datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Uni-GUI-Desktop-1
Uni-GUI-Desktop-1
A large-scale desktop GUI agent trajectory dataset, used as part of the training data for UI-MOPD (Multi-platform On-Policy Distillation for Continual GUI Agent Learning).
Dataset Statistics
Metric
Value
Trajectories
2,685
Total Steps
~36K
Platform
Desktop (1920x1080)
Applications
10 categories
Coordinate System
Normalized to [0, 999]
Applications
App
Description
chrome
Web browsing tasks
gimp… See the full description on the dataset page: https://huggingface.co/datasets/UI-MOPD/Uni-GUI-Desktop-1.GUI-Odyssey
Dataset Card for GUI Odyssey
News⭐️
A new and improved version of the GUIOdyssey dataset has been released! 🎉🎉
👉 Please use the latest version and refer to the updated README for the most up-to-date information.
We highly recommend using the new version for all training and evaluation!
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Latest Version of Dataset: hflqf88888/GUIOdyssey
Paper: https://arxiv.org/pdf/2406.08451
Introduction
GUI Odyssey is… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/GUI-Odyssey.guildnouketsukejoudesugazangyouwaiyananodebosswosolotoubatsushiyoutoomoimasu
Bangumi Image Base of Guild No Uketsukejou Desu Ga, Zangyou Wa Iya Nanode Boss Wo Solo Toubatsu Shiyou To Omoimasu
This is the image base of bangumi Guild no Uketsukejou desu ga, Zangyou wa Iya nanode Boss wo Solo Toubatsu Shiyou to Omoimasu, we detected 64 characters, 4480 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/guildnouketsukejoudesugazangyouwaiyananodebosswosolotoubatsushiyoutoomoimasu.Uni-GUI-OpenMobile
Uni-GUI-OpenMobile
A mobile GUI agent trajectory dataset collected on open-source Android applications via AndroidWorld, used as part of the training data for UI-MOPD (Multi-platform On-Policy Distillation for Continual GUI Agent Learning).
Dataset Statistics
Metric
Value
Trajectories
2,640
Total Steps
~25.9K
Platform
Android Mobile (1080x2400)
Applications
19 open-source apps
Coordinate System
Normalized to [0, 1000]… See the full description on the dataset page: https://huggingface.co/datasets/UI-MOPD/Uni-GUI-OpenMobile.reef-guidance-system
Dataset Card for Reef Guidance System
This dataset provides imagery used for training and evaluation of models in the Reef Guidance System. All imagery was collected by the Australian Institute of Marine Science using the ReefScan™ Transom Marine Monitoring System.
If you use this dataset in your work, please cite the associated paper: AI-driven dispensing of coral reseeding devices for broad-scale restoration of the Great Barrier Reef (citations provided at bottom of this… See the full description on the dataset page: https://huggingface.co/datasets/QCR-Underwater-Perception/reef-guidance-system.Long-Horizon-GUI-DatasetGui-agent
Gui-Agent — GUI trajectories in LIBERO/VLA format
Human GUI demonstrations from four sources, unified into a single VLA-style
intermediate representation and written as LIBERO-layout HDF5, so LIBERO/VLA
dataloaders run against GUI data unchanged.
raw source ──[adapter]──> GuiEpisode ──[writer]──> LIBERO-style HDF5
per-source the IR format- what you train on
only specific
25,872 episodes / 453,264 steps / 235 GB… See the full description on the dataset page: https://huggingface.co/datasets/Yushi123/Gui-agent.gui-odyssey-train
Dataset Card for GUI Odyssey (Train Split)
⬆️ Test split shown above, but this also represents the train split.
This is a FiftyOne dataset with 89365 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/gui-odyssey-train")
# Launch… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/gui-odyssey-train.Uni-GUI-Mobile
Uni-GUI-Mobile
A mobile GUI agent trajectory dataset, used as part of the training data for UI-MOPD (Multi-platform On-Policy Distillation for Continual GUI Agent Learning).
