datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OmniWorld[ICLR 2026] OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling
🎉NEWS
[2026.3.21] 🔥 OmniWorld-Game with Metric Scale is now released! Check out our latest model Pi3X (an enhanced version of Pi3), which leverages this data to achieve better performance!
[2026.1.26] 🎉 OmniWorld was accepted by ICLR 2026!
[2026.1.7] Update OmniWorld-Game, release RH20T-Robot, RH20T-Human, Ego-Exo4D, EgoDex, Epic-Kitchens.
[2025.11.11] The OmniWorld is… See the full description on the dataset page: https://huggingface.co/datasets/InternRobotics/OmniWorld.OmniEdit-Filtered-1.2M
OmniEdit
In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision
from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality.
📃Paper | 🌐Website |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M.OmniAction
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
📖 arXiv Paper (Accepted to ICLR 2026 🎉) |
🌐 Website |
🤗 Model |
🤗 Dataset |
🛠️ Github |
Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision–Language–Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/OmniAction.OmniVitacOmniDocBench
OmniDocBench
English | 简体中文
OmniDocBench is an evaluation dataset for diverse document parsing in real-world scenarios, with the following characteristics:
Diverse Document Types: The evaluation set contains 1651 PDF pages, covering 10 document types, 5 layout types and 5 language types. Coverage includes academic literature, research and financial reports, newspapers, textbooks, exam papers, magazines, handwritten notes, historical documents, and more.
Rich Annotations:… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/OmniDocBench.OmniRooms
UniSHARP:
Universal Sharp Monocular View Synthesis
Meixi Song1 ·
Dizhe Zhang1,* ·
Hao Ren1 ·
Ruiyang Zhang1 ·
Bo Du2 ·
Ming-Hsuan Yang3 ·
Lu Qi1,2,*
1Insta360 Research · 2Wuhan University · 3University of California, Merced
UniSHARP extends SHARP-style photorealistic monocular view synthesis to universal camera systems. Given a single image from a perspective, wide-FoV, fisheye, or panoramic camera, UniSHARP predicts a 3D Gaussian representation and… See the full description on the dataset page: https://huggingface.co/datasets/Insta360-Research/OmniRooms.omnibioai-sif-images
OmniBioAI SIF Images 🧬
500+ native ARM64 Singularity (SIF) container images for bioinformatics,
built on NVIDIA DGX (aarch64).
Tool Categories
Category
Tools
Genomics & Alignment
BWA, STAR, HISAT2, Minimap2, Bowtie2
Variant Calling
GATK, DeepVariant, Clair3, Mutect2
RNA-seq
Salmon, Kallisto, DESeq2, edgeR
Single Cell
Seurat, Scanpy, Cell Ranger, Harmony
Epigenomics
MACS2, deepTools, Bismark
Metagenomics
Kraken2, MetaPhlAn, QIIME2
Proteomics… See the full description on the dataset page: https://huggingface.co/datasets/omnibioai/omnibioai-sif-images.OmniCorpus-CC
🐳 OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
⭐️ NOTE: Several parquet files were marked unsafe (viruses) by official scaning of hf, while they are reported safe by ClamAV and Virustotal.
We found many false positive cases of the hf automatic scanning in hf discussions and raise one discussion to ask for a re-scanning.
This is the repository of OmniCorpus-CC, which contains 988 million image-text interleaved documents collected from Common… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/OmniCorpus-CC.OmniCorpus-CC-210M
🐳 OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
This repository contains 210 million image-text interleaved documents filtered from the OmniCorpus-CC dataset, which was sourced from Common Crawl.
Repository: https://github.com/OpenGVLab/OmniCorpus
Paper (ICLR 2025 Spotlight): https://arxiv.org/abs/2406.08418
OmniCorpus dataset is a large-scale image-text interleaved dataset, which pushes the boundaries of scale and diversity by encompassing… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/OmniCorpus-CC-210M.omni-refiner-videoOmniRet-train
OmniRet training dataset
OmniRet-train is the training-data release for
OmniRet, a unified retrieval model for
text, image, video, and audio. This card documents the released snapshot for
researchers training or analyzing OmniRet.
