datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
zen-multi-imagelaion2b_multi_korean_subset_with_image
laion2b_multi_korean_subset_with_image
img2dataset을 통해 다운로드에 성공한 Bingsu/laion2B-multi-korean-subset 이미지를 정리한 데이터셋입니다.
이미지는 9,800,137장입니다.
이미지는 짧은 쪽 길이가 256이 되도록 리사이즈 되었으며, 품질 100인 webp파일로 다운로드 되었습니다.
Usage
1. datasets
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/laion2b_multi_korean_subset_with_image", streaming=True, split="train")
>>> dataset.features
{'image': Image(decode=True, id=None),
'text': Value(dtype='string', id=None)… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/laion2b_multi_korean_subset_with_image.multi_reference_image_editing
Multi-Reference Instruction-Based Image Editing Dataset
Overview
This dataset contains 20,000 high-resolution image pairs and multi-modal instructions designed for training advanced image-to-image editing models. It combines two complementary example types: 10,000 reference-grounded edits, where structural or stylistic changes are driven by up to three provided visual reference images, and 10,000 occlusion-based inpainting/outpainting edits, where the model must… See the full description on the dataset page: https://huggingface.co/datasets/molbal/multi_reference_image_editing.vgllm-spar234k-multi-image-vqa-20k-sampleMolmo2-MultiImagePoint
Molmo2 Multi-Image Pointing
This dataset contains multi-image pointing/counting metadata.
This dataset is generated by extending PixMo-Points using a semantic grouping algorithm designed to maximize coverage.
Molmo2-MultiImagePoint is a part of the Molmo2 dataset collection and was used to
provide the multi-image pointing capabilities of the Molmo2 family of models.
Quick links:
📃 Paper
🎥 Blog with Videos
Columns
image_urls: list of image URLs (original source… See the full description on the dataset page: https://huggingface.co/datasets/allenai/Molmo2-MultiImagePoint.VideoEspresso_train_multi_image
VideoEspresso
This dataset is the multi-image version.
Leaderboard
Model
Params
Frames
Overall
Narrative Analysis
Event Dynamic
Preparation Steps
Causal Analysis
Theme Analysis
Contextual Analysis
Influence Analysis
Role Analysis
Interaction Analysis
Behavior Analysis
Emotion Analysis
Cooking Process
Traffic Analysis
Situation Analysis
LLaVA-Video
72B
64
66.3%
68.4%
66.2%
74.5%
62.7%
62.3%
71.6%
62.5%
63.5%
67.7%
63.2%
60.0%
75.5%
76.7%
74.0%
LLaVA-OneVision… See the full description on the dataset page: https://huggingface.co/datasets/hshjerry0315/VideoEspresso_train_multi_image.imagecode-multiMolmo2-MultiImageQA
Molmo2-MultiImageQA
Molmo2-MultiImageQA is a multi-image extension of PixMo-AskModelAnything, an instruction-tuning dataset for vision-language models. It contains human-authored question-answer pairs over multiple images with long-form answers.
Molmo2-MultiImageQA is part of the Molmo2 dataset collection and was used to train the Molmo2 family of models.
Quick links:
📃 Paper
🎥 Blog with Videos
Loading
import datasets
train_dataset =… See the full description on the dataset page: https://huggingface.co/datasets/allenai/Molmo2-MultiImageQA.sdxl_images_sb_prompts-multi_artist-seed1MultiImage-Caption
MultiImage-Caption
MultiImage-Caption is a multimodal dense captioning dataset featuring 10,602 entries designed for training, supervised fine-tuning (SFT), and evaluating multi-image Vision-Language Models (such as Qwen2-VL, LLaVA-NeXT, and PaliGemma).
Each sample pairs an interleaved set of multiple images (images list) with comprehensive, comparative, and granular descriptive captions (response) analyzing the context, subjects, and interactions across all provided images.… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/MultiImage-Caption.multi-modal-peg-in-square-hole-image-aloneThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "ur5",
"total_episodes": 11,
"total_frames": 1892,
"total_tasks": 1,
"total_videos": 33,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:11"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/hainh22/multi-modal-peg-in-square-hole-image-alone.JA-Multi-Image-VQA
JA-Multi-Image-VQA
Dataset Description
JA-Multi-Image-VQA is a dataset for evaluating the question answering capabilities on multiple image inputs.
We carefully collected a diverse set of 39 images with 55 questions in total.
