datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mnist
Dataset Card for MNIST
Dataset Summary
The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.
Half of the image were drawn by Census Bureau employees and the other half by high school students… See the full description on the dataset page: https://huggingface.co/datasets/ylecun/mnist.fashion_mnist
Dataset Card for FashionMNIST
Dataset Summary
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing… See the full description on the dataset page: https://huggingface.co/datasets/zalando-datasets/fashion_mnist.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.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.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.semasia-mnist
Latents for mnist (timm)
This repository hosts precomputed latent representations (embeddings) extracted from timm image-classification backbones on mnist, released as part of SEMASIA — a large-scale resource for studying semantic communication, cross-model latent space alignment, and explainability.
Each config corresponds to a single model;
only that model's Parquet files are read on load_dataset.
Usage
Load with datasets and convert to… See the full description on the dataset page: https://huggingface.co/datasets/spaicom-lab/semasia-mnist.N-MNISTcurated-mnist
Dataset Card for CuratedMNIST
This dataset is a curated version of the classic MNIST dataset, enriched with model predictions, embeddings, and various analytical fields generated using the FiftyOne library. It was created as part of the "Image Classification and Dataset Curation with FiftyOne and PyTorch" tutorial to demonstrate practical computer vision workflows.
The dataset contains the original 60,000 training and 10,000 test samples, with the training set further split into… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/curated-mnist.MNIST-M
Train Example
👉Domain-Adversarial-Neural-Network
mnistmnist-adversarial-datasetMNIST
🖼️ MNIST (Extracted from PyTorch Vision)
MNIST is a classic dataset of handwritten digits, widely used for image classification tasks in machine learning.
ℹ️ Dataset Details
📖 Dataset Description
The MNIST database of handwritten digits is a commonly used benchmark dataset in machine learning. It consists of 70,000 grayscale images of handwritten digits (0-9), each with a size of 28x28 pixels. The dataset is split into 60,000 training images and 10,000… See the full description on the dataset page: https://huggingface.co/datasets/p2pfl/MNIST.mnist-cleaned-full
Dataset Card for 2025.11.21.16.40.44.970939
This is a FiftyOne dataset with 69807 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("Linus-L/mnist-cleaned-full")
# Launch the App
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Linus-L/mnist-cleaned-full.gov_myscheme
MyScheme Dataset
Overview
This dataset contains structured information extracted from the Indian Government's MyScheme website (myscheme.gov.in). The data is collected from the sitemap and processed for accessibility, research, and analytical purposes.
Data Type
723 PDF with Text inside each PDF (copyable) most of the schemes are here.
Data Source
Official Website: MyScheme
Sitemap URL: Sitemap
Dataset Contents
The dataset includes… See the full description on the dataset page: https://huggingface.co/datasets/Mniah686/gov_myscheme.wds_mnistsemasia-fashion_mnist
Latents for fashion_mnist (timm)
This repository hosts precomputed latent representations (embeddings) extracted from timm image-classification backbones on fashion_mnist, released as part of SEMASIA — a large-scale resource for studying semantic communication, cross-model latent space alignment, and explainability.
Each config corresponds to a single model;
only that model's Parquet files are read on load_dataset.
Usage
Load with… See the full description on the dataset page: https://huggingface.co/datasets/spaicom-lab/semasia-fashion_mnist.tactile-mnist-touch-syn-single-t32-320x240Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
colored_mnist_28
Colored MNIST Dataset
A comprehensive dataset of MNIST digits with RGB colored backgrounds, designed for multi-objective classification tasks including digit recognition and background color classification.
Overview
This dataset extends the classic MNIST digit dataset by adding colored backgrounds (blue, green, red) to create a multi-label classification challenge. Each image contains:
Digit label: 0-9 (digit recognition)
Color label: blue, green, red (background… See the full description on the dataset page: https://huggingface.co/datasets/FrankCCCCC/colored_mnist_28.tactile-mnist-mnist3dDocumentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#3d-mesh-datasets.
emnist_mnist
Dataset Card for "emnist-mnist"
Dataset Information
The emnist-mnist dataset is a set of images of handwritten digits. The dataset is split into a training set and a test set.
Data Fields
image: The image of the handwritten digit. The data type of this field is image.
label: The label of the handwritten digit. The data type of this field is class_label, and it can take on the values '0' to '9'.
Data Splits
train: The training set consists of 60000… See the full description on the dataset page: https://huggingface.co/datasets/tanganke/emnist_mnist.vdf_PC_ANN_Fashion-MNIST_d784_euclideanThis is a dataset created using vector-io
chinese-mnistMNIST_laern_csvmnist1d
[!NOTE]
This dataset card is based on the README file of the authors' GitHub repository: https://github.com/greydanus/mnist1d
The MNIST-1D Dataset
Most machine learning models get around the same ~99% test accuracy on MNIST. The MNIST-1D dataset is 100x smaller (default sample size: 4000+1000; dimensionality: 40) and does a better job of separating between models with/without nonlinearity and models with/without spatial inductive biases.
MNIST-1D is a core teaching dataset in… See the full description on the dataset page: https://huggingface.co/datasets/christopher/mnist1d.tactile-mnist-touch-starstruck-syn-single-t32-320x240Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.
mnistBharat-NanoBEIR-mnimnist-world
MNIST World Dataset
This repository contains the MNIST World dataset, used for experiments in the paper Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments, accepted at ICML 2026.
MNIST World is a 2D partially observed video world modeling benchmark designed to evaluate how well models can handle smooth, time-parameterized symmetries and unobserved regions that continue to evolve.
Project Resources
Paper: arXiv:2601.01075
Project… See the full description on the dataset page: https://huggingface.co/datasets/flowm123/mnist-world.mnist-lance
MNIST (Lance Format)
A Lance-formatted version of the classic MNIST handwritten-digit dataset covering 70,000 28×28 grayscale digits across ten balanced classes. Each row carries inline PNG bytes, the digit label, the human-readable class name, and a cosine-normalized CLIP image embedding, all backed by a bundled IVF_PQ vector index plus scalar indices on the label columns and available directly from the Hub at hf://datasets/lance-format/mnist-lance/data.
Key features… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/mnist-lance.tactile-mnist-touch-real-seq-t256-320x240Documentation is available at https://github.com/TimSchneider42/tactile-mnist/blob/main/doc/datasets.md#touch-datasets.