datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
imagenet_1k_resized_256
Dataset Card for "imagenet_1k_resized_256"
Dataset summary
The same ImageNet dataset but all the smaller side resized to 256.
A lot of pretraining workflows contain resizing images to 256 and random cropping to 224x224, this is why 256 is chosen.
The resized dataset can also be downloaded much faster and consume less space than the original one.
See here for detailed readme.
Dataset Structure
Below is the example of one row of data. Note that the labels in… See the full description on the dataset page: https://huggingface.co/datasets/evanarlian/imagenet_1k_resized_256.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.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.Defactify_Image_Dataset
Defactify_Image_Dataset
This dataset is associated with the paper A Comprehensive Dataset for Human vs. AI Generated Image Detection.
📝 Dataset Description
Dataset Summary
The Defactify_Image_Dataset (A Comprehensive Dataset for Human vs. AI Generated Image Detection) is a high-quality collection of 96,000 images and associated metadata designed to benchmark models for detecting and identifying the source of artificially generated content. Built using the MS… See the full description on the dataset page: https://huggingface.co/datasets/Rajarshi-Roy-research/Defactify_Image_Dataset.MetaPKLot-Dataset
MetaPKLot
A Large-Scale Benchmark for Vision-Based Parking Lot Management
2,265,974 labeled samples · 1,366,185 new annotations · 3 research challenges · COCO-style annotations
MetaPKLot is a large-scale, harmonized dataset designed for research on vision-based parking lot management.
It extends and standardizes three existing parking datasets:
PKLot
CNRPark-EXT
PLds
MetaPKLot introduces new annotations, revises existing parking-space annotations, standardizes… See the full description on the dataset page: https://huggingface.co/datasets/DSBD-Research/MetaPKLot-Dataset.sawhill-dataset
Sawhill Numismatic Collection Dataset
Dataset Description
This dataset contains video recordings and extracted images of coins from the MacKenzie Art Gallery's Sawhill Numismatic Collection. The dataset is designed for research in automated coin identification, cultural heritage digitization, and computer vision applications in numismatics.
Dataset Summary
Source: MacKenzie Art Gallery Sawhill Numismatic Collection
Content: Handheld video recordings of coins… See the full description on the dataset page: https://huggingface.co/datasets/COIN-Research-Group/sawhill-dataset.PUUM-koa-restoration-camera-trap-dataset
Dataset Card for Koa Associated Biodiversity Camera Trap Dataset
This dataset is aimed at classification of birds visiting planted Acacia koa (koa) trees in the Pu'u Maka'ala Natural Area Reserve (PUUM) on the island of Hawaii (Big Island). The dataset contains full and cropped images collected by camera trap. These images were collected from January 24th to February 25th, 2025.
Dataset Details
This dataset is aimed at classification of birds visiting planted Acacia… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/PUUM-koa-restoration-camera-trap-dataset.resisc45Redistributed from https://drive.google.com/file/d/1DnPSU5nVSN7xv95bpZ3XQ0JhKXZOKgIv without modification. Only converted the RAR file to a ZIP file. Please cite https://doi.org/10.1109/jproc.2017.2675998 if you use this dataset. The train-val-test split files come from https://arxiv.org/abs/1911.06721.
RESISC45
Remote Sensing Image Scene Classification (RESISC45) Dataset
Paper Remote Sensing Image Scene Classification: Benchmark and State of the Art
Paper with code: RESISC45
Description
The RESISC45 dataset is a scene classification dataset that focuses on RGB images extracted using Google Earth. This dataset comprises a total of 31,500 images, with each image having a resolution of 256x256 pixels. RESISC45 contains 45 different scene classes, with 700 images per… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/RESISC45.NWPU-RESISC45
Dataset Card for "NWPU-RESISC45"
Licensing Information
[CC-BY-SA]
Citation Information
Remote sensing image scene classification: Benchmark and state of the art
@article{cheng2017remote,
title = {Remote sensing image scene classification: Benchmark and state of the art},
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
year = 2017,
journal = {Proceedings of the IEEE},
publisher = {IEEE},
volume = 105… See the full description on the dataset page: https://huggingface.co/datasets/jonathan-roberts1/NWPU-RESISC45.SCIN-Dermatology-Raw-Images
SCIN-Dermatology-Raw-Images
This dataset contains 6,517 patient-submitted photographs organized into 3,061 clinical cases of common skin diseases. The source images are curated from the public Google Skin Condition Image Network (SCIN) corpus, cleansed of quality and gradability conflicts, and paired with complete patient-reported demographics, clinical symptoms, and dermatologist gradings.