Some images contain Japanese culture and objects in Japan. The Japanese questions and answers were created manually.
Usage
from datasets import load_dataset
dataset = load_dataset("SakanaAI/JA-Multi-Image-VQA", split="test")… See the full description on the dataset page: https://huggingface.co/datasets/SakanaAI/JA-Multi-Image-VQA.multi-image-composition-instruction-following
Multi-Image Composition Instruction-Following
A large-scale multimodal dataset for multi-image composition via natural language instruction-following. Each case provides 2-3 input images (characters + scene) along with detailed Chinese instructions to compose them into a single photorealistic output image.
Designed for training and evaluating models on complex image composition tasks that require understanding of character identity preservation, pose generation, scene integration… See the full description on the dataset page: https://huggingface.co/datasets/obaydata/multi-image-composition-instruction-following.NJ_multi-streetview-imagesIDKB_multi_no_image_valIDKB_multi_with_image_val21125-People-Multi-race-and-Multi-pose-Face-Images-Data
Description
다양한 인구통계학적 배경을 가진 21,125명의 얼굴 이미지로 구성된 데이터셋입니다. 동남아시아인, 백인, 흑인, 갈색 피부 인구 및 인도계 등 다양한 인종의 데이터를 포함합니다. 각 대상자는 서로 다른 얼굴 자세와 조명 조건에서 29장의 이미지가 촬영되었으며, 28장의 변형 이미지와 1장의 신원 참조 이미지로 구성됩니다. 본 데이터셋은 얼굴 인식, 얼굴 검증, 신원 매칭, 얼굴 검출 및 기타 컴퓨터 비전 분야의 다양한 작업에 활용할 수 있습니다.
자세한 내용은 아래 링크를 참고해 주세요: https://ko.nexdata.ai/datasets/computervision/1016?source=hf.kr
Data size
21,125명, 1인당 27~29장의 이미지
Race distribution
흑인 7,324명, 백인 3,830명, 갈색 피부 인구(멕시코인) 918명, 인도계… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-kr/21125-People-Multi-race-and-Multi-pose-Face-Images-Data.Multi_Scale_ImageNet
Multi-Scale_ImageNet
The dataset contains multi-scale ImageNet from Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer (arXiv:2508.14187) published in ICCV 2025.
The dataset generating pipeline is provided here: https://github.com/ashiq24/local-scale-equivariance/tree/main/datagen_ImNet
Pipeline Overview
Global Scaling
Local Scaling
If you use this dataset, please consider citing
@inproceedings{rahman2025local,
title={Local Scale… See the full description on the dataset page: https://huggingface.co/datasets/ashiq24/Multi_Scale_ImageNet.Multi_Subject_Driven_Image_Generation
Dataset Card
Dataset in ImagenHub.
Citation
Please kindly cite our paper if you use our code, data, models or results:
@article{ku2023imagenhub,
title={ImagenHub: Standardizing the evaluation of conditional image generation models},
author={Max Ku and Tianle Li and Kai Zhang and Yujie Lu and Xingyu Fu and Wenwen Zhuang and Wenhu Chen},
journal={arXiv preprint arXiv:2310.01596},
year={2023}
}
imagecode-multisdxl_images_sb_prompts-multi_artistmulti-modal-image-editvgllm-spar234k-multi-image-vqa-0.3k-type-sampleminimind-v_multi_imageFlame-Waterfall-React-Multi-Images
Flame-Waterfall-React: A Structured Data Synthesis Dataset for Multimodal React Code Generation
Flame-Waterfall-React is a dataset synthesized using the Waterfall-Model-Based Synthesis method, Advancing Vision-Language Models in Front-End Development via Data Synthesis. This dataset is designed to train vision-language models (VLMs) for React code generation from UI design mockups and specifications.
The Waterfall synthesis approach mimics real-world software development by… See the full description on the dataset page: https://huggingface.co/datasets/Flame-Code-VLM/Flame-Waterfall-React-Multi-Images.multi-edit-image-pairs
Image Editing Dataset
This dataset contains image editing examples with instructions.
Dataset Structure
instruction: Text instruction for editing
original_image: Original image before editing
edited_image: Image after applying the edit
Multi_Subject_Concepts
Dataset Card for Dataset Name
Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/ImagenHub/Multi_Subject_Concepts.llamafactory-sqa3d-traces-multiimage-vqallamafactory-sqa3d-traces-multiimage-vqavgllm-spar234k-multi-image-vqa