datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
EmbodiedGenDatahttps://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Gallery-Explorer
waqfeya-library
Waqfeya Library
📖 Overview
Waqfeya is one of the primary online resources for Islamic books, similar to Shamela. It hosts more than 10,000 PDF books across over 80 categories.
In this dataset, we processed the original PDF files using Google Document AI APIs and extracted their contents into two additional formats: TXT and DOCX.
📊 Dataset Contents
The dataset includes 22,443 PDF files (spanning 8,978,634 pages) representing 10,150 Islamic books. Each book is… See the full description on the dataset page: https://huggingface.co/datasets/ieasybooks-org/waqfeya-library.agent-reward-bench
AgentRewardBench
💾Code
📄Paper
🌐Website
🤗Dataset
💻Demo
🏆Leaderboard
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent TrajectoriesXing Han Lù, Amirhossein Kazemnejad*, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stańczak, Peter Shaw, Christopher J. Pal, Siva Reddy*Core Contributor
Loading dataset
You can use the huggingface_hub library to load the dataset. The dataset is available on Huggingface Hub at… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/agent-reward-bench.PhysicalAI-SimReady-Warehouse-01
NVIDIA Physical AI SimReady Warehouse OpenUSD Dataset
Dataset Version: 1.1.0
Date: May 18, 2025
Author: NVIDIA, Corporation
License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
Contents
This dataset includes the following:
This README file
A CSV catalog that enumerates all of the OpenUSD assets that are part of this dataset including a sub-folder of images that showcase each 3D asset (physical_ai_simready_warehouse_01.csv). The CSV file is organized in… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-SimReady-Warehouse-01.image-as-an-imu-finetuning
Image as an IMU: Real-world Finetuning Dataset
Official real-world finetuning dataset from Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image (ICCV 2025 Oral).
[arXiv] [Webpage] [GitHub]
PIXL, University of Oxford
Jerred Chen, Ronald Clark
Dataset Details
This dataset consists of 32 sequences of real-world motion-blurred videos in various indoor scenes, captured using the iPhone 13 camera.
dataset_train_real-world.csv and… See the full description on the dataset page: https://huggingface.co/datasets/jerredchen00/image-as-an-imu-finetuning.cmevs-erp-eval
CM-EVS: A Coverage-Curated Panoramic RGB-D Dataset for Indoor Scene Understanding
CM-EVS is a curated panoramic RGB-D dataset built under a single principle: maximize the geometric coverage of a 3D scene with the fewest equirectangular (ERP) frames possible. The release is structured as one redistributable Blender indoor data archive plus four license-aware adapter packages that regenerate matched frames locally from upstream sources whose terms forbid redistribution.
v1.0… See the full description on the dataset page: https://huggingface.co/datasets/anon-cmevs-2026/cmevs-erp-eval.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.fish-vista
Dataset Card for Fish-Visual Trait Analysis (Fish-Vista)
Note that the '</Use this dataset>' option will only load the CSV files. To download the entire dataset, including all processed images and segmentation annotations, refer to Instructions for downloading dataset and images.
See Example Code to Use the Segmentation Dataset
Figure 1. A schematic representation of the different tasks in Fish-Vista Dataset.
Instructions for downloading dataset… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/fish-vista.StreamingBench
StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
🏠 Project Page |
📄 arXiv Paper |
📦 Dataset |
🏅Leaderboard
StreamingBench evaluates Multimodal Large Language Models (MLLMs) in real-time, streaming video understanding tasks. 🌟
[NEW! 2025.05.15] 🔥: Seed1.5-VL achieved ALL model SOTA with a score of 82.80 on the Proactive Output.
[NEW! 2025.03.17] ⭐: ViSpeeker achieved Open-Source SOTA with a score of 61.60 on the… See the full description on the dataset page: https://huggingface.co/datasets/mjuicem/StreamingBench.nrvbench-review
NR Video Editing Benchmark
This repository contains two non-rigid video editing benchmark subsets for evaluating instruction-driven video editing methods. Each row in metadata.csv corresponds to one editing instruction for a source video, with relative paths to the source video, extracted frames, binary masks, prompts, and evaluation questions.
The dataset card is written without author or institution identifiers so it can be used for anonymous review uploads. Before a non-anonymous… See the full description on the dataset page: https://huggingface.co/datasets/NRVBench/nrvbench-review.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llm/or-bench.umbraThe SAR data are obtained from the UMBRA Open Data Program (https://umbra.space/open-data/).
The dataset has been created combining the WorldCover by ESA (https://esa-worldcover.org/en) with the UMBRA images.
BIOME information has been extracted from the RESOLVE biome dataset (https://ecoregions.appspot.com/).
