Enhance dataset card: Add paper/code links, task categories, detailed description, and sample usage
#2
by
nielsr
HF Staff
- opened
Hello team,
This PR aims to significantly improve the dataset card for VADB: A Large-Scale Video Aesthetic Database.
Key enhancements include:
- Metadata:
- Added
task_categories: ['video-classification']to accurately reflect the dataset's purpose of video aesthetic assessment. - Included
language: ['en']for better indexing. - Added relevant
tags: ['aesthetics', 'video-quality', 'multimedia']to improve discoverability.
- Added
- Introduction: A new introductory section linking to the research paper (VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations) and the GitHub repository (https://github.com/BestiVictory/VADB).
- Dataset Details: Expanded the dataset description to include more specifics about its contents, drawing from the GitHub README, such as the number of videos and the types of annotations (scores, comments, tags).
- Sample Usage: A comprehensive "Getting Started & Sample Usage" section has been added, directly incorporating code snippets from the GitHub README for installing dependencies, downloading the dataset, and running the various VADB-Net scoring models (Overall Aesthetic Score, General Attribute Scores, and Human-Centric Attribute Scores). This will make it much easier for users to get started.
These updates aim to provide a more informative and user-friendly dataset card on the Hugging Face Hub.