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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- video-classification |
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tags: |
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- aesthetics |
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- video-quality |
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- multimedia |
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language: |
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- en |
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--- |
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This repository introduces **VADB**, a large-scale video aesthetics database, presented in the paper [VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations](https://huggingface.co/papers/2510.25238). |
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The VADB dataset is designed to provide comprehensive resources for researchers and developers interested in video aesthetics analysis, computer vision, and multimedia content assessment. |
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The associated code and models can be found on GitHub: [https://github.com/BestiVictory/VADB](https://github.com/BestiVictory/VADB) |
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## Dataset Details |
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The data from our video dataset is stored in **VADB.zip**. |
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The VADB dataset includes: |
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* **7,881 videos** covering diverse video styles and content categories. |
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* **Detailed language comments** for each video. |
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* **Aesthetic scores across 7-11 dimensions**, comprehensively covering the aesthetic attribute features of videos. |
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* **Rich objective tags**, annotating video shooting techniques and other objective dimensions. |
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Our `train.csv` contains all annotated data related to aesthetic scores. For text annotations such as comments and tags mentioned in the paper, please refer to `merged_comment_tag.json`. |
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[2025/10/22 UPDATE] The annotation files are fully open-sourced, but due to copyright issues with some videos, 2,609 video clips have been retained and not open-sourced, while the remaining 7,881 video clips have been fully released. |
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## Getting Started & Sample Usage |
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### 🚀 Install Dependencies |
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First, install the required dependencies (From CLIP): |
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```bash |
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conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0 |
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pip install ftfy regex tqdm |
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pip install opencv-python boto3 requests pandas |
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``` |
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### 📦 Download the Dataset |
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Download the VADB dataset from Hugging Face: |
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```bash |
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git lfs install |
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git clone https://huggingface.co/datasets/BestiVictoryLab/VADB |
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``` |
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### 🧠 VADB-Net Scoring Models |
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The VADB project also provides **VADB-Net**, a novel video aesthetics scoring framework. You will need to load a pre-trained video encoder model (details and link provided in the [GitHub repository's Video Encoder section](https://github.com/BestiVictory/VADB#video-encoder)). This encoder extracts aesthetic feature vectors from videos and serves as the foundational component for all scoring models. |
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The repository is structured into three main components for different scoring tasks: |
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#### 1. Overall Aesthetic Score |
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- **Folder**: `1TotalScore` |
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- **Model**: Predicts the overall aesthetic score of videos |
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- **Usage**: |
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```bash |
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cd 1TotalScore |
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python 1TotalScore.py |
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``` |
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#### 2. General Attribute Scores |
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- **Folder**: `2GeneralAttribute` |
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- **Model**: Evaluates general aesthetic attributes of videos |
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- **Evaluation Dimensions**: Composition, Shot Size, Lighting, Visual Tone, Color, Depth of Field |
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- **Usage**: |
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```bash |
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cd 2GeneralAttribute |
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python 2GeneralAttribute.py |
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``` |
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#### 3. Human-Centric Attribute Scores |
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- **Folder**: `3HumanAttribute` |
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- **Model**: Focuses on evaluating specific aesthetic attributes of human subjects |
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- **Evaluation Dimensions**: Expression, Movement, Costume, Makeup |
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- **Usage**: |
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```bash |
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cd 3HumanAttribute |
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python 3HumanAttribute.py |
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``` |
