datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
propagator-multimodal-pretraining-data
Propagator Multimodal Pretraining Data
This public dataset contains tokenized multimodal pretraining data prepared for the Propagator model family. It combines language, image-grounded, and speech/audio-token examples into a single training format.
This is not a raw text or image browsing dataset. The examples have already been converted into compact binary token frames for model training, with a manifest that records the source groups and file layout.
Source Code… See the full description on the dataset page: https://huggingface.co/datasets/ken-sungmin/propagator-multimodal-pretraining-data.multimodal_meme_classification_singapore
Dataset Card for Offensive Memes in Singapore Context
Dataset Details
Dataset Description
This dataset is a collection of memes from various existing datasets, online forums, and freshly scrapped contents. It contains both global-context memes and Singapore-context memes, in different splits. It has textual description and a label stating if it is offensive under Singapore society's standards.
Curated by: Cao Yuxuan, Wu Jiayang, Alistair Cheong, Theodore Lee… See the full description on the dataset page: https://huggingface.co/datasets/aliencaocao/multimodal_meme_classification_singapore.multimodal-privacy
Auditing M-LLMs for Privacy Risks: A Synthetic Benchmark and Evaluation Framework
Recent advances in multi-modal Large Language Models (M-LLMs) have demonstrated a powerful ability to synthesize implicit information from disparate sources, including images and text. These resourceful data from social media also introduce a significant and underexplored privacy risk: the inference of sensitive personal attributes from seemingly daily media content. However, the lack of benchmarks and… See the full description on the dataset page: https://huggingface.co/datasets/xaddh/multimodal-privacy.MultimodalMathBenchmarks
MultimodalMathBenchmarks
This repository contains the datasets for the paper Multiplication in Multimodal LLMs: Computation with Text, Image, and Audio Inputs (ACL Findings 2026).
It covers the public benchmark datasets and their modality assets (text, images, and audio) used to evaluate the arithmetic capabilities of multimodal LLMs.
Canonical Upload Manifest
HF path
Local source
Count
Purpose
SharedMultimodalGrid.csv
SavedData/SharedMultimodalGrid.csv… See the full description on the dataset page: https://huggingface.co/datasets/cjerzak/MultimodalMathBenchmarks.pcb-defect-multi-modal-dataset
印制电路板焊接及装联缺陷多模态数据集
面向 PCB 焊接与装联缺陷识别、资料辅助根因分析和维修建议生成的中文多模态研究数据集。数据集将光学、扫描声学显微（SAM）和 X 射线等缺陷图像与来源标签、中文任务文本，以及可用的检测测量和试验工况关联，支持视觉语言模型的数据准备、指令微调实验和检索增强问答。
版本 v3 包含 5,000 条图像／缺陷区域样本、15,000 条任务指令，覆盖焊点连接质量、焊料分布与形态、焊接层结构及元件装联位置等方面的 14 类目标缺陷：
目标缺陷
含义与标注范围
虚焊
焊接界面未形成可靠的冶金结合，可能表现为接触不稳或间歇导通。
冷焊
热过程不足导致焊料未充分熔融或未形成合格连接，结合来源的热过程与外观信息标注。
少锡
焊料量或有效焊脚不足，焊接部位的填充或覆盖不充分。
多锡
焊料过量，形成过大的焊脚或明显堆积。
连锡
焊料连接本应相互隔离的端子或焊盘，形成焊料桥接。
开焊
应有焊接连接的部位缺少完整连接，或连接部位发生分离。
润湿不良… See the full description on the dataset page: https://huggingface.co/datasets/dezoe/pcb-defect-multi-modal-dataset.solarhive-community-solar-multimodal
SolarHive Community Solar Dataset
Canonical training corpus for the SolarHive family of fine-tuned Gemma 4 models. 1,727 rows (1,713 text + 14 image-grounded).
A combined text + sky-image training corpus for community solar energy intelligence. Built to fine-tune Gemma 4 into an AI energy advisor for residential solar microgrids — answering questions about production, storage, grid mix, weather impact, maintenance scheduling, and cross-source planning, with native… See the full description on the dataset page: https://huggingface.co/datasets/Truthseeker87/solarhive-community-solar-multimodal.fashion-stylist-multimodal
👗 Fashion Stylist Multimodal Dataset
A synthetic multimodal dataset pairing structured fashion metadata, styled outfit text, and AI-generated portraits.
🎯 Overview
This dataset contains ~1,000 synthetic fashion-styling profiles, each combining:
🧬 Structured demographic & style metadata
📝 A styled outfit description with e-commerce search queries
🖼️ A generated 512×512 studio-style portrait of a fictional person wearing the outfit
The dataset was built… See the full description on the dataset page: https://huggingface.co/datasets/lihicarmeli/fashion-stylist-multimodal.gemma4-multimodal-recipe-dataset
🍳 Gemma 4 Multimodal Recipe & Food Dataset
A balanced, high-density multimodal dataset curated specifically for fine-tuning compact vision-language models (such as gemma-4-e2b-it) for visual food recognition, recipe generation, and dietary recommendation.
🔗 Upstream & Source Datasets
This dataset was created by cleaning, reformatting, and synthesizing samples across the following 5 Hugging Face sources:
Dataset
Modality
Role in Pipeline… See the full description on the dataset page: https://huggingface.co/datasets/alst10/gemma4-multimodal-recipe-dataset.svg-multimodal-rubrics
SVG Multimodal Rubrics
A multimodal dataset of SVG code generation samples with natural language descriptions and evaluation rubrics. Each sample pairs a detailed prompt (Markdown) with its corresponding SVG source code, covering animations, 3D scenes, games, and visual effects.
Designed for training and evaluating models on visual code generation — generating complex, interactive SVG artwork from natural language descriptions.
Overview
Item
Details
Samples… See the full description on the dataset page: https://huggingface.co/datasets/obaydata/svg-multimodal-rubrics.MultiModalDataset
Dataset Card for MultiModal Dataset
Dataset Description
Dataset Summary
MultiModal Dataset is a curated collection of 85,000 samples spanning three modalities: text, images, and audio. It combines high-quality web content, image-caption pairs from COCO 2017, and audio samples from AudioSet to enable comprehensive multimodal model training and evaluation.
The dataset is organized into three subsets:
fineweb: 37,500 high-quality web text samples (>8… See the full description on the dataset page: https://huggingface.co/datasets/lv12/MultiModalDataset.multimodal_rewardbench
Dataset Card for Multimodal RewardBench
🏆 Dataset Attribution
This dataset is created by Yasunaga et al. (2025).
📄 Paper: Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision Language Models
💻 GitHub Repository: https://github.com/facebookresearch/multimodal_rewardbench
I have downloaded the dataset from the GitHub repo and only modified the "Image" attribute by converting file paths to datasets.Image() for easier integration with 🤗… See the full description on the dataset page: https://huggingface.co/datasets/syhuggingface/multimodal_rewardbench.