Reverse geo-coding with OSM nominatim (https://nominatim.openstreetmap.org).
(more details asap)
Authors: Federico Ricciuti, Federico Serva, Alessandro Sebastianelli
License: Same as… See the full description on the dataset page: https://huggingface.co/datasets/fedric95/umbra.CADS-dataset
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Overview
CADS is a robust, fully automated framework for segmenting 167 anatomical structures in Computed Tomography (CT), spanning from head to knee regions across diverse anatomical systems.
The framework consists of two main components:
CADS-dataset:
22,022 CT volumes with complete annotations for 167 anatomical structures.
Most extensive whole-body CT dataset… See the full description on the dataset page: https://huggingface.co/datasets/huggingface/CADS-dataset.brain-lm-alignment-ds002236
Brain–language-model alignment: ds002236 (whole-brain)
Lytle et al. 2020 — orthographic, phonological and semantic word processing in school-aged children (8.7–15.5), auditory and visual.
Paper: https://pubmed.ncbi.nlm.nih.gov/31956678/
Data: https://openneuro.org/datasets/ds002236/versions/1.0.1
Generated: 2026-10-06
Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
Read this first: does the measurement work?
Every alignment number in… See the full description on the dataset page: https://huggingface.co/datasets/BrainAlign/brain-lm-alignment-ds002236.brain-lm-alignment-ds006239
Brain–language-model alignment: ds006239 (whole-brain)
Wang et al. 2025 — word-level phonological and semantic reading tasks in children and adolescents aged 10–17.
Paper: https://www.sciencedirect.com/science/article/pii/S2352340925009692
Data: https://openneuro.org/datasets/ds006239/versions/1.0.5
Generated: 2026-10-06
Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
Read this first: does the measurement work?
Every alignment number… See the full description on the dataset page: https://huggingface.co/datasets/BrainAlign/brain-lm-alignment-ds006239.wildjailbreak
WildJailbreak Dataset Card
WildJailbreak is an open-source synthetic safety-training dataset with 262K vanilla (direct harmful requests) and adversarial (complex adversarial jailbreaks) prompt-response pairs. In order to mitigate exaggerated safety behaviors, WildJailbreaks provides two contrastive types of queries: 1) harmful queries (both vanilla and adversarial) and 2) benign queries that resemble harmful queries in form but contain no harmful intent.
Vanilla Harmful: direct… See the full description on the dataset page: https://huggingface.co/datasets/allenai/wildjailbreak.Obstacle-Detection-Dataset-YOLO
ROD-Dataset: Real-Time Obstacle Detection for Smartphone-Based Assistive Vision
24,326-image, 25-class YOLO dataset for obstacle detection
This dataset is the data product of our Real-Time Obstacle Detection (ROD) project at Amirkabir University of Technology, Tehran. The project addresses two related public-safety problems on the city sidewalk: the limited situational awareness of people living with visual impairments, and the elevated collision and fall risk for pedestrians… See the full description on the dataset page: https://huggingface.co/datasets/Abtinzandi/Obstacle-Detection-Dataset-YOLO.CADS-dataset
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Overview
CADS is a robust, fully automated framework for segmenting 167 anatomical structures in Computed Tomography (CT), spanning from head to knee regions across diverse anatomical systems.
The framework consists of two main components:
CADS-dataset:
22,022 CT volumes with complete annotations for 167 anatomical structures.
Most extensive whole-body CT dataset… See the full description on the dataset page: https://huggingface.co/datasets/4141ms/CADS-dataset.brain-lm-alignment-ds001894
Brain–language-model alignment: ds001894 (whole-brain)
Lytle et al. 2019 — longitudinal word-level phonological processing in children scanned twice, at roughly 10 and 12 years old.
Paper: https://www.nature.com/articles/s41597-019-0338-5
Data: https://openneuro.org/datasets/ds001894/versions/1.4.2
Generated: 2026-10-06
Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
Read this first: does the measurement work?
Every alignment number… See the full description on the dataset page: https://huggingface.co/datasets/BrainAlign/brain-lm-alignment-ds001894.MMAD
MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
💡 This dataset is the full version of MMAD
Content：Containing both questions, images, and captions.
Questions: All questions are presented in a multiple-choice format with manual verification, including options and answers.
Images：Images are collected from the following links：
DS-MVTec
, MVTec-AD
, MVTec-LOCO
, VisA
, GoodsAD.
We retained the mask… See the full description on the dataset page: https://huggingface.co/datasets/jiang-cc/MMAD.global-piqa-nonparallel
Global PIQA Non-Parallel
Global PIQA is a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world.
