datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
smart-bin-detect
arudaev/smart-bin-detect
Training data for Smart Bin Recognition – a validator ("is there a bin?")
and an identifier ("which bin?"). The design lives in docs/04-ml-pipeline.md
in the project repo, which is private; the manifests here carry per-image
provenance and are the authoritative record of what this dataset contains.
Every image carries provenance: source, source URL, licence, region,
capture date, annotator where known, label origin (human / machine /
legacy /… See the full description on the dataset page: https://huggingface.co/datasets/arudaev/smart-bin-detect.c3vdv2-SfM
C3VDv2 — Colonoscopy 3D Video Dataset v2
This dataset is a re-packaged version of C3VDv2 originally published by
Johns Hopkins University, distributed under the
Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Original dataset DOI: https://doi.org/10.7281/T1/JC64MK
Dataset archive: https://archive.data.jhu.edu/dataset.xhtml?persistentId=doi:10.7281/T1/JC64MK
Attribution
This re-packaged version was created to facilitate streaming access. The… See the full description on the dataset page: https://huggingface.co/datasets/SmartWhatt/c3vdv2-SfM.DeepJEB-PP
DeepJEB++
Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation
Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang · Hyogu Jeong · Namwoo Kang — KAIST SmartDesignLab
📦 Dataset size & viewer note. DeepJEB++ contains 15,360 deployable, simulation-labeled brackets. The Hugging Face Dataset Viewer above shows only a small preview because the FEA field data are distributed as a compressed archive… See the full description on the dataset page: https://huggingface.co/datasets/KAIST-SmartDesignLab/DeepJEB-PP.guiact_smartphone_test
GUIAct Smartphone Dataset - Test Split
This is a FiftyOne dataset with 2079 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/guiact_smartphone_test")
# Launch the App
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/guiact_smartphone_test.VisionThink-Smart-Train
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao/VisionThink-Smart-Train
This is the training dataset used for our Efficient Reasoning VLM on general VQA tasks.VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning [Paper]
Senqiao Yang,
Junyi Li,
Xin Lai,
Bei Yu,
Hengshuang Zhao,
Jiaya Jia
Highlights
Our VisionThink leverages reinforcement learning to autonomously learn whether… See the full description on the dataset page: https://huggingface.co/datasets/Senqiao/VisionThink-Smart-Train.SmartHarvest
SmartHarvest: Multi-Species Fruit Ripeness Detection Dataset
Dataset Description
SmartHarvest is a comprehensive multi-species fruit ripeness detection and segmentation dataset designed for precision agriculture applications. The dataset contains high-resolution images of fruits in natural garden environments with detailed polygon-based instance segmentation annotations and ripeness classifications.
Key Features
8 fruit species: Apple, cherry, cucumber… See the full description on the dataset page: https://huggingface.co/datasets/TheCoffeeAddict/SmartHarvest.trash-in-river-2025
Street Parade 2025 Dataset
Overview
This dataset was collected by SARA, a student initiative at ETH Zurich, to enable open research on trash presence in aquatic environments. It contains images of litter in the Limmat River in Zurich the day after the Street Parade (August 9, 2025). The dataset is intended for training and evaluating trash classification models.
Dataset summary
Collection date: August 9, 2025
Location: Kornhausbrücke, Zurich… See the full description on the dataset page: https://huggingface.co/datasets/SARA-smartphone-assisted-river-analysis/trash-in-river-2025.real-colon-SfM
REAL-Colon HF Triplets
This dataset is a re-packaged version of REAL-Colon for streaming
self-supervised AF-SfMLearner training. The original dataset is distributed
under CC BY 4.0.
Original dataset DOI: https://doi.org/10.25452/figshare.plus.22202866
Rows are lower-fps temporal triplets sampled from the extracted frame files that
exist on disk. If the official extraction is already subsampled, the requested
target_fps is approximated by the nearest integer step on that available… See the full description on the dataset page: https://huggingface.co/datasets/SmartWhatt/real-colon-SfM.smart-product-pricing-2025street-smart-road-signs
Street Smart: Road Sign Recognition
Dataset Summary
A public, viewer-ready educational challenge dataset. Host-only scoring data and hidden targets are excluded.
Splits
Split
Examples
Description
train
701
Labeled training data
test
176
Public inputs with withheld target labels or annotations
Data Fields
Field
Type
image
Image
image_id
string
width
int64
height
int64
objects.bbox… See the full description on the dataset page: https://huggingface.co/datasets/hoangbang/street-smart-road-signs.smart-product-pricing-2025_test_datatrash-in-river-2024
Trash in River 2024 — Street Parade 2024 Dataset
Overview
This dataset was collected by SARA, a student initiative at ETH Zurich, to enable open research on trash presence in aquatic environments. It contains visual records of litter observed in the Limmat River in Zurich the day after the Street Parade (August 10, 2024). The dataset is intended for training and evaluating trash classification models.
Dataset summary
Collection date: August 10… See the full description on the dataset page: https://huggingface.co/datasets/SARA-smartphone-assisted-river-analysis/trash-in-river-2024.smart-retail-shelf-auditing-v1LaTeX_OCRsmart-contract-aggregators-educationalLowlight-Smartphone-Dataset
[WACV'26] Low-light Smartphone Dataset (LSD)
This is the official dataset proposed in our paper titled "Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild"
📄 Paper: arXiv💻 Code: GitHub - LSD-TFFormer
Overview
We introduce LSD, the largest in-the-wild Single-Shot Low-Light Image Enhancement (SLLIE) dataset to date.