Dataset summary
The release contains 6,405,109 query rows and 7,119,841 candidate rows from 30
datasets. It covers 15 retrieval directions across text (T), image (I), video
(V), and audio (A). The OmniRet paper reports this corpus as… See the full description on the dataset page: https://huggingface.co/datasets/chuonghm/OmniRet-train.Omni-MATH
Dataset Card for Omni-MATH
Recent advancements in AI, particularly in large language models (LLMs), have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g., OpenAI o1 achieves 94.8% on MATH dataset), indicating their inadequacy for truly challenging these models. To mitigate this limitation, we propose a comprehensive and challenging benchmark specifically designed… See the full description on the dataset page: https://huggingface.co/datasets/KbsdJames/Omni-MATH.ASMR-Archive-Processed
ASMR-Archive-Processed (WIP)
Update (2026-04-03): This dataset has reached the Hugging Face Public Storage Limit. After contacting support, we were informed that the only option is to pay for a storage expansion. Consequently, updates to this dataset are now suspended.
Work in Progress — expect breaking changes while the pipeline and data layout stabilize.
This dataset contains ASMR audio data sourced from DeliberatorArchiver/asmr-archive-data-01 and… See the full description on the dataset page: https://huggingface.co/datasets/OmniAICreator/ASMR-Archive-Processed.OmniAction-LIBERO
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
📖 arXiv Paper (Accepted to ICLR 2026 🎉) |
🌐 Website |
🤗 Model |
🤗 Dataset |
🛠️ Github |
Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision–Language–Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/OmniAction-LIBERO.OmniThoughtV_Raw_1.8M
Dataset Introduction
OmniThoughtV is a large-scale multimodal long-chain-of-thought dataset distilled from the FineVision dataset using Alibaba Cloud's AI platform (PAI) distillation toolkit, EasyDistill. This dataset establishes a transparent and reproducible data distillation pipeline, enabling efficient construction of multimodal reasoning chains of thought. Fine-tuning smaller models with this dataset effectively endows them with stronger reasoning capabilities and enhances… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/OmniThoughtV_Raw_1.8M.OmniReasoner-SFT
OmniReasoner-SFT
OmniReasoner-SFT is a mixed-source, research-only supervised fine-tuning dataset
for audio-visual and long-video reasoning. It contains two-stage cold-start SFT
trajectories with interval selection, zoom-in evidence, and final answers.
Contents
data/train.jsonl: HF-ready training JSONL with repo-relative media paths.
media/: raw and derived media referenced by train.jsonl.
manifests/media_manifest.jsonl: media inventory with repo paths, source
family… See the full description on the dataset page: https://huggingface.co/datasets/Rocky131/OmniReasoner-SFT.Real5-OmniDocBench
Real5-OmniDocBench
A Full-Scale Physical Reconstruction Benchmark for Robust Document Parsing in the Wild
Leaderboard | Overview | Dataset | Evaluation | Submit Results | Citation
Real5-OmniDocBench measures the robustness of document parsing systems under five physical acquisition conditions: Scanning, Warping, Screen-Photography, Illumination, and Skew. It reconstructs the same 1,355 pages from OmniDocBench v1.5 in every condition, producing 6,775 images in total. The one-to-one… See the full description on the dataset page: https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench.Omni-CAD-Subset-CompleteAA-Omniscience-Public
Public Dataset for AA-Omniscience: Evaluating Cross-Domain Knowledge Reliability in Large Language Models
AA-Omniscience-Public contains 600 questions across a wide range of domains used to test a model’s knowledge and hallucination tendencies.
