datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GUIOdyssey
Dataset Card for GUIOdyssey
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Paper: https://arxiv.org/pdf/2406.08451
News⭐️
Latest version of GUIOdyssey released!🎉
This updated version features a larger dataset with 8,334 episodes, as well as richer semantic annotations. Compared to the previous version, we have added more fine-grained low-level instructions, image descriptions, action intentions, and context review for each step. Additionally, we provide bounding… See the full description on the dataset page: https://huggingface.co/datasets/hflqf88888/GUIOdyssey.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-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.GUI-Net-1M
Check more details at how to use this dataset at our repo
GUI-Net-1M is the dataset we keep running the pipeline introduced from TongUI paper.
Due to large file size, we have to split image files into parts. To do the extraction of images, please use the following script:
#!/bin/bash
# Directory containing the split files
SPLIT_DIR="/mnt/bofeidisk2/tmp/baidu_experience_full/images/split_parts_baidu_experience"
OUTPUT_DIR="merged_files"
# Create output directory if it doesn't… See the full description on the dataset page: https://huggingface.co/datasets/Bofeee5675/GUI-Net-1M.GUI_BASED_PLATFORMguidelines
🎉 NEW DROP 🎉 PubMed Guidelines
We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas!
Clinical Guidelines
The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the Meditron Large Language Model (LLM). We publicly release a subset of 37K articles… See the full description on the dataset page: https://huggingface.co/datasets/epfl-llm/guidelines.Gui-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.ATO-Australian-Tax-Rulings-and-Guidance
ATO Rulings & Guidance — Australian Tax Law, Structured for AI
67,000+ Australian Taxation Office documents as RAG-ready NDJSON/CSV — Edited Private Advice, public rulings and determinations, ATO Interpretative Decisions, practical compliance guidelines, taxpayer alerts, decision impact statements, practice statements and legislative instruments. Every document parsed into structured, typed fields for legal RAG, LLM fine-tuning, and tax research automation.
Machine-readable… See the full description on the dataset page: https://huggingface.co/datasets/simplelex/ATO-Australian-Tax-Rulings-and-Guidance.easyr1-grounding-dataset-30k-not_grounded-SE-GUI-3B-2MPguiowl-aw-mix-full
GUI-Owl AndroidWorld SFT Mix — FULL / generalization
Purpose: AndroidWorld (116-task) SFT for a GUI-Owl-1.5-2B block-diffusion VLA.
This dataset is an action-balanced, source-mixed SFT corpus assembled from five Android
GUI-agent trajectory sources. It is built for in-domain supervised fine-tuning ahead of RL.
The mix deliberately includes AndroidWorld task-family coverage (via the openmobile anchor,
whose app field holds AW task-family names) and accepts in-domain overlap by… See the full description on the dataset page: https://huggingface.co/datasets/KMK040412/guiowl-aw-mix-full.GUIOdyssey
Dataset Card for GUIOdyssey
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Paper: https://arxiv.org/pdf/2406.08451
News⭐️
Latest version of GUIOdyssey released!🎉
This updated version features a larger dataset with 8,334 episodes, as well as richer semantic annotations. Compared to the previous version, we have added more fine-grained low-level instructions, image descriptions, action intentions, and context review for each step. Additionally, we provide… See the full description on the dataset page: https://huggingface.co/datasets/hsinv/GUIOdyssey.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.guitarset
GuitarSet
GuitarSet v1.1.0 (Xi et al. 2018) on HuggingFace. 360 rows × 4 audio captures per row + canonical labels derived from the original JAMS. Three documented upstream errata are corrected (see below); the original file-based distribution lives on Zenodo with a permanent DOI. CC-BY 4.0.
Schema
Column
Type
Notes
track_id
string
e.g. 00_BN1-129-Eb_comp
player
int32
0–5
style
string
comp | solo
tempo_bpm
float64
key, mode, key_mode
string
e.g. Eb… See the full description on the dataset page: https://huggingface.co/datasets/jhartquist/guitarset.fineweb-atlas
FineWeb Atlas (v0.1)
FineWeb Atlas annotates 14.9 million FineWeb documents (95.5M chunks, 10.2B tokens) with 16,790 human-readable concepts spanning entities, topics, tones, and document types. Each chunk receives ~15 concept labels on average. The release includes chunk- and document-level annotations, a concept metadata table with prevalence stats, a reverse index for concept-first retrieval, and a packed cooccurrence matrix.
