datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Open-Sora-Plan-v1.1.0
Annotation
We resized the dataset to 1080p for easier uploading. Therefore, the original annotation file might not match the video names. Please refer to this https://github.com/PKU-YuanGroup/Open-Sora-Plan/issues/312#issuecomment-2197312973
Pexels
Pexels consists of multiple folders, but each folder exceeds the size limit for Huggingface uploads. Therefore, we divided each folder into 5 parts. You need to merge the 5 parts of each folder first, and then extract each… See the full description on the dataset page: https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0.cc12m-wds
Dataset Card for Conceptual Captions 12M (CC12M)
Dataset Summary
Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training.
Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).
Usage
This instance of Conceptual Captions is in webdataset .tar format. It can be used with webdataset library or upcoming releases of Hugging Face datasets.… See the full description on the dataset page: https://huggingface.co/datasets/pixparse/cc12m-wds.PhysicalAI-WorldModel-Synthetic-Physical-Interaction-Scenes
PhysicalAI-WorldModel-Synthetic-Physical-Interaction-Scenes Dataset Card
Dataset Description
PhysicalAI-WorldModel-Synthetic-Physical-Interaction-Scenes is a large-scale synthetic dataset of physically-simulated multi-object interaction scenes, generated using NVIDIA Isaac Sim and the PhysX physics engine. It is designed to train and evaluate AI models on physical reasoning, rigid body dynamics, optical flow, depth estimation, and scene understanding.
Each clip… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-WorldModel-Synthetic-Physical-Interaction-Scenes.MINT-1T-PDF-CC-2023-23
🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/MINT-1T-PDF-CC-2023-23.MINT-1T-PDF-CC-2023-14
🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/MINT-1T-PDF-CC-2023-14.MINT-1T-PDF-CC-2024-10
🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/MINT-1T-PDF-CC-2024-10.cc3m-wds
Dataset Card for Conceptual Captions (CC3M)
Dataset Summary
Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the… See the full description on the dataset page: https://huggingface.co/datasets/pixparse/cc3m-wds.pixelprose-shards
PixelProse Sharding Tars
arXiv | public-released version: pixelprose | JSON-only version: pixelprose-jsons
summary
Each tar file is approximately 500-600 MB, friendly for fast on-the-fly sampling, filtering, and loading in dataloaders.
Each tar file contains triplets of images, text, and JSON files. The *.txt files contain the raw original captions, while the *.json files include all the relevant information.
Due to Gemini-1.0 internal version changes during the… See the full description on the dataset page: https://huggingface.co/datasets/pixelprose/pixelprose-shards.pd12m-fullThis dataset is the downloaded variant of Spawning/PD12M. More specifically, this dataset
is compatible with webdataset. It was made public after obtaining permission
from the original authors of the dataset.
You can use the following to explore the dataset with webdataset:
import webdataset as wds
dataset_path = "pipe:curl -s -f -L https://huggingface.co/datasets/sayakpaul/pd12m-full/resolve/main/{00155..02480}.tar"
dataset = (
wds.WebDataset(dataset_path… See the full description on the dataset page: https://huggingface.co/datasets/Spawning/pd12m-full.pdfa-eng-wds
Dataset Card for PDF Association dataset (PDFA)
Dataset Summary
PDFA dataset is a document dataset filtered from the SafeDocs corpus, aka CC-MAIN-2021-31-PDF-UNTRUNCATED. The original purpose of that corpus is for comprehensive pdf documents analysis. The purpose of that subset differs in that regard, as focus has been done on making the dataset machine learning-ready for vision-language models.
An example page of one pdf document, with added bounding boxes… See the full description on the dataset page: https://huggingface.co/datasets/pixparse/pdfa-eng-wds.danbooru
Danbooru 2024 Dataset
Danbooru 2024 数据集
A collection of images from Danbooru website, organized and packaged by ID sequence. This dataset is for research and learning purposes only.
本数据集收集了来自 Danbooru 网站的图像，按 ID 顺序组织打包。该数据集仅用于研究和学习目的。
Dataset Description
数据集描述
This dataset contains image resources from Danbooru website, updated to ID 8380648 (Update time: 2024-11-03).
本数据集包含来自 Danbooru 网站的图像资源，更新至 ID 8380648（更新时间：2024-11-03）。
Data… See the full description on the dataset page: https://huggingface.co/datasets/picollect/danbooru.Mobile-O-Post-Train
Mobile-O Post-Training Data
Unified Multimodal Post-Training · ~105K Quadruplet Samples
📌 Overview
This dataset is used for Stage 3: Unified Multimodal Post-Training of Mobile-O, a unified multimodal model for on-device understanding and generation.
The goal of this stage is to jointly improve both image generation and visual understanding through a multi-task objective using quadruplet samples.
📊 Dataset Format
Each sample is a quadruplet consisting of:… See the full description on the dataset page: https://huggingface.co/datasets/Amshaker/Mobile-O-Post-Train.BLIP3o-Pretrain-Long-Caption
BLIP3o Pretrain Long-Caption Dataset
This collection contains 27 million images, each paired with a long (~120 token) caption generated by Qwen/Qwen2.5-VL-7B-Instruct.
