Loading...
Loading...
Image-text pairs, instruction tuning, visual QA, cross-modal data, foundation model training data
1,947 datasets
A multimodal dataset likely containing paired images and text questions for visual question answering tasks. The dataset is published on Kaggle, but its specific size, creation date, and authorship are unknown. Columns and sample data are unavailable, limiting detailed assessment of its content.
A multimodal dataset published on Kaggle. The dataset's specific content, scale, and origin are not detailed in the available metadata. Further inspection after download is required to determine its exact composition and suitability for tasks.
MNIST VQA 09 is a dataset hosted on Kaggle. Its title suggests it is a multimodal dataset combining handwritten digit images from the MNIST corpus with question-answering tasks. The dataset's author, organization, and specific contents are unknown from the provided metadata.
MMSU is a multimodal benchmark for spoken language understanding and reasoning featuring 47 sub-tasks across linguistic domains like phonetics and prosody. Created by ddwang2000 and documented in Arxiv 2506.04779, the collection contains between 1,000 and 10,000 records.
Penguin Recap I contains metadata for 68,581,657 image records aggregated from three subsets: DataComp+COYO (57.6M records), SA-1B (9.3M records), and OpenImages (1.7M records). The dataset, published by Tencent, provides recap metadata for images but does not include the image binaries themselves.
Japanese Medical VQA 12M provides 12 million multimodal records for medical visual question answering, developed by MIL-UT and released in 2026. It consists of medical images paired with English and Japanese text across five distinct data-construction stages including captions and Q&A pairs.
S1-MMAlign contains 15.5 million image-text pairs extracted from 2.5 million open-access scientific papers across biology, chemistry, and physics. Developed by ScienceOne-AI and released in 2026, it provides a large-scale resource for aligning complex scientific imagery with textual descriptions. The dataset is designed to bridge the semantic gap in scientific multimodal learning using peer-reviewed literature.
BioReason-Pro Test Data is an evaluation dataset for protein function prediction containing proteins with Gene Ontology term annotations, GO-GPT predictions, InterPro domains, STRING protein-protein interactions, and protein metadata. It was created by wanglab and follows the CAFA framework's temporal holdout protocol. The dataset was last updated in March 2026.
Evaluation dataset for the BioReason-Pro model, containing proteins with Gene Ontology term annotations, GO-GPT predictions, InterPro domains, STRING protein-protein interactions, and protein metadata. The dataset was created by wanglab and follows the CAFA framework's temporal holdout methodology. It was last updated in March 2026.
A summary list of foundation models supported by the BioFuse embedding fusion framework. The dataset is a 9.5 KB Excel file created by Mirza Nasir Hossain and last updated on March 18, 2026. It is shared under a CC-BY-4.0 license on figshare.
Nemotron RL Instruction Following MultiTurnChat v1 is a benchmark dataset designed to test and improve large language models in complex, multi-turn conversations. It was created by NVIDIA and employs a 'model breaking' methodology, testing tasks against advanced models like Nemotron-Nano-V2 and Qwen3-235B-A22B-Thinking-2507 to expose failure modes. The dataset was last updated on March 11, 2026.
MMPD-3 contains images and morphological metrics for 30 species of medicinal plants. The dataset is hosted on Kaggle, but the author, organization, and specific data collection details are not provided. The last update date and data volume are unknown.
A multimodal dataset concerning cyberbullying, likely containing text and other media types from Indonesian-language social media sources. It was published on Kaggle, but the author, organization, and specific collection details are unknown. The last update date, row count, and file formats are also unspecified.
A dataset titled 'VLM_cosmosreason1_4B_dataset' suggests content for training Vision-Language Models. It is hosted on Kaggle, but no further metadata is available. The dataset's size, structure, and specific contents require verification after download.
A dataset containing motion and physiological sensor data collected from swimmers. The data is multimodal, likely combining time-series signals from wearable devices. It is hosted on Kaggle, but specifics on size, collection period, and authors are unknown.
BD-HazardVLM_500_final is a dataset published on Kaggle, likely designed for evaluating or training vision-language models on hazard detection tasks. The title suggests it contains 500 final entries, but specific content, columns, and authorship details are unconfirmed. Metadata is minimal; actual data quality and scope require verification after download.
A dataset of adversarial prompts designed to explicitly conflict with an AI model's standard training instincts, such as writing code without comments or refusing helpfulness norms. It focuses on 8 distinct 'anti-convention' patterns and was created by NVIDIA, with a last recorded update on March 11, 2026. The dataset uses a targeted 'model breaking' methodology to generate candidate responses for testing constraint difficulty.
Created by VLR-CVC for the ICDAR2026 competition, this multimodal dataset contains approximately 1,000 document-question pairs focused on complex reasoning. It spans eight distinct domains including business reports, scientific papers, slides, posters, maps, comics, infographics, and engineering drawings.
115,732 pairwise human preference labels compare outputs from 4 frontier video generation models. Annotators from Datapoint AI evaluated videos across 3 quality dimensions, using 417 unique prompts and 11 motion categories. The dataset was last updated in March 2026.
SimWorld-AI created SimWorld, an open-ended realistic simulation platform for developing and evaluating LLM and VLM AI agents. The platform supports complex physical and social environments, with a last recorded update on 2026-03-14. The description indicates support for importing customized environments and agents.