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Image-text pairs, instruction tuning, visual QA, cross-modal data, foundation model training data
1,956 datasets
Instruction tuning data for large language models, sourced from Kaggle. The dataset's specific size, format, and content details are not provided in the metadata. Its primary purpose is to support the supervised fine-tuning process for aligning model outputs with human instructions.
Trained-vlm-config is a dataset hosted on Kaggle. The title suggests it contains configuration files or parameters for a trained Vision-Language Model. The dataset's specific contents, scale, and authorship are not detailed in the provided metadata.
A multimodal dataset focused on emotion recognition, published on Kaggle. The dataset likely contains data from multiple modalities such as text, audio, or images, aligned for emotion analysis. Specific details on volume, collection method, and authorship are not provided in the available metadata.
A dataset for emotion recognition, likely containing multiple data modalities such as text, audio, or images. It is hosted on Kaggle and may be associated with a pre-trained model. The specific volume, source, and creation date are not detailed in the available metadata.
my_vqa_dataset12 is a dataset about Visual Question Answering (VQA). It is published on Kaggle. The dataset's specific content, size, and authorship are unknown.
Universe_multimodal_cleaned is a dataset published on Kaggle. The title suggests it contains cleaned, multimodal data, likely combining multiple data types such as text, images, or audio. Specific details on its size, origin, and creation date are not provided in the available metadata.
ORCA RLHF is a dataset hosted on Kaggle, likely related to training large language models using reinforcement learning from human feedback. The dataset's specific content, size, and structure are not detailed in the provided metadata. Its origin and creation methodology are also unspecified.
A Kaggle-hosted dataset focused on emotions. The dataset likely contains multimodal data, such as text, audio, or images, related to emotional states. Its specific content, size, and creation details are not provided.
BLIP Model is a dataset or model artifact related to the BLIP (Bootstrapping Language-Image Pre-training) framework, hosted on Kaggle. The specific content, such as pre-training data, model weights, or fine-tuning examples, is not detailed in the available metadata. Its origin and creation date are unknown.
Multimodal-dataset-lessismore is a dataset hosted on Kaggle. Its title suggests it contains multiple data types, such as images, text, or audio, combined for machine learning tasks. The dataset's specific content, scale, and origin are not detailed in the available metadata.
Agri VLM Dataset is a multimodal dataset likely containing agricultural imagery paired with textual descriptions, sourced from Kaggle. The dataset's specific size, content details, and creation date are not provided in the available metadata. Its purpose appears to be for training and evaluating vision-language models on agricultural concepts.
Kaggle hosts this collection of scripts and resources for a Visual Question Answering (VQA) model. The dataset's specific content, size, and authorship are not detailed in the provided metadata. It is categorized on the platform as a 'Pre Trained Model' resource.
Instruction tuning data for fine-tuning large language models on Arabic language tasks. The dataset is hosted on Kaggle, but its specific size, creation date, and authorship are not provided in the available metadata. Columns and sample data are unknown, limiting immediate assessment of its content and structure.
MedVLM-Src is a dataset published on Kaggle. The title suggests it contains source data for training or evaluating medical vision-language models. The dataset's specific content, scale, and origin require verification after download.
A dataset titled 'Multimodal_csv' is available on Kaggle. The dataset's specific content, size, and origin are not detailed in the provided metadata. Further verification is required to confirm the exact nature and composition of the multimodal elements.
A dataset titled 'ttv_sp_llava_final' published on Kaggle. The title suggests it is a final version of data related to the LLaVA (Large Language-and-Vision Assistant) model, likely containing multimodal content for vision-language tasks. Metadata is minimal; the specific content, size, and origin require verification after download.
GVLM-Data is a dataset hosted on Kaggle. The dataset's title suggests it is likely related to General Vision-Language Models. Its specific content, size, and origin are not detailed in the available metadata.
MicroVQA++ is a three-stage, large-scale and high-quality microscopy visual question answering corpus derived from biomedical imaging sources. The dataset, created by author ieellee and last updated on 2025-12-14, is designed to address the scarcity of training data for scientific reasoning in microscopy with multimodal large language models.
1600 voice samples are paired with 304 voice features and 4 demographic variables for diabetes prediction. The dataset is hosted on Kaggle and includes platform tags suggesting a focus on deep learning applications and synthetic data. Its multimodal nature combines audio signal processing with demographic information.
PixelProse contains 16,896,214 image-caption pairs featuring dense synthetic descriptions generated by Gemini 1.0 Pro Vision. Released in 2024 by researchers at the University of Maryland (tomg-group-umd), the collection provides detailed textual representations for images sourced from CommonPool and CC12M.