Dataset Structure
This repository follows the standard Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/HawkFranklin-Research/SCIN-Dermatology-Raw-Images.cardio-mark
CardioMark Review Subset
This repository contains an anonymized review subset of the CardioMark benchmark introduced for automated vertebral heart score (VHS) estimation in canine thoracic radiographs.
The subset is provided to support reproducibility and data-quality inspection during peer review.
Dataset Overview
CardioMark is a large-scale benchmark for evaluating the complete VHS measurement pipeline, including:
cardiac landmark localization
geometric VHS estimation… See the full description on the dataset page: https://huggingface.co/datasets/gen-ai-researcher/cardio-mark.neuralatlas-attributions-resnet18
Neural Atlas attributions — resnet18 on imagenet-pico
Precomputed attribution maps and faithfulness metrics for the torchvision
resnet18 model (default pretrained weights, no fine-tuning) on imagenet-pico,
a 3000-image subset of ImageNet-1k with three images for each of the 1000
classes.
This repository is part of Neural Atlas, a web tool for comparing
attribution methods across vision architectures on the same image, developed
as an undergraduate thesis at the Facultad de… See the full description on the dataset page: https://huggingface.co/datasets/Matgc04/neuralatlas-attributions-resnet18.neuralatlas-attributions-resnet101
Neural Atlas attributions — resnet101 on imagenet-pico
Precomputed attribution maps and faithfulness metrics for the torchvision
resnet101 model (default pretrained weights, no fine-tuning) on imagenet-pico,
a 3000-image subset of ImageNet-1k with three images for each of the 1000
classes.
This repository is part of Neural Atlas, a web tool for comparing
attribution methods across vision architectures on the same image, developed
as an undergraduate thesis at the Facultad de… See the full description on the dataset page: https://huggingface.co/datasets/Matgc04/neuralatlas-attributions-resnet101.SD-198SD-198 dataset contains 198 different diseases from different types of
eczema, acne and various cancerous conditions. There are 6,584 images in total.
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/vpasx/lgg-mri-segmentation-research.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/mteb/resisc45.OpenJev-Vision-Research-v0.1
OpenJev Vision Research v0.1
12,832 image records, with public provenance, original synthetic scenes,
and programmatically derived decision questions.
This is an experimental research dataset for visual posterior learning and
compositional decisions, released with OpenJev.
It is not a reproduction of TypeSafe's proprietary Jev model or training method.
Three separate configurations
Config
Images
What the labels mean
License
synthetic
8,192
Exact… See the full description on the dataset page: https://huggingface.co/datasets/IamBusy/OpenJev-Vision-Research-v0.1.AI-vs-Deepfake-vs-Real-Resized-Aug
🧠 AI vs Deepfake vs Real — Processed Version
This dataset is the result of preprocessing and augmentation applied to the original datasetprithivMLmods/AI-vs-Deepfake-vs-Real.
📘 Overview
This dataset contains a collection of images categorized into three main classes:
🟩 AI-generated
🟥 Deepfake
🟦 Real (authentic human faces)
It is designed for image classification tasks that aim to distinguish between AI-generated, deepfake, and real faces.… See the full description on the dataset page: https://huggingface.co/datasets/chintalaswathi/AI-vs-Deepfake-vs-Real-Resized-Aug.imagenet_1k_resized_256
Dataset Card for "imagenet_1k_resized_256"
Dataset summary
The same ImageNet dataset but all the smaller side resized to 256.
A lot of pretraining workflows contain resizing images to 256 and random cropping to 224x224, this is why 256 is chosen.
The resized dataset can also be downloaded much faster and consume less space than the original one.
See here for detailed readme.
Dataset Structure
Below is the example of one row of data. Note that the… See the full description on the dataset page: https://huggingface.co/datasets/arthtrivedi/imagenet_1k_resized_256.real-infrared-maritime-vessel-dataset-super-resolved
Real Infrared Maritime Vessel Dataset (Super-Resolved, HYPIR)
HYPIR-restored crops of the real infrared maritime vessel dataset (cropped/), with vs without a fixed class-name text prompt guiding restoration.