Dataset Statistics
Metric
Value
Trajectories
871
Total Steps
~14K
Platform
Mobile (1080x2400)
Applications
10 categories
Coordinate System
Normalized to [0, 999]
Applications
App
Description
calendar
Calendar event management
chrome
Mobile… See the full description on the dataset page: https://huggingface.co/datasets/UI-MOPD/Uni-GUI-Mobile.Uni-GUI-OpenCUA
Uni-GUI-OpenCUA
A post-processed desktop GUI agent trajectory dataset derived from OpenCUA, used as part of the training data for UI-MOPD (Multi-platform On-Policy Distillation for Continual GUI Agent Learning).
Overview
Uni-GUI-OpenCUA contains 832 trajectories with ~14K interaction steps across 11 desktop applications and task categories. The raw OpenCUA trajectories have been cleaned, filtered, and post-processed through our Unified Cross-Platform Data… See the full description on the dataset page: https://huggingface.co/datasets/UI-MOPD/Uni-GUI-OpenCUA.gui-odyssey-test
Dataset Card for GUI Odyssey (Test Split)
This is a FiftyOne dataset with 29426 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/gui-odyssey-test")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/gui-odyssey-test.GUIDE-dataset
GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation
GUI Unbiasing via Instructional-video Driven Expertise
Accepted to ECCV 2026
Project Page |
Paper |
GitHub
This dataset supports the accepted ECCV 2026 paper "GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation".
Overview
GUIDE (GUI Unbiasing via Instructional-Video… See the full description on the dataset page: https://huggingface.co/datasets/sharryXR/GUIDE-dataset.GUIGuard-Bench
GUIGuard-Bench (Public Ladder)
GUIGuard-Bench is a cross-platform GUI agent benchmark for studying privacy risks and privacy-preserving execution in multimodal GUI agents.
This public-ladder release contains 121 GUI interaction trajectories (68 Android + 53 PC) for benchmark evaluation, with 26,407 region-level privacy annotations across 2,002 screenshots.
For the anonymous review version of the evaluation toolkit, see GUIGaurd-Bench-CA4F.
Dataset Summary
GUI agents… See the full description on the dataset page: https://huggingface.co/datasets/ShaofantuoshuzhengzhiSha/GUIGuard-Bench.gui-agent-outputguiact_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.easyr1-grounding-dataset-30k-not_grounded-SE-GUI-3B-2MPguiact_websingle_test
Dataset Card for GUIAct Web-Single Dataset - Test Set
This is a FiftyOne dataset with 1410 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_websingle_test")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/guiact_websingle_test.FiftyOne-GUI-Grounding-Train
Dataset Card for FiftyOne GUI Grounding Training Set
Dataset Details
Dataset Description
This dataset contains 739 annotated GUI screenshots designed for training computer vision models to understand and interact with graphical user interfaces. The dataset uses the specialized COCO4GUI format, which extends the standard COCO detection format to handle GUI-specific features, interaction sequences, and rich metadata.
The dataset captures real user… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/FiftyOne-GUI-Grounding-Train.FiftyOne-GUI-Grounding-Train-with-Synthetic
Dataset Card for FiftyOne GUI Grounding Training Set with Synthetic Augmentation
Dataset Details
Dataset Description
This dataset represents a significant expansion of the original FiftyOne GUI Grounding Training Set, growing from 739 real GUI screenshots to 4,036 total samples through systematic synthetic data generation. The dataset combines authentic GUI interactions with carefully crafted synthetic variants designed to improve model robustness… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/FiftyOne-GUI-Grounding-Train-with-Synthetic.Uni-GUI-Desktop-2
Uni-GUI-Desktop-2
A desktop GUI agent trajectory dataset collected on OSWorld environments, used as part of the training data for UI-MOPD (Multi-platform On-Policy Distillation for Continual GUI Agent Learning).
