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Home > All Category Datasets > LLM Datasets > 138K Videos – Entity Interaction Dataset for Video Understanding

138K Videos – Entity Interaction Dataset for Video Understanding

video understanding dataset
video interaction dataset
entity relationship dataset
video relation dataset
entity interaction annotation dataset

This dataset contains 138,172 video clips covering diverse real-world scenes and entity-interaction relationships. Each video clip includes keyframes that represent important entity interactions and their corresponding relationships. The annotation files provide entity types, bounding boxes, and interaction labels for relationship triples identified in the keyframes. Each relationship can be represented as a structured triple: (entity1, entity2, interaction). This dataset can be used for video understanding task.

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SpecificationsSpecifications
Data content
138,172 video clips, each containing: ①keyframes with representative entity-interaction relationships; ②annotation documents labeling types and bounding boxes of keyframe entity-interaction relationship triples, like (entity1, entity2, interaction).
Data distribution
①videos cover indoor/outdoor scenes and different time periods; ②keyframes involving diverse entity-interaction relationships between humans, animals, and interactive objects; ③interaction relationships including static positional relationships and dynamic behavioral relationships.
Data quality
①video resolution is no less than 1080p, frame rate is no less than 20fps, duration is no less than 4.6s; ②the relationship types and bounding boxes of triples are accurate.
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What can Nexdata’s LLM datasets be used for?

Nexdata’s LLM datasets can support a wide range of large language model development tasks, including pre-training, supervised fine-tuning, instruction tuning, preference optimization, evaluation, and domain-specific model development. Depending on the dataset, data may include text, instruction-response pairs, conversations, question-answer pairs, reasoning data, and other structured or annotated content.

Can Nexdata customize LLM datasets based on our specific model and requirements?

Yes. If our off-the-shelf LLM datasets do not fully match your requirements, Nexdata provides flexible custom data collection, generation, annotation, curation, and quality control services. We can customize datasets based on your target languages, domains, use cases, data formats, task types, volume, and quality requirements to support specific LLM training and evaluation projects.

Can Nexdata provide large-scale and high-quality data for LLM development?

Yes. Nexdata can support large-scale LLM data projects across multiple languages, domains, and data types. Our data services include multi-stage quality control, data cleaning, annotation, validation, and curation to help ensure consistency and usability. For projects requiring data beyond our existing datasets, our customized data services can be scaled according to the required volume, specifications, and delivery schedule.

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