Layout
real-infrared-maritime-vessel-dataset-super-resolved/
├── w_cat_prompt/ restored with prompt: "infrared image of a <class-name> on sea water"
│ ├── labels.txt, {train,val,test}.csv
│ ├── train/{0..6}/*.png 25,045
│ ├── val/{0..6}/*.png… See the full description on the dataset page: https://huggingface.co/datasets/hanchong/real-infrared-maritime-vessel-dataset-super-resolved.ImageIn_annotations_resized_images
Dataset Card for ImageIn_annotations_resized_images
More Information needed
residuals-fingerprints
RESIDUALS — LiDAR DEM residual fingerprints
39,716 residual images extracted by applying 593 distinct decomposition configurations × 25 upsampling methods to a single Fairfield County, Ohio LiDAR-derived Digital Elevation Model (1500×375 at 3.33 ft/px). Each row pairs a 256×256 PNG of the residual (rendered with the standard RdBu_r colormap, 99th-percentile symmetric clipping) with the algorithm and parameters that produced it, plus a 40-dim signature vector and pre-computed 2D/3D… See the full description on the dataset page: https://huggingface.co/datasets/bshepp/residuals-fingerprints.imagenette-320px-resplit
Imagenette 320px with Fixed Validation and Test Splits
Dataset Description
This dataset is a reproducible, Parquet-based version of the 320px configuration of frgfm/imagenette. Imagenette is a subset of ten readily classified ImageNet classes created for fast experimentation with image-classification methods.
This version preserves the source images, numeric labels, and label metadata. Its only data change is a fixed, stratified division of the original validation… See the full description on the dataset page: https://huggingface.co/datasets/leandrodevai/imagenette-320px-resplit.Latent-Resonance-AI-Image-Forensics-Benchmark-N100
Latent Resonance: SOTA Empirical AI Image Forensics Benchmark (N=100 & N=1,000 Scale)
Author: Debdip Bandyopadhyay (Independent AI Researcher, Kolkata, India; M.Tech, IIT Jodhpur, AI & Data Science)Preprint & Paper: Latent Resonance: Zero-Shot Autoencoder Inversion and Azimuthal Spectral Forensics for Diffusion Image Attribution (IEEE Flagship / CERN Zenodo 2026)
Benchmark Overview
This repository provides:
The official verified $N=100$ ground-truth image… See the full description on the dataset page: https://huggingface.co/datasets/DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N100.imagewoof-320px-resplit
ImageWoof 320px with Fixed Validation and Test Splits
Dataset Description
This dataset is a reproducible, Parquet-based version of the 320px configuration of frgfm/imagewoof. ImageWoof is a subset of ten dog-breed classes from ImageNet designed to be more difficult than broad-category image-classification benchmarks.
This version is intended for image classification and confidence-calibration experiments. It introduces two changes to the source dataset:
It… See the full description on the dataset page: https://huggingface.co/datasets/leandrodevai/imagewoof-320px-resplit.eurosat
EuroSAT Image Classification Dataset
This dataset contains the EuroSAT satellite image classification data in parquet format for easy loading and processing.
Dataset Information
Task: Image Classification
Source: EuroSAT Dataset
Classes: 10 land use/land cover classes
Image Size: 64x64 pixels (RGB)
Format: Parquet with embedded images
Splits: train, test
Classes
The dataset contains 10 land use and land cover classes:
ID
Class Name
Description
0… See the full description on the dataset page: https://huggingface.co/datasets/resaro/eurosat.High_Res-vs-Low_Res
High_Res-vs-Low_Res
High_Res-vs-Low_Res is a dataset designed for image classification, distinguishing between high-quality and low-quality images. This dataset includes a diverse collection of 5,016 high-resolution and low-resolution images to enhance classification accuracy and improve the model’s overall efficiency. By providing a well-balanced dataset, it aims to support the development of robust image quality assessment models.
Label Mappings
Mapping of IDs to… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/High_Res-vs-Low_Res.ICDAR2019_cTDaR_TRACKB_resized
Dataset Card for ICDAR2019-cTDaR-TRACKB
This dataset is a resized version of the original cndplab-founder/ICDAR2019_cTDaR, merged with with its supplement cndplab-founder/ICDAR2019_cTDaR_dataset_supplement.
You can easily and quickly load it:
dataset = load_dataset("dvgodoy/ICDAR2019_cTDaR_TRACKB_resized")
DatasetDict({
train: Dataset({
features: ['image', 'width', 'height', 'category', 'label', 'bboxes_table', 'bboxes_cell'],
num_rows: 1200
})
test:… See the full description on the dataset page: https://huggingface.co/datasets/dvgodoy/ICDAR2019_cTDaR_TRACKB_resized.NWPU-RESISC45
Dataset Card for "NWPU-RESISC45"
Licensing Information
[CC-BY-SA]
Citation Information
Remote sensing image scene classification: Benchmark and state of the art
@article{cheng2017remote,
title = {Remote sensing image scene classification: Benchmark and state of the art},
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
year = 2017,
journal = {Proceedings of the IEEE},
publisher = {IEEE},
volume… See the full description on the dataset page: https://huggingface.co/datasets/Ling200424/NWPU-RESISC45.