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.MIC21
[!NOTE]
Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/18029/
MIC21
Original description
One of the processing tasks for large multimodal data streams is automatic image description (image classification, object segmentation and classification). Although the number and the diversity of image datasets is constantly expanding, still there is a huge demand for more datasets in terms of variety of domains and object classes covered. The goal of the… See the full description on the dataset page: https://huggingface.co/datasets/FrancophonIA/MIC21.GroMo25
GroMo25: Multiview Time-Series Plant Image Dataset for Age Estimation and Leaf Counting
Dataset Summary
GroMo25 is a multiview, time-series plant image dataset designed for plant age estimation (in days) and leaf counting tasks in precision agriculture. It contains high-quality images of four crop species — Wheat, Okra, Radish, and Mustard — captured over multiple days under controlled conditions. Each plant is photographed from 24 angles across 5 vertical levels per day… See the full description on the dataset page: https://huggingface.co/datasets/MrigLabIITRopar/GroMo25.mvtec-ad
Dataset Card for MVTec AD
This dataset originates from MVTec but is provided in a different format. You can easily load it using FiftyOne
The total number of samples remains the same as the original: 5,354.
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/mvtec-ad.beans
Dataset Card for Beans
Dataset Summary
Beans leaf dataset with images of diseased and health leaves.
Supported Tasks and Leaderboards
image-classification: Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any.
Languages
English
Dataset Structure
Data Instances
A sample from the training set is provided below:
{
'image_file_path':… See the full description on the dataset page: https://huggingface.co/datasets/AI-Lab-Makerere/beans.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.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.PaveBench
PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis
Abstract
PaveBench is a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. It supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. On the visual side, PaveBench provides large-scale annotations on real… See the full description on the dataset page: https://huggingface.co/datasets/MML-Group/PaveBench.mvtec-ad
MVTec AD - Anomaly Detection Dataset
This repository mirrors the MVTec AD dataset for CI/CD and non-commercial research workflows.
License
The data is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). It is not allowed to use the dataset for commercial purposes.
Attribution
If you use the dataset in scientific work, please cite the original authors:
Paul Bergmann, Kilian Batzner… See the full description on the dataset page: https://huggingface.co/datasets/foersben/mvtec-ad.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.MPII_Human_Pose_Dataset
Dataset Card for MPII Human Pose
MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation.
The dataset includes around 25K images containing over 40K people with annotated body joints.
The images were systematically collected using an established taxonomy of every day human activities.
Overall the dataset covers 410 human activities and each image is provided with an activity label.
Each image was extracted from a YouTube… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/MPII_Human_Pose_Dataset.PlantVillage
PlantVillage Dataset
The PlantVillage Dataset is an open access repository of 54,306 images of healthy and diseased plant leaves, collected to advance research in automated plant disease diagnosis. It covers 14 crop species and 26 diseases.
This dataset was introduced in the paper "Using Deep Learning for Image-Based Plant Disease Detection" by Mohanty et al. (2016).
Quick Start
The dataset comes with pre-defined 80/20 train/test splits that preserve the leaf grouping… See the full description on the dataset page: https://huggingface.co/datasets/mohanty/PlantVillage.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.military_vehicles
Citation
If you use this dataset, please cite the following paper:
@article{kricheli2024error,
title={Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge},
author={Kricheli, Joshua Shay and Vo, Khoa and Datta, Aniruddha and Ozgur, Spencer and Shakarian, Paulo},
journal={arXiv preprint arXiv:2407.15192},
year={2024}
}
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.Devanagari-Characters-Image
Devanagari Characters Image Dataset
Dataset Summary
The Devanagari Characters Image Dataset is a high-resolution dataset designed to support research and experimentation in generative modeling, specifically for the Hindi script. It includes images for:
Vowels (स्वर)
Consonants (व्यंजन)
Matra combinations (e.g., का, कि, की, कु)
Hindi numerals (०-९)
The dataset was created to address the limitations of existing Devanagari datasets, which often suffer from low resolution… See the full description on the dataset page: https://huggingface.co/datasets/Mayank022/Devanagari-Characters-Image.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.real-infrared-maritime-vessel-dataset
Real Infrared Maritime Vessel Dataset
Real infrared imagery of maritime vessels.
The dataset is provided in three forms — full-frame detection images, per-object classification crops, and a hand-curated subset.
Classes (7): liner, bulk carrier, warship, sailboat, canoe, container ship, fishing boat.