Leaderboard and detailed results
Paper
Introduction
We introduce AA-Omniscience, a benchmark dataset designed to measure a model’s ability to both recall factual information accurately across domains, and correctly… See the full description on the dataset page: https://huggingface.co/datasets/ArtificialAnalysis/AA-Omniscience-Public.Omni-iEEGOmnisharing_DB_SampleData
Overview
The embodied intelligence industry is currently facing significant development challenges. The most critical issue is the lack of high-quality data, particularly omnimodal data that integrates force and tactile sensing. The PaXini introduces the PX OmniSharing Dataset, built on the PaXini Super EID Factory, enabling large-scale, high-fidelity human data collection across diverse tasks and scenarios.
The dataset includes multi-dimensional tactile data, multi-view visual… See the full description on the dataset page: https://huggingface.co/datasets/paxini/Omnisharing_DB_SampleData.Belle_1.4M-SLAM-Omni
Belle_1.4M
This dataset is prepared for the reproduction of SLAM-Omni.
This is a multi-round Chinese spoken dialogue training dataset. For code and usage examples, please refer to the related GitHub repository: X-LANCE/SLAM-LLM (examples/s2s)
🔧 Modifications
Data Filtering: We removed samples with excessively long data.
Speech Response Tokens: We used CosyVoice to synthesize corresponding semantic speech tokens for the speech response. These tokens, represented as… See the full description on the dataset page: https://huggingface.co/datasets/worstchan/Belle_1.4M-SLAM-Omni.OmniThoughtV_Filter_0.5M
Dataset Introduction
OmniThoughtV is a large-scale multimodal long-chain-of-thought dataset distilled from the FineVision dataset using Alibaba Cloud's AI platform (PAI) distillation toolkit, EasyDistill. This dataset establishes a transparent and reproducible data distillation pipeline, enabling efficient construction of multimodal reasoning chains of thought. Fine-tuning smaller models with this dataset effectively endows them with stronger reasoning capabilities and enhances… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/OmniThoughtV_Filter_0.5M.omnibox-backupsOmnimodal-Agent-SFT-2K
OmniGAIA: Omni-Modal General AI Assistant Benchmark
📄 Paper
•
💻 Code & Demo
•
🤗 Dataset & Model
•
📈 Leaderboard
This dataset contains omni-modal agent supervised fine-tuning (SFT) trajectories in the LlamaFactory SFT data format. You can directly follow LlamaFactory's instructions to fine-tune your omni-modal LLMs.OmniGAIA is a benchmark for Omni-Modal General AI Assistants that jointly reason over vision, audio, and language with external tools. It is… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/Omnimodal-Agent-SFT-2K.OmniAction
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
📖 arXiv Paper (Accepted to ICLR 2026 🎉) |
🌐 Website |
🤗 Model |
🤗 Dataset |
🛠️ Github |
Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision–Language–Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue… See the full description on the dataset page: https://huggingface.co/datasets/hosam12kalad/OmniAction.pubmed-faiss-indexesVoiceAssistant-400Komnigibson-robot-assets
omnigibson-robot-assets
This repo contains the robot assets and the state mesh assets for OmniGibson.
Pushing updates
First, make sure you have updated the VERSION file. Every zipped release must have a higher version.
This can go ahead of the OmniGibson version.
echo "9999.9.9" > VERSION
Then commit the change to main before building the archive, since git archive only packages committed files:
git status # Verify it contains the stuff you need
git add -A && git… See the full description on the dataset page: https://huggingface.co/datasets/behavior-1k/omnigibson-robot-assets.Timechat-OmniCaptioner-42K
TimeChat-Captioner: Scripting Multi-Scene Videos with Time-Aware and Structural Audio-Visual Captions
🌟 Overview
TimeChat-Captioner is a multimodal model designed to generate detailed, time-aware, and structurally coherent captions for multi-scene videos. It effectively coordinates visual and audio information to provide comprehensive video descriptions.
🌐 Project Page: timechat-captioner.github.io
🏠 Model: TimeChat-Captioner (7B)
📚 Train Dataset:… See the full description on the dataset page: https://huggingface.co/datasets/yaolily/Timechat-OmniCaptioner-42K.