Dataset Structure
This repository contains the following training data files:
patch_DLL_gtPatch.tar.gz… See the full description on the dataset page: https://huggingface.co/datasets/ARM4588/Lowlight-Smartphone-Dataset.FaDianDataset
Reference from
@misc{zhong2023clot,
title={Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation},
author={Zhong, Shanshan and Huang, Zhongzhan and Gao, Shanghua and Wen, Weushao and Lin, Liang and Zitnik, Marinka and Zhou, Pan},
journal={arXiv preprint arXiv:2312.02439},
year={2023}
}
FaDianDataset
This dataset is split from zhongshsh/CLoT-Oogiri-GO. I wash and split all the chinese part.
paired_retina
Paired Retina Dataset
Dataset Summary
This dataset contains paired retinal images captured from the same patients using both tabletop and portable devices, designed to support research in cross-device retinal image analysis and diabetic retinopathy screening.
Dataset Details
Dataset Description
Curated by: Research group developing RetSyn. Will release the whole dataset after accepted.
Modality: Color Fundus Photos
The Paired Retina Dataset comprises… See the full description on the dataset page: https://huggingface.co/datasets/smartretina2025/paired_retina.DeepWheel
DeepWheel
Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation
Published in ASME Journal of Mechanical Design (2026) 148(5): 051702 · KAIST SmartDesignLab / Narnia Labs
Contents
Overview
The data, qualitatively
Dataset structure
Usage
Applications
Citation
License
Overview
DeepWheel is a synthetic automotive wheel dataset for design and performance evaluation,
generated with a generative-AI framework:… See the full description on the dataset page: https://huggingface.co/datasets/KAIST-SmartDesignLab/DeepWheel.audits-with-reasonsThis dataset builds on top of the base dataset by augmenting it using the quantized Llama3 8b instruct model by Unsloth
Namely, it:
Expands on the level of detail of the description and recommendation.
Cleans-up the code by fixing formatting and removing out-of-context comments (e.g external URLs which might confuse a model)
Adds two new fields: functionality and type (see table for more detail)
The non-vulnerable examples only have values for code, functionality and type='no vulnerability'… See the full description on the dataset page: https://huggingface.co/datasets/msc-smart-contract-auditing/audits-with-reasons.guiact_smartphone_test
GUIAct Smartphone Dataset - Test Split
This is a FiftyOne dataset with 2079 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/guiact_smartphone_test")
# Launch the App
session = fo.launch_app(dataset)
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ZhuOnR/guiact_smartphone_test.smartchef-recipes
🍽️ SmartChef Recipes Dataset
A synthetic dataset of 10,000 AI-generated recipe descriptions labeled
by meal vibe, created for the SmartChef AI application using distilgpt2
from HuggingFace Transformers.
📖 Dataset Overview
Feature
Details
Total Generated
10,000 examples
After Cleaning
9,407 examples
Modality
Text
Language
English
Generation Model
distilgpt2 (HuggingFace)
Task
Text Classification
Labels
6 vibe categories
🏷️ Vibe… See the full description on the dataset page: https://huggingface.co/datasets/adigabay2003/smartchef-recipes.amazon_smartsearchindoor-smartphone-3d-reconstruction-control
Indoor Smartphone 3D Reconstruction Control Dataset
Набор данных подготовлен для сравнения методов восстановления 3D-сцены в помещении по короткому видео со смартфона. Он содержит три небольшие indoor-сцены, очищенные публикационные видеоролики, подвыборки кадров, ручную CVAT-разметку и физически измеренные контрольные расстояния.
Датасет предназначен для оценки геометрической согласованности результатов 3D-реконструкции. Разметка не является обучающей dense-разметкой глубины:… See the full description on the dataset page: https://huggingface.co/datasets/Maksonchek/indoor-smartphone-3d-reconstruction-control.water-meter-image
Water Meter Pics - 5,000+ photos
Dataset comprises 5,000+ photos of water meters, including high-quality images, segmentation masks, and OCR labels for meter readings. Each entry provides detailed information such as the meter reading value, bounding box coordinates, and segmentation data, making it ideal for training models in utility management, automatic meter reading (AMR), and water usage analysis.- Get the data
Dataset characteristics:
Characteristic
Data… See the full description on the dataset page: https://huggingface.co/datasets/ud-smart-city/water-meter-image.spain-license-plate-dataset
License Plate Recognition - 116,237 Image
This dataset provides 116,237 vehicle images captured in Spain, serving as a robust foundation for OCR systems, license plate identification, and vehicle registration data retrieval. Every image includes a companion CSV file containing the accurate plate number and country code, making it perfect for building and validating automated text recognition solutions. - Get the data
Dataset characteristics:
Characteristic
Data… See the full description on the dataset page: https://huggingface.co/datasets/ud-smart-city/spain-license-plate-dataset.ComfyUI-Smart-Upscaler-Samplessmart-airport-test-assetsEntrnal_eyes_data_6class_allNew_not_other_resize_224garbage-classification
Trash Classification - 5,000+ photos
Dataset comprises 5,000+ photos of garbage cans featuring various capacities, types, and waste materials, designed for advancing garbage classification and waste management systems. By leveraging this dataset, researchers and developers can enhance classification systems, automate garbage collection processes, and improve strategies for reducing environmental pollution. - Get the data
Dataset characteristics:
Characteristic… See the full description on the dataset page: https://huggingface.co/datasets/ud-smart-city/garbage-classification.