Layout
real-infrared-maritime-vessel-dataset/
├── original/ Full-frame IR images + XML bounding-box labels (detection)
│ ├── images/{train,test}/*.jpg… See the full description on the dataset page: https://huggingface.co/datasets/hanchong/real-infrared-maritime-vessel-dataset.military-aircraft-detection-dataset
Military Aircraft Detection Dataset
Military aircraft detection dataset in COCO and YOLO format.
The dataset was initially developed exclusively for military aircraft detection, but was later expanded to include commercial airliners for a broader and more challenging detection task.
The dataset contains 103 military aircraft types and 11 commercial airliner types.
Military aircraft: A10, A400M, AG600, AH64, AKINCI, AV8B, An124, An22, An225, An72, B1, B2, B21, B52, Be200, C1… See the full description on the dataset page: https://huggingface.co/datasets/a2015003713/military-aircraft-detection-dataset.cats_vs_dogs
Dataset Card for Cats Vs. Dogs
Dataset Summary
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
From the competition page:
The Asirra data set
Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs.lfw
LFW HF-ready
This folder packages the local LFW (Labeled Faces in the Wild) images as a
Hugging Face imagefolder dataset with the canonical 10-fold verification
pairs file.
Layout
lfw/
├── README.md
├── pairs.csv
└── train/
├── images/<shard>/<file>.jpg
└── metadata.csv
metadata.csv columns
file_name: relative image path used by ImageFolder, e.g. images/000/Aaron_Eckhart_0001.jpg.
label: numeric identity label.
label_name / identity: identity name.… See the full description on the dataset page: https://huggingface.co/datasets/marcelohaps/lfw.curated-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.mixlora-eval-data
🚀 MixLoRA Evaluation Data
This dataset is the held-out multimodal evaluation suite used in
Multimodal Instruction Tuning with Conditional Mixture of LoRA (ACL 2024).
It bundles 9 instruction-formatted tasks (mm_tasks/) plus the MME benchmark
(mme/) used to evaluate MixLoRA and baseline models in the paper.
The 9 tasks in mm_tasks/ are the zero-shot / held-out task split from
Vision-Flan. MME is a
separate benchmark, evaluated independently.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/yingss/mixlora-eval-data.lgg-mri-segmentation-research
LGG Brain MRI Segmentation with Genomic Clusters
This repository provides a Patient-Centric version of the Lower-Grade Glioma (LGG) Segmentation dataset. While other versions of this data exist, they often treat slices as independent images. This version preserves the 3D patient volume and integrates all genomic/clinical labels directly into a multimodal-ready format.
🌟 Why This Version?
Developed for Multimodal AI Research, this dataset addresses several limitations… See the full description on the dataset page: https://huggingface.co/datasets/Ehsan-rmz/lgg-mri-segmentation-research.OmniFake
OmniFake
OmniFake is a large-scale, well-categorized synthetic image dataset introduced in Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples. It contains 1.17 million AI-generated images from 45 distinct generators, paired with 1.17 million real images, designed for research on AI-generated image (AIGI) detection and source attribution.
For usage instructions and experimental protocols, please refer to the OmniDFA GitHub repository.… See the full description on the dataset page: https://huggingface.co/datasets/MoeNew/OmniFake.MLLM-Generated-Image-Detection-Dataset
MLLM-Generated Image Dataset
This dataset contains real and AI-generated image samples organized for binary MLLM-generated image detection.
Paper | Code
Dataset Summary
We construct an MLLM-generated image detection benchmark from GPT Image2 and Nano Banana2. This benchmark covers texture-dominated, structure-dominated, and hybrid-dominated. It is designed to evaluate detector performance under the new challenges introduced by large-scale image generation models.… See the full description on the dataset page: https://huggingface.co/datasets/zr-zhang/MLLM-Generated-Image-Detection-Dataset.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.LAION-Beyond
LAION-Beyond: Reproducible Vision-Language Models Meet Concepts Out of Pre-Training
📄 Paper (CVPR 2025) |
💻 Code |
🌐 Project Page
Dataset Summary
LAION-Beyond is the first multi-domain benchmark specifically designed to evaluate the Out-of-Pre-training (OOP) generalization of vision-language models (e.g., CLIP, OpenCLIP, EVA-CLIP).
We distinguish two types of visual concepts:
IP (In-Pre-training): concepts that appear in the pre-training data (e.g.… See the full description on the dataset page: https://huggingface.co/datasets/MHuangX/LAION-Beyond.