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.cifar100cifar10cifar-10-cwds_vtab-cifar10cifar100-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).wds_vtab-cifar100cifar10
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.cifar100_2_to_100_constant_size_dataset
Dataset Card for "cifar100_2_to_100_constant_size_dataset"
More Information needed
cifar10-multirun-logits
A kernel function which improves the accuracy and interpretability of large ensembles of neural networks
We describe a new kernel (i.e. similarity function between pairs of examples) which is computed using an ensemble of neural networks. It has the following properties:
Using it to predict test labels (via k-nearest neighbors across the training set) yields even higher accuracy than the standard ensemble inference method
of averaging predictions, once the number of networks… See the full description on the dataset page: https://huggingface.co/datasets/kjj0/cifar10-multirun-logits.CIFAR10_train
Dataset Card for "CIFAR10_train"
More Information needed
CIFAR10_test
Dataset Card for "CIFAR10_test"
More Information needed
cifar100_2_to_100
Dataset Card for "cifar100_2_to_100"
More Information needed
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.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.cifar10
Dataset Specifications
Contains the entire CIFAR10 dataset, downloaded via PyTorch, then split and saved as .png files representing 32x32 images.
There a three splits, perfectly balanced class-wise:
train: 49,000 out of the original 50,000 samples from the training set of CIFAR10;
calibration: 1,000 left-out samples from the training set;
test: 10,000 samples, the entire original test set.
File Structure
Files are archives <split>/<classname>.zip. Each… See the full description on the dataset page: https://huggingface.co/datasets/ego-thales/cifar10.CIFAR10
🖼️ CIFAR10 (Extracted from PyTorch Vision)
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.
ℹ️ Dataset Details
📖 Dataset Description
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 classes are completely mutually exclusive. There is no… See the full description on the dataset page: https://huggingface.co/datasets/p2pfl/CIFAR10.datadream-cifar100-perclasscifar100-fewshot-synthetic-512CIFAR-10
CIFAR-10 - Object Recognition in Images
Benchmark dataset for object classification.🖼️ 60,000 32x32 color images🏷️ 10 classes📁 Format: PNG, CSV📦 Files: 4🧪 Subset of the 80 million tiny images dataset
Dataset Summary
CIFAR-10 is a widely used computer vision dataset consisting of 60,000 32x32 color images in 10 mutually exclusive classes. It was created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The dataset is a labeled subset of the 80 million tiny… See the full description on the dataset page: https://huggingface.co/datasets/KDKCE/CIFAR-10.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/mteb/cifar100.cifar10-multirun-logits-60k
cifar10-multirun-logits-60k
This repo contains the logit outputs produced by 61,565 independently and identically trained ResNets on the CIFAR-10 test-set.
To plot the histogram of accuracies across the first 500 trained models, run the following:
import numpy as np
import matplotlib.pyplot as plt
from huggingface_hub import HfApi
api = HfApi()
logits_path = api.hf_hub_download('kjj0/cifar10-multirun-logits-60k', repo_type='dataset', filename='logits500.npy')
labels_path =… See the full description on the dataset page: https://huggingface.co/datasets/kjj0/cifar10-multirun-logits-60k.cifar10_512x512px
Dataset Card for "cifar10_512x512px"
More Information needed
cifar10-c-parquet
Dataset Card for CIFAR10-C
This dataset is simply an update of the original dataset into the parquet format which should work with the current (circa 2025) huggingface dataset library
Dataset Details
Dataset Description
The CIFAR-10-C dataset is an extension of CIFAR-10 designed to evaluate model robustness to common corruptions. It consists of 950,000 images derived from the original CIFAR-10 test set (10,000 images) by applying 19 different corruption… See the full description on the dataset page: https://huggingface.co/datasets/robro/cifar10-c-parquet.xai-attack-detection-cifar10
XAI Attack Detection — CIFAR-10 PGD
This private research dataset contains balanced, paired clean and adversarial images for
studying whether an attack can be detected from a classifier explanation map.
Dataset construction
The source is the CIFAR-10 test split. A fine-tuned OpenCLIP ViT-B/16 classifies each
clean image. Clean-correct examples are attacked with untargeted L-infinity PGD using
epsilon 8/255, step size 2/255, 10 steps, and deterministic random… See the full description on the dataset page: https://huggingface.co/datasets/nimaeb/xai-attack-detection-cifar10.E2AM_ConvNeXtV2_CIFAR10CIFAR100-Blended-20pct-Backdoor-ExclNaturalcifar10
Dataset Card for "cifar10"
More Information needed
cifar10h
CIFAR-10H Hugging Face Dataset
This repository contains a Hugging Face dataset build of CIFAR-10H, an extension of the CIFAR-10 test set with human-annotated label distributions.
Dataset Description
CIFAR-10H adds human uncertainty information to the CIFAR-10 test images by providing:
expert_probs: probability distributions over the 10 CIFAR-10 classes
expert_counts: raw human vote counts for each class
expert_argmax: one-hot encoded labels from the human-majority choice… See the full description on the dataset page: https://huggingface.co/datasets/MKZuziak/cifar10h.