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Image-text pairs, instruction tuning, visual QA, cross-modal data, foundation model training data
1,947 datasets
MultimodalLLM-Moroccan-SLT is a dataset hosted on Kaggle. The title suggests it likely contains data for Moroccan Sign Language Translation, potentially involving multiple modalities such as video or images paired with text. The dataset's specific content, size, and authorship are unknown and require verification after download.
DataConcept-128M contains 128 million web-crawled image-text pairs annotated with fine-grained concept composition details. It is derived from DataComp-CLIP and designed to enable Concept-Aware Batch Sampling for multimodal pretraining.
A dataset titled 'Vqagent Pairwise Preference' was published on the Hugging Face platform by the user 'qgfvadfuvads'. The title suggests it contains pairwise preference comparisons, likely used for training or evaluating reinforcement learning agents. The dataset was last updated on April 12, 2026.
VisRes Bench contains 10,000 to 100,000 image-text pairs designed to evaluate the visual reasoning of Vision-Language Models (VLMs) in naturalistic settings. Developed by researchers at TII (tiiuae) and updated in March 2026, it isolates visual logic by removing contextual language supervision.
Quran-MD is a multimodal dataset of the Qur'an integrating textual, linguistic, and audio dimensions at the verse and word levels. The dataset was created by 'yourmumisacow' and is associated with a paper accepted at the 5th Muslims in ML Workshop co-located with NeurIPS 2025. The specific ayah-level subset was last updated on February 21, 2026.
A dataset likely associated with the LLaVA (Large Language-and-Vision Assistant) project for training multimodal AI models. It was published on Kaggle, but its specific contents, size, and creation details are not provided in the metadata. The dataset name suggests it is designed for instruction-following tasks involving both visual and textual data.
A dataset for Vision-Language Models (VLMs) focused on the Blind 3D (B3D) task. The dataset was created by VietMedTeam, with main authors Nguyen Kim Hai Bui and An Ngo Xuan, and was last updated on April 1, 2026.
A dataset titled 'checkpoint_vqa' hosted on Kaggle. The dataset's title suggests it is related to Visual Question Answering, a multimodal AI task combining images and text. The author's description indicates they are a person with autism requesting patience, but no further metadata about the dataset's content, size, or origin is provided.
OmniBrainBench is a multimodal benchmark dataset for brain imaging analysis across multi-stage clinical tasks. The dataset was created by FrankPN and is associated with a CVPR 2026 paper. Specific details on row count, column count, and data size are not provided in the input.
VLM4D is a benchmark of approximately 1,000 real-world and synthetic videos designed to evaluate spatiotemporal reasoning in Vision Language Models. Developed by Shijie Zhou and researchers at UCLA in 2025, the dataset provides curated video-text pairs to test model awareness of motion and time.
Mdpbench Vlmevalkit is a dataset published on HuggingFace by Delores-Lin. It was last updated on April 13, 2026. The dataset's title suggests it is a benchmark for evaluating vision-language models.
FineVision-vlmbench-mini is a 128-row subset of the FineVision dataset, designed for benchmarking Vision-Language Model inference performance. The dataset was created by the author vlm-run and last updated on 2026-02-08. It is intended to provide a realistic and diverse workload for measuring metrics like tokens per second and VRAM usage.
Human Behavior Atlas (HBA) is a multimodal benchmark aggregating between 100,000 and 1,000,000 records for psychological and social behavior analysis, published by keentomato. It standardizes diverse behavioral datasets into a single framework for training foundation models on signals like emotion, intent, and sarcasm. The collection spans text, audio, image, and video modalities to support social intelligence tasks.
VLMData is a dataset published on Kaggle, likely containing data for training or evaluating Vision-Language Models. The dataset's specific content, size, and origin are not detailed in the available metadata. Its structure and intended use must be verified after download.
Joy Captioning 20250408A contains between 100,000 and 1,000,000 image-text pairs used for the initial training of the JoyCaption Beta One vision-language model. Created by fancyfeast and updated in early 2026, the collection focuses on detailed image descriptions and visual question-answering tasks. The data includes a mix of human-written and machine-generated text, explicitly labeled for provenance.
1,000 real-world iOS mobile UI screens collected from diverse application categories on the Apple App Store. Each screen is paired with human-validated structured JSON ground truth annotations, enabling research in UI understanding and layout analysis. The dataset was created by atharparvezce and last updated on Hugging Face in February 2026.
Stephengzk published this dataset on Hugging Face on April 4, 2026. The title suggests it contains YouTube videos, likely short-form content, associated with a visual question-answering (FVQA) task. The dataset's specific content, scale, and structure require verification after download due to minimal provided metadata.
RobotInter-VQA is a Visual Question Answering dataset for robotic manipulation, developed as part of the RoboInter project. It covers generation and understanding of Intermediate Representations for task planning and is built on annotations from RoboInter-Data, with raw robot datasets sourced from DROID and RH20T. The dataset was created by InternRobotics and was last updated on February 14, 2026.
STVQA-7K is a high-quality spatial visual question answering dataset comprising 7,587 samples. It was created by hunarbatra and last updated on 2026-01-29. The dataset is fully grounded in human-annotated scene graphs from Visual Genome and is designed for training and evaluating spatial reasoning capabilities in multimodal large language models.
VLM Dynamic Model Information is a dataset related to vision-language models and their evaluation, published on the Hugging Face platform. The dataset was created by 'open-cn-llm-leaderboard' and was last updated on April 3, 2026. The specific content, scale, and structure require verification after download as metadata is minimal.