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Image-text pairs, instruction tuning, visual QA, cross-modal data, foundation model training data
1,956 datasets
MER2024 is a large-scale multimodal dataset released for the MER24 Challenge at IJCAI. It builds upon the MER23 and MRAC23 datasets from ACM Multimedia, expanding data volume and task diversity. The dataset aims to advance robust and practical multimodal emotion recognition.
A multimodal dataset for vision-language model training, hosted on HuggingFace by author Journey9ni. The dataset was last updated in June 2025 and is categorized as containing up to 100,000 entries. It is designed for tasks involving the 3R framework.
NVIDIA, UC Berkeley, and UCSF released this collection of 100,000 to 1,000,000 records in 2025 for training Describe Anything Models (DAM). The data consists of localized image and video captions stored in WebDataset tar files to support vision-language tasks.
S3E is a multimodal dataset for collaborative Simultaneous Localization and Mapping (SLAM) created by PengYu-Team. The dataset was last updated on May 15, 2025. It is designed for multi-robot systems and includes experimental sequences captured in a laboratory environment.
Synthetic annotations for images and documents created by Facebook for the PLM model. The dataset includes generated captions for images from SA1B, OpenImages, and Object365, and question-answer pairs for documents from ArXivQA, UCSF, and PDFAcc. The dataset was last updated on April 21, 2025.
DLC-Bench is a dataset for benchmarking detailed and localized image and video captioning. It was created by researchers from NVIDIA, UC Berkeley, and UCSF, including Long Lian, Yifan Ding, and others. The dataset was last updated on the Hugging Face platform on April 24, 2025.
Synthetic annotations for video understanding tasks, covering the YT-1B and Ego4d datasets. The dataset includes video captions and multiple-choice question-answer pairs, as described in the associated technical report. It was created by Facebook and last updated on the Hugging Face platform in April 2025.
Mpdocvqa Corpus is a multimodal dataset published on HuggingFace by author AHS-uni. The dataset was last updated on June 8, 2025. Its specific content and scale are unknown from the provided metadata.
This DPO dataset contains pairs of harmful prompts and model responses derived from the LLM-LAT/harmful-dataset. It reconfigures the preference structure by labeling standard model refusals as 'rejected' and the original harmful or incorrect answers as 'chosen'.
FunBench is a novel visual question answering benchmark designed to evaluate multimodal large language models' fundus reading skills. The dataset was created by AIMClab-RUC and last updated on May 14,ๆไปฌๅ็ฐไบไธไธช้ฎ้ขใ 2025. Code and a description are available on a linked GitHub repository.
April 2025 is the last update date for this dataset of 1.6 million pathology image-text pairs. It was created by jamessyx and is intended for training Vision Language Models (VLMs) like CLIP. The dataset is designed to support applications in pathology, such as zero-shot image classification and Whole Slide Image analysis.
HistBench is a benchmark dataset introduced in the paper 'On Path to Multimodal Historical Reasoning: HistBench and HistAgent'. The dataset is hosted on HuggingFace by the author jiahaoq and was last updated on May 27, 2025. Its specific size and structure are not detailed in the provided metadata.
A collection of filtered image-text pairs from academic resources, used for pre-training the Open-Qwen2VL multimodal large language model. The dataset includes subsets like ccs_ebdataset, derived from CC3M-CC12M-SBU and filtered by CLIP, and datacomp_medium_dfn_webdataset. It was created by weizhiwang and last updated on April 16, 2025.
Magma is a foundation model for multimodal AI agents developed by researchers from Microsoft Research, University of Maryland, University of Wisconsin-Madison, KAIST, and University of Washington. The dataset, last updated on April 12, 2025, is associated with a project page, arXiv paper, and GitHub repository. It likely contains multimodal data for training and evaluating AI agents capable of processing and reasoning across different data types.
MM-RLHF is a project for aligning Multimodal Large Language Models with human preferences. The release includes a high-quality alignment dataset and a strong critique-based reward model. The project was open-sourced by yifanzhang114 in February 2025.
FUSION-10M is a large-scale dataset of image-caption pairs designed for pretraining multimodal AI models. It builds upon established datasets like LLaVA, ShareGPT4, and PixelProse and includes 2 million synthesized task-specific pairs. The dataset was created by author starriver030515 and was last updated in April 2025.
A dataset created by remyxai and last updated on April 23, 2025. It is designed for training LLaVA-style Vision-Language Models and contains synthesized spatial reasoning traces. The data was generated using VQASynth from a subset of images in the localized narratives split of the cauldron.
A text dataset derived from the LLM-Tuning-Safety/HEx-PHI dataset, intended for research on large language model safety alignment. The dataset was created by Unispac and last updated on April 23, 2025. Its specific content and scale are not detailed in the provided metadata.
LiveBench is a benchmark for large language models designed to limit test set contamination by releasing new questions monthly. Questions are based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses. It was created by 'livebench' and last updated in April 2025.
SWE-bench Multimodal provides 617 task instances for evaluating AI systems on real-world software engineering problems. The dataset, created by SWE-bench, was last updated on April 29, 2025. It is designed to test the ability of language models to resolve actual GitHub issues.