For background on how the atlas was built, see the… See the full description on the dataset page: https://huggingface.co/datasets/guidelabs/fineweb-atlas.DATA_SOURCEguiowl-aw-mix-targeted
GUI-Owl AndroidWorld SFT Mix — TARGETED / in-domain
Purpose: AndroidWorld (116-task) SFT for a GUI-Owl-1.5-2B block-diffusion VLA.
This dataset is an action-balanced, source-mixed SFT corpus assembled from five Android
GUI-agent trajectory sources. It is built for in-domain supervised fine-tuning ahead of RL.
The mix deliberately includes AndroidWorld task-family coverage (via the openmobile anchor,
whose app field holds AW task-family names) and accepts in-domain overlap by… See the full description on the dataset page: https://huggingface.co/datasets/KMK040412/guiowl-aw-mix-targeted.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.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.GUIDEGUI-Net-1M-relative-annotationsguiowl-aw-mix-hybrid-packed
guiowl-aw-mix-hybrid-packed
Episode-PACKED AndroidWorld-SFT mix for GUI-Owl-1.5-2B block-diffusion VLA.
600,040 steps / 57,669 episodes (whole-episode, ordered, action-trace history). 151 shards.
Source mix: openmobile 28% (AW-app anchor), gui_odyssey 26% (long/cross-app), aitw 18% (visual ballast), androidcontrol 16% (open donor), amex 12%.
Episode-presence: type 64% / swipe 57% / terminate 45% / open 13% / answer 7%. ep_len mean 10.4, p90 20, max 60.
Validated:… See the full description on the dataset page: https://huggingface.co/datasets/KMK040412/guiowl-aw-mix-hybrid-packed.spine
GUI World Model — Spine Transitions
(s, a, s') transitions collected by walking task instructions on a live
Ubuntu desktop. Every state is captured from the running machine: a screenshot,
the accessibility tree as XML, and the rendered element table the model reads.
This set is spine only — the path an agent actually took. No branches.
Where the instructions come from
instruction_source
what it is
agentnet
Human recordings of people using their own… See the full description on the dataset page: https://huggingface.co/datasets/gui-wm/spine.australian-tax-guidance-retrieval
Australian Tax Guidance Retrieval 🏦
Australian Tax Guidance Retrieval by Isaacus is a novel, diverse, and challenging legal information retrieval evaluation dataset consisting of 112 real-life Australian tax law questions paired with expert-annotated, relevant Australian Government tax guidance and policies.
Uniquely, this dataset sources its real-life tax questions from the posts of everyday Australian taxpayers on the ATO Community forum, with relevant Australian Government… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/australian-tax-guidance-retrieval.agentnet-partial-and-fail-v1
agentnet-partial-and-fail-v1
GUI state transitions (s, a, s') walked on an Ubuntu desktop by
Qwen3.8-27B, from tasks taken from AgentNet and run inside OSWorld's
Docker environment.
Walks the judge ruled partial or failed. These are the larger half and, for a world model, the more useful one: a walk that did not finish still opened dialogs, switched tabs and changed settings, and each of those is a real transition. Measured over eighty-eight walks, a failure visits 13.5 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gui-wm/agentnet-partial-and-fail-v1.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.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.agentnet-success-v1
agentnet-success-v1
GUI state transitions (s, a, s') walked on an Ubuntu desktop by
Qwen3.8-27B, from tasks taken from AgentNet and run inside OSWorld's
Docker environment.
Walks the judge ruled had finished the task. Use these to score an agent: the trajectory is a worked example, and action_target gives the element each step was aiming at, so an answer can be marked right by control rather than by pixel.
This repository is the one-condition pool. Tasks are drawn from the 623… See the full description on the dataset page: https://huggingface.co/datasets/gui-wm/agentnet-success-v1.guiltygearstrivedualrulers
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.guide_and_master