FineVideo: behind the scenes
mfarre, andito, lewtun, lvwerra, pcuenq, thomwolf
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/finevideo/logo.png" alt="FineVideo logo" style="width: 50%;"><br>
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Open video datasets are scarce and therefore slowing down the development of open-source video AI. For this reason we built [FineVideo](https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer), a dataset with 43k videos that span 3.4k hours and are annotated with rich descriptions, narrative details, scene splits, and QA pairs. 

FineVideo contains a highly diverse collection of videos and metadata which makes it a good ingredient to train models to understand video content, train diffusion models to generate videos from a text description or train computer vision models using its structured data as input.

Wait, you haven’t seen FineVideo yet? take a look at it through the [dataset explorer page](https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer).

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    <a href="https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer">
        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/finevideo/finevideo.gif" alt="FineVideo Explorer" style="width: 60%;">
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## Table of Contents
- [Table of Contents](#table-of-contents)
- [About this blog post](#about-this-blog-post)
- [Building the Raw dataset](#building-the-raw-dataset)
  - [Filtering YouTube-Commons](#filtering-youtube-commons)
  - [Downloading the videos](#downloading-the-videos)
- [Keeping dynamic content](#keeping-dynamic-content)
  - [Word density filtering](#word-density-filtering)
  - [Visual dynamism filtering](#visual-dynamism-filtering)
- [Video Categorization](#video-categorization)
  - [Custom built Taxonomy](#custom-built-taxonomy)
  - [Content annotation](#content-annotation)
  - [Feedback loop taxonomy - content annotation](#feedback-loop-taxonomy