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Image-text pairs, instruction tuning, visual QA, cross-modal data, foundation model training data
1,956 datasets
COCO-QA Vietnamese is a fully translated Vietnamese version of the popular COCO-QA dataset for Visual Question Answering (VQA) tasks. It contains over 117,684 image-based question-answer pairs translated into Vietnamese, with answers limited to one word. The dataset was created by ThucPD and last updated on June 8, -2025.
BigDocs-7.5M is a dataset created by ServiceNow for training multimodal models on document and code tasks, as described in the associated arXiv paper. The dataset was last updated on June 20, 2025, and is hosted on Hugging Face. It appears to contain both text and image data, with some parts distributed using an image identifier column that requires a provided script to reconstruct.
NVIDIA's Nemotron Content Safety Dataset V2 contains 33,416 annotated interactions between humans and LLMs, released in June 2025. It provides structured training, validation, and test splits curated from human preference data to support safety alignment and toxicity detection.
Developed by the MIT Visualization Group (mitvis) and updated in 2025, VisText is a benchmark dataset for chart captioning. It provides paired chart images and captions to evaluate how models interpret visual data representations.
Simple Image Captions provides a collection of image-text pairs for multimodal tasks. The dataset contains at least 1,000 entries, as indicated by its size category, and was uploaded by user 'uygarkurt' to Hugging Face in August 2025.
EEE-Bench is a multimodal benchmark comprising 2,860 problems across 10 electrical and electronics engineering subdomains, including analog circuits and control systems. It was created by afdsafas and last updated on June 23, 2025. The benchmark is designed to evaluate the practical engineering capabilities of large multimodal models using complex visual inputs.
A collection of 10,000 medical image-reasoning pairs converted from the LLaVA-Med dataset into MedGemma's structured reasoning format. The dataset was created by author Manusinhh and last updated on June 29, 2025. Each sample contains an original medical image, step-by-step diagnostic reasoning, a final diagnosis with supporting evidence, and clinically relevant web search terms.
Text2CAD is a dataset for generating sequential computer-aided design (CAD) operations from text prompts. The dataset was created by Mohammad Sadil Khan, Sankalp Sinha, Talha Uddin Sheikh, Didier Stricker, Sk Aziz Ali, and Muhammad Zeshan Afzal. The dataset page was last updated on June 11, 2025.
GameQA-5K is a dataset of 5,000 training samples extracted from the larger GameQA-140K dataset. It was created by the OpenMOSS-Team and published on Hugging Face in June 2025 for use in training models via the GRPO method. The data is synthesized from game code to enhance multimodal reasoning in vision-language models.
5,047 real-world indoor scenes captured using Apple's ARKit framework, preprocessed for SpatialLM training. The dataset is formatted for oriented object bounding box detection with large language models. It was created by Gen3DF and last updated on June 30, 2025.
20,000 samples combine questions and images from three established VQA datasets: AOKVQA, Path-VQA, and TDIUC. This medium-sized benchmark is designed to test the multi-domain knowledge of vision-language models. It was created by dutta18 for educational and research purposes, with copyright retained by the original dataset owners.
1,639 English-language web screenshots from over 100 websites are paired with natural-language instructions and pixel-level click targets. The dataset provides a high-quality benchmark for evaluating multimodal navigation models, created by Hcompany and released in June 2025.
47,400 curated problems spanning mathematics and programming domains specifically formatted for reinforcement learning. The collection includes 39,000 math problems from sources like AoPS and DeepMath-103K, alongside approximately 8,400 coding challenges.
ChartMimic evaluates visually-grounded code generation in large multimodal models using information-intensive visual charts. The dataset was created by the ChartMimic team and was last updated in June 2025.
Four meta-task categories including Screenshot Retrieval (SR), Composed Screenshot Retrieval (CSR), Screenshot QA (SQA), and Open-Vocabulary form the core of this Visualized Information Retrieval (Vis-IR) benchmark. The dataset utilizes digital screenshots to unify search and information extraction tasks across diverse application scenarios.
Miriad 5.8M contains 5.8 million medical question-answer pairs distilled from peer-reviewed biomedical literature using Large Language Models. Released in June 2025 by the Miriad research team, the dataset provides structured data for medical instruction tuning and retrieval-augmented generation. It serves as a large-scale resource for training models on verified scientific knowledge rather than general web content.
ALLaVA-4V is a multimodal dataset created by FreedomIntelligence using GPT-4V to generate detailed captions and complex reasoning question-answer pairs for images. The dataset incorporates data from sources like LAION and WizardLM, with its generation pipeline and prompts documented on the project page. It was last updated on June 8, 2025.
Coderonion curated this repository of public projects and datasets focusing on Large Language Models (LLM) and AI Generated Content (AIGC), last updated in August 2025. It aggregates links to specialized domains including Vision Language Action (VLA), AI for Science (AI4S), and specific models like DeepSeek and Qwen3.
MIRIAD contains 4.4 million medical question-answer pairs. The pairs were distilled from peer-reviewed biomedical literature using large language models, providing structured data for downstream tasks.
A collection of multimodal histological images from the Tumor Profiler Study. It includes whole-slide H&E images, multiplexed immunofluorescence images from Ultivue panels, alignment matrices, exclusion masks, and nuclear segmentation outputs for 10 cancer samples. The dataset was authored by CTPLab-DBE-UniBas and last updated on HuggingFace in June 2025.