The non-parallel split covers 136 language varieties, covering five continents, 18 language families, and 24 writing systems.
In this non-parallel split, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements.
Details are in our preprint:… See the full description on the dataset page: https://huggingface.co/datasets/mrlbenchmarks/global-piqa-nonparallel.hest-benchREPID
REPID: Rendering Evaluation of Photographic Image Dataset
REPID (officially introduced as the Rendering Evaluation of Photographic Image Dataset) is a large-scale benchmark designed for Image Rendering Quality Assessment (IRQA) in paper Beyond distortions: a benchmark for subjective evaluation of image rendering quality.
Unlike traditional Image Quality Assessment (IQA) which focuses on technical degradations like noise or blur, REPID aims to model subjective human aesthetic… See the full description on the dataset page: https://huggingface.co/datasets/vsevolodpl/REPID.Sparkle
Sparkle: Realizing Lively Instruction-Guided Video Background Replacement via Decoupled Guidance
Ziyun Zeng, Yiqi Lin, Guoqiang Liang, and Mike Zheng Shou
📦 Dataset
Sparkle is a large-scale video background replacement dataset comprising ~140K high-quality source–edited video pairs. It is fully open-sourced at 🤗stdKonjac/Sparkle. For full methodology and dataset details, please refer to our paper.
The dataset is organized into five themes along different… See the full description on the dataset page: https://huggingface.co/datasets/stdKonjac/Sparkle.JailBreakV-28k
⛓💥 JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks
🌐 GitHub | 🛎 Project Page ｜ 👉 Download full datasets
If you like our project, please give us a star ⭐ on Hugging Face for the latest update.
📰 News
Date
Event
2024/07/09
🎉 Our paper is accepted by COLM 2024.
2024/06/22
🛠️ We have updated our version to V0.2, which supports users to customize their attack models… See the full description on the dataset page: https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k.Aperture_Lab_Synthetic_Aperture_Sonar_v1_1
ApertureLab Synthetic SAS Dataset v1.1
Version 1.1 (September 2026). Author: Isaac Gerg. Made with
ApertureLab; figures, the label scheme and
the changes from v1.0 are described on the
v1.1 page.
Synthetic aperture sonar images the seafloor at the resolution of a
photograph, yet labeled sonar imagery has been too scarce, too costly to
annotate and too often restricted to share for vision models to learn from it.
A simulator that knows where every object sits writes the labels… See the full description on the dataset page: https://huggingface.co/datasets/idg101/Aperture_Lab_Synthetic_Aperture_Sonar_v1_1.TACK_Tunnel_Data
TACK Tunnel Data (TTD): A Benchmark Dataset for Deep Learning-Based Defect Detection in Tunnels
Tunnels are essential elements of transportation infrastructure, but are increasingly affected by ageing and deterioration mechanisms such as cracking. Regular inspections are required to ensure their safety, yet traditional manual procedures are time-consuming, subjective, and costly. Recent advances in mobile mapping systems and Deep Learning (DL) enable automated visual inspections.… See the full description on the dataset page: https://huggingface.co/datasets/TACK-project/TACK_Tunnel_Data.eccv2026-cad-challenge-data
ECCV 2026 CAD Challenge Data
This challenge is part of the workshop The Path to Manufacturing: Evolving
3D Generation to Intelligent Computer-Aided Design.
Workshop homepage: https://3dgen-cad-workshop.github.io/
Challenge submission Space: https://huggingface.co/spaces/jingwei-xu-00/eccv2026-cad-challenge
Dataset rendering and preparation code (only .step files are required): https://github.com/DavidXu-JJ/eccv2026-cad-challenge-data-render
This repository contains the public… See the full description on the dataset page: https://huggingface.co/datasets/jingwei-xu-00/eccv2026-cad-challenge-data.awesome-egocentric-atlas
Use this dataset
from datasets import load_dataset
ds = load_dataset("cy0307/awesome-egocentric-atlas", split="train")
print(len(ds), "resources")
print(ds[0])
papers = load_dataset(
"csv",
data_files="https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas/resolve/main/awesome-egocentric-papers.csv",
split="train",
)
print(len(papers), "paper-linked resources")
Each row is one catalogued resource. Columns:
Column
Description
name
Resource name… See the full description on the dataset page: https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas.global-piqa-parallel
Global PIQA Parallel
Global PIQA is a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world.
The parallel split is a multi-parallel dataset for 131 language varieties, covering five continents, 16 language families, and 23 writing systems.
In this parallel split, each example was machine-translated from English, then manually corrected by a native speaker of the target language.… See the full description on the dataset page: https://huggingface.co/datasets/mrlbenchmarks/global-piqa-parallel.