sibling: TTA-CIFAR-10-C
Maintained as part of: TTA-Evaluation-Harness
Citation
@inproceedings{hendrycks2019benchmarking,
title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
author={Hendrycks, Dan and Dietterich… See the full description on the dataset page: https://huggingface.co/datasets/WNJXYK/TTA-CIFAR-100-C.cifar100-lt
Dataset Card for CIFAR-100-LT (Long Tail)
Dataset Summary
Note (March 2026): This dataset has been migrated from a Python loading script to
parquet format for compatibility with datasets v4.4+. No trust_remote_code=True
is needed. Available configs: r-10, r-20, r-50, r-100.
The CIFAR-100-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 100 distinct classes.
The number of samples within each class… See the full description on the dataset page: https://huggingface.co/datasets/tomas-gajarsky/cifar100-lt.BrainTumorClassification
BrainTumorClassification
A large-scale curated brain MRI image dataset for multi-class brain tumor classification and medical imaging research.
Dataset Overview
BrainTumorClassification is a curated collection of 16,599 brain MRI images designed for training and evaluating machine learning and deep learning models for brain tumor classification.
The dataset contains four diagnostic categories:
Glioma
Meningioma
Pituitary Tumor
No Tumor
This dataset was developed… See the full description on the dataset page: https://huggingface.co/datasets/devpatel1012/BrainTumorClassification.TTA-CIFAR-10-C
TTA-CIFAR-10-C
Mirror of CIFAR-10-C (Hendrycks & Dietterich, ICLR 2019) with a revision
pin for reproducible test-time adaptation evaluation.
Upstream: Zenodo record 2535967
License: CC BY 4.0 (matches upstream)
SHA256 of upstream tarball: c72763e101c723b7c507b96205f7e938912a5d587376173b825850cf3cb876a7
Maintained as part of: TTA-Evaluation-Harness
Citation
@inproceedings{hendrycks2019benchmarking,
title={Benchmarking Neural Network Robustness to Common… See the full description on the dataset page: https://huggingface.co/datasets/WNJXYK/TTA-CIFAR-10-C.FindFlower-Premium-100-flowering
FindFlower-Premium-100
Total classes: 1,500 botanical taxa covering common, semi-rare, and cultivated flora.
Total verified samples: 150,000 images with a strict 100-sample balanced distribution.
Preprocessing: quality filtered, deduplicated via perceptual hashing, and resolution normalized to 224x224 JPEG.
Intended downstream task: MaxViT-1500 on-device botanical classification.
Mars-Analog-Dunes-10
Mars-Analog-Dunes-10 Dataset
Dataset Description
Mars-Analog-Dunes-10 is a curated remote sensing dataset designed for Earth-Mars Comparative Planetology. It contains high-quality satellite imagery of Earth's sand dunes that serve as morphological analogs to features found on Mars.
Key Features
Mars Analog Focus: Selected from Earth regions (e.g., deserts in China, Africa) known for their similarity to Martian geomorphology.
10 Fine-Grained Classes:… See the full description on the dataset page: https://huggingface.co/datasets/Keiyoo/Mars-Analog-Dunes-10.cifar10-lt
Dataset Card for CIFAR-10-LT (Long Tail)
Dataset Summary
Note (March 2026): This dataset has been migrated from a Python loading script to
parquet format for compatibility with datasets v4.4+. No trust_remote_code=True
is needed. Available configs: r-10, r-20, r-50, r-100.
The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 10 distinct classes.
The number of samples within each class decreases… See the full description on the dataset page: https://huggingface.co/datasets/tomas-gajarsky/cifar10-lt.NHD-100
NHD-100: Nepali Handwriting Dataset
Description
NHD-100 is a Nepali offline handwriting dataset collected for research on writer identification and handwriting analysis. The dataset contains handwritten Nepali samples from 100 writers, with 6 handwritten pages collected from each writer, resulting in 600 handwritten pages in total.
The handwriting is written in the Devanagari script and contains a variety of Nepali words, characters, conjuncts, and numerals.… See the full description on the dataset page: https://huggingface.co/datasets/pranita-mukhia/NHD-100.fundus-10class-augmented
Fundus 10-Class Augmented Dataset
Augmented and split-curated version of the Mendeley fundus
dataset, used in
the Master's thesis "Classification of Fundus Lesion Images Using
Deep Learning Models" (Xidian University, 2026).
Models trained on this dataset: DoB24/fundus-9model-benchmark.
Summary
10 disease classes (see table below).
5,335 original images; 16,242 augmented images (rotation, flip, brightness/contrast jitter, Gaussian blur).
Group-aware test split… See the full description on the dataset page: https://huggingface.co/datasets/DoB24/fundus-10class-augmented.CleanSTL-10
Dataset Card for STL-10 Cleaned (Deduplicated Training Set)
Paper | Code
Dataset Description
This dataset is a modified version of the STL-10 dataset. The primary modification involves deduplicating the training set by removing any images that are exact byte-for-byte matches (based on SHA256 hash) with images present in the original STL-10 test set. The dataset comprises this cleaned training set and the original, unmodified STL-10 test set.
The goal is to provide a… See the full description on the dataset page: https://huggingface.co/datasets/Shu1L0n9/CleanSTL-10.