Dataset Statistics
Metric
Value
Trajectories
1,247
Total Steps
~14.8K
Platform
Desktop (1920x1080)
Applications
10 categories
Coordinate System
Normalized to [0, 1000]
Applications
App
Count
Description… See the full description on the dataset page: https://huggingface.co/datasets/UI-MOPD/Uni-GUI-Desktop-2.MolmoPoint-GUISyn
MolmoPoint-GUISyn
MolmoPoint-GUISyn is a large-scale synthetic dataset of 36K GUI screenshots with dense pointing annotations for training GUI grounding agents. Each screenshot is a realistic simulation of a digital environment (desktop apps, mobile apps, websites) generated entirely from code, with an average of 54 annotated UI elements per image.
The data is generated using the MolmoPoint-GUISyn pipeline, with Claude Sonnet 4.6 as the coding LLM.
Quick links:
Model:… See the full description on the dataset page: https://huggingface.co/datasets/allenai/MolmoPoint-GUISyn.guiltycrown
Bangumi Image Base of Guilty Crown
This is the image base of bangumi Guilty Crown, we detected 30 characters, 2278 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters'… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/guiltycrown.GUIDElicense-plates-700ktw-privacy-guides
私路 - 隱私之路
正體中文的數位隱私教育素材
介紹如何用「開源」、「免費」、「尊重隱私」的替代品，取代主流軟體服務主流軟體服務的「免費」特質，實際上是用你我的個資所換取如果你重視隱私權、個資，卻不知從何著手。此系列會帶你一步步擺脫控制，從貪婪的個資匪徒手中，奪回被遺忘的權利
:warning: 重要聲明 :warning::本專案的文件、圖片資料採用 CC-BY-SA 4.0 許可證。若使用本專案的資料進行 AI 模型訓練、微調 或 軟體服務架設，則 衍生作品（如模型權重、程式碼）須以 AGPL-3.0 許可證開放原始碼及權重。
目錄
概論
數位隱私的重要性
中國線上服務風險
生活中洩漏的個資
開源 (開放原始碼)
隱私和資安的異同
開源和隱私的關係
民主國家擁抱監控
網路實名浪潮起因
零信任
尊重、保障隱私的免費開源選擇
搜尋引擎
電子郵件
瀏覽器… See the full description on the dataset page: https://huggingface.co/datasets/twinkle-ai/tw-privacy-guides.GUIOdyssey
cua-lite/GUIOdyssey
cua-lite preprocessed version of GUIOdyssey (hflqf88888/GUIOdyssey). A long-horizon cross-app Android mobile dataset of 8,334 task trajectories over ~128k screenshots. Produces two cohorts: use (multi-step agent episodes) and understanding (per-step screen captioning from the source description annotations).
Origin
https://huggingface.co/datasets/hflqf88888/GUIOdyssey
Load via datasets
from datasets import load_dataset
#… See the full description on the dataset page: https://huggingface.co/datasets/cua-lite/GUIOdyssey.GUI-CC
GUI-CC
GUI-CC is a benchmark for evaluating the contextual consistency of GUI world models when
they are used as agent environments rather than as isolated next-screen predictors.
A GUI world model predicts the next interface given the current screenshot and an action.
When that prediction is fed back as the next state, the rollout must stay coherent: app identity,
navigation history, created entities, selected options, and action affordances all have to remain
mutually… See the full description on the dataset page: https://huggingface.co/datasets/minuzero/GUI-CC.guitar-chords
Guitar Chords Dataset
This dataset contains images annotated for guitar chord object detection. It is a consolidated collection of several existing chord detection datasets.
Data Sources
This project aggregates and builds upon data from the following Roboflow Universe sources:
ChordDetection 2.2: school-sps5k/chorddetection2.2
Chord Detection v11: school-sps5k/chorddetection/dataset/11
Chord Gitar Detection: guztiart/chord-gitar-detection/dataset/1
Intended… See the full description on the dataset page: https://huggingface.co/datasets/dduka/guitar-chords.GUI-SD-dataguiltygearstrivedualrulers
Bangumi Image Base of Guilty Gear Strive: Dual Rulers
This is the image base of bangumi Guilty Gear Strive: Dual Rulers, we detected 37 characters, 1811 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1%… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/guiltygearstrivedualrulers.