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    4.5M+ UGC Videos Dataset for AI Training | 30K+ Hours

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    Deployed on AWS
    Sample repository from a large-scale User Generated Content (UGC) video corpus featuring real-world videos across diverse creators, environments, activities, and content categories for video understanding and AI applications.

    Overview

    User Generated Content (UGC) Video Dataset

    Overview

    This dataset is a large-scale collection of User Generated Content (UGC) videos designed to support video understanding, content analysis, computer vision, multimodal AI, and machine learning applications.

    The corpus contains authentic user-generated videos captured across diverse environments, activities, lifestyles, and real-world scenarios. The dataset reflects the variety and complexity commonly found in modern digital content, providing valuable visual data for developing robust AI systems capable of understanding real-world video content.

    The collection includes videos recorded by individuals across multiple settings, capturing natural interactions, activities, events, locations, and everyday experiences. This diversity enables AI systems to learn from realistic visual patterns and user-generated media formats.

    Key Use Cases

    • Video Understanding
    • Content Analysis
    • Activity Recognition
    • Scene Understanding
    • Human Behavior Analysis
    • Multimodal AI
    • Visual Search
    • Video Classification
    • Content Recommendation Systems
    • Social Media Analytics
    • Consumer Content Analysis
    • Video Intelligence Applications

    Dataset Features

    • Large-scale UGC video collection
    • Real-world user-generated videos
    • Diverse creators and recording environments
    • Multiple activity and lifestyle categories
    • Natural visual content and interactions
    • Broad environmental coverage
    • Suitable for training and evaluation workflows
    • Rich contextual video information

    Content Coverage

    The dataset includes user-generated videos spanning a wide range of categories and scenarios, including:

    • Lifestyle content
    • Daily activities
    • Entertainment videos
    • Social interactions
    • Indoor and outdoor environments
    • Personal experiences
    • Community activities
    • Consumer-generated media
    • Event-based recordings
    • Real-world visual content

    The diversity of environments, creators, and activities provides extensive visual variability for training robust AI systems.

    AI & Analytics Applications

    The corpus supports development of video intelligence systems capable of understanding visual content, contextual information, activity patterns, and user-generated media. Organizations can leverage the dataset for video analytics, content moderation, recommendation systems, visual search, multimodal learning, and next-generation video understanding applications.

    Data Collection

    The dataset consists of user-generated video content curated to represent diverse visual environments, activities, and content styles. The collection is organized to support research, evaluation, and large-scale AI development workflows.

    Licensing & Access

    This listing contains sample data intended for research, evaluation, and educational purposes. Enterprise licensing and access to the complete dataset are available upon request.

    InfoBay AI

    Email:  datareq@infobay.ai  Phone: +91 9236398619

    Highlights

    • Large-scale User Generated Content (UGC) video corpus featuring real-world videos captured across diverse environments, creators, and content categories.
    • Includes authentic consumer-generated video content covering daily activities, lifestyle, entertainment, social interactions, and real-world scenarios.
    • Supports video understanding, content analysis, activity recognition, multimodal learning, visual search, and AI model development workflows.

    Details

    Delivery method

    Deployed on AWS
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    Pricing

    4.5M+ UGC Videos Dataset for AI Training | 30K+ Hours

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    This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    AI Insights

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    Dimensions summary

    This listing uses a single pricing dimension, Product Access (Units), offered at no cost. Subscribing grants you access to the product. There are no tiers, instance sizes, or usage-based add-ons to compare. The dimension covers the video training dataset, which contains user-generated content for AI model training. Because the pricing is free with one access dimension, your cost does not scale with volume, hours, or usage. To scope a sample or discuss licensing details, you work directly with the vendor.

    Top-of-mind questions for buyers

    A unit grants you access to the product itself, not a per-hour, per-video, or per-gigabyte charge. Access covers the user-generated video training dataset. Because the dimension is free, the unit does not meter volume, hours, or downloads. One access grant covers the listed corpus.
    Yes. The vendor supports scoped sample requests so your team can evaluate format, coverage, and suitability. Each engagement begins with a quality baseline where you share your model, languages, and volume. The vendor then scopes a sample and licensing path with you.
    The dataset supports AI training, fine-tuning, evaluation, and domain-specific model development. The corpus covers user-generated video for visual grounding and cross-modal alignment. Refining steps include duplicate asset elimination, vertical format validation, codec audit, text recognition, synthetic media detection, and watermark analysis.
    infobay.ai
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    No Refunds

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    Usage information

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    Delivery details

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    Additional details

    Data sets (1)

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    You will receive access to the following data sets.

    Data set name
    Type
    Historical revisions
    Future revisions
    Sensitive information
    Data dictionaries
    Data samples
    UGC Video Dataset for Computer Vision & Multimodal AI
    All historical revisions
    All future revisions
    Not included
    Not included

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