Download
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="BLIP3o/BLIP3o-Pretrain-Long-Caption",
repo_type="dataset"
)
Load Dataset without Extracting
You don’t need to unpack the .tar archives, use WebDataset support in 🤗datasets instead:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption.MINT-1T-PDF-CC-2023-50
🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/MINT-1T-PDF-CC-2023-50.BLIP3o-Pretrain-Short-Caption
BLIP3o Pretrain Short-Caption Dataset
This collection contains 5 million images, each paired with a short (~20 token) caption generated by Qwen/Qwen2.5-VL-7B-Instruct.
Download
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="BLIP3o/BLIP3o-Pretrain-Short-Caption",
repo_type="dataset"
)
Load Dataset without Extracting
You don’t need to unpack the .tar archives, use WebDataset support in 🤗datasets instead:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Short-Caption.scientific-papersPE-Video
PE Video Dataset (PVD)
[📃 Tech Report]
[📂 Github]
The PE Video Dataset (PVD) is a large-scale collection of 1 million diverse videos, featuring 120,000+ expertly annotated clips. The dataset was introduced in our paper "Perception Encoder".
Overview
PE Video Dataset (PVD) comprises 1M high quality and diverse videos. Among them, 120K videos are accompanied by automated and human-verified annotations. and all videos are accompanied with video description and keywords.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PE-Video.WMGStereo
What Makes Good Synthetic Training Data for Zero-Shot Stereo Matching? (WMGStereo)
Paper | GitHub
WMGStereo is a procedural dataset generator specifically optimized for zero-shot stereo matching performance. This repository contains the WMGStereo-150k dataset, a large-scale synthetic training dataset featuring indoor, nature, and dense "flying" scenes.
Dataset Download
You can download the dataset using the huggingface-cli:
pip install huggingface-cli
huggingface-cli… See the full description on the dataset page: https://huggingface.co/datasets/princeton-vl/WMGStereo.minty-astro-ph
MINT-1T ArXiv Astro-ph
An astronomy-focused subset of mlfoundations/MINT-1T-ArXiv, filtered to include only papers from the astro-ph arXiv category (including cross-listed papers).
Overview
Papers
~845k
Total size
~804 GB
Format
WebDataset tar shards
Shards
287 (astro-ph-00000.tar to astro-ph-00286.tar)
Shard size
~3 GB each
Source
MINT-1T (Awadalla et al., 2024)
Data Format
Each tar shard contains paired files per paper:… See the full description on the dataset page: https://huggingface.co/datasets/Smith42/minty-astro-ph.laion-audio-previewolmoearth_pretrain_datasetThis is the pre-training dataset for training the OlmoEarth pre-trained remote sensing foundation models.
Documentation is on GitHub at https://github.com/allenai/olmoearth_pretrain/blob/main/docs/Pretraining-Dataset.md
The dataset is released under CC BY 4.0. It includes data from the following sources:
Sentinel-2 L2A imagery from the European Space Agency, available under the Copernicus Sentinel Data and Service Legal Notice
Sentinel-1 GRD IW vv+vh imagery from the European Space Agency… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmoearth_pretrain_dataset.Physical-AI-AV-US
PhysicalAI-AV-SFT
Supervised fine-tuning (SFT) dataset for an autonomous-vehicle vision-language
waypoint-prediction model. Contains 2,789,773 samples from 150 000
driving scenes (18 seconds per scene, sampled at 1 Hz) recorded in the
United States.
Format
WebDataset — 100 uncompressed .tar shards,
each containing pairs of files per sample:
Entry
Description
{key}.png
Front-facing wide-angle camera frame (640 × 360 px)
{key}.json
Metadata (see schema below)… See the full description on the dataset page: https://huggingface.co/datasets/tom-jerry-123/Physical-AI-AV-US.pickapic_v2_webdatasetwebdataset archive of yuvalkirstain/pickapic_v2.
Dataloading code can be found here.
marianne_pdf_7idl-wds
Dataset Card for Industry Documents Library (IDL)
Dataset Summary
Industry Documents Library (IDL) is a document dataset filtered from UCSF documents library with 19 million pages kept as valid samples.
Each document exists as a collection of a pdf, a tiff image with the same contents rendered, a json file containing extensive Textract OCR annotations from the idl_data project, and a .ocr file with the original, older OCR annotation. In each pdf, there may be from 1 to up… See the full description on the dataset page: https://huggingface.co/datasets/pixparse/idl-wds.marianne_pdf_9Mobile-O-Pre-Train
Mobile-O Pre-Training Data
Cross-Modal Alignment · 9M Text-Image Pairs
📌 Overview
This dataset is used for Stage 1: Cross-Modal Alignment pre-training of Mobile-O, a unified multimodal model for on-device understanding and generation.
The goal of this stage is to align the DiT diffusion decoder and Mobile Conditioning Projector (MCP) with the frozen VLM backbone using large-scale text-image pairs.
📊 Dataset Composition
Source
Samples
Description… See the full description on the dataset page: https://huggingface.co/datasets/Amshaker/Mobile-O-Pre-Train.Puffin-4M
Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation
📖 Project Page ｜ 🖥️ GitHub | 🤗 Hugging Face | 📑 Paper
Dataset Details
Datasets and benchmarks that span vision, language, and camera modalities remain scarce in the domain of spatial multimodal intelligence.
To address this gap, we introduce Puffin-4M, a large-scale, high-quality dataset comprising 4 million vision-language-camera… See the full description on the dataset page: https://huggingface.co/datasets/KangLiao/Puffin-4M.cifar100-pythonmarianne_pdf_5