Image to Text Datasets

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 collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing the HTML subset of…

Dataset · Image to text

prophet-mosque-library

Ieasybooks Org

Prophet's Mosque Library Overview Prophet’s Mosque Library is one of the primary resources for Islamic books. It hosts more than 48,000 PDF books across over 70 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 70,884 PDF files (spanning 23,494,042 pages) representing 48,717 Islamic books. Each book is

Dataset · Image to text

waqfeya-library

Ieasybooks Org

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

Dataset · Image to text

CT-RATE

Ibrahim Ethem Hamamci

The CT-RATE Team organizes the VLM3D Challenge VLM3D 2026 (2nd Edition) → Challenge Finals at MICCAI 2026 VLM3D 2025 (1st Edition) → Challenge Finals at MICCAI 2025 • Workshop at ICCV 2025 The CT-RATE Team is developing the MR-RATE Dataset A large-scale brain MRI dataset with paired radiology reports for training 3D Foundation Models from a Multimodal Dataset for 3D Computed Tomography

This is a standardized, high-efficiency version of the JUNJIE99/MegaPairs dataset. Why use this version? The original dataset is distributed as a massive Tar archive containing millions of images, accompanied by a separate JSONL annotation file. Each row in the dataset represents a Universal Retrieval Pair (Query -> Target). You can load this dataset directly using the datasets library. This is the easiest way. The library handles the Arrow files automatically. from datasets import loaddataset dataset = loaddataset("86Cao/MegaPairs-Standard", split="train") print(f"Total samples: {len(dataset)}") sample = dataset[0] print(f"Text: {sample['querytexts'][0]}") sample['queryimage'].show() #…

Picbreeder-VLM Archive Every image evolved by the swarm of vision-language-model "breeders" in In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models (GECCO 2026), together with the CPPN genomes that produced them, the agents' reasoning transcripts, the lineage graphs, and the analysis artifacts behind the paper and blog. The original Picbreeder (Secretan et al., 2008) let crowds of humans collaboratively evolve images from CPPN