---
license: cc-by-nc-4.0
task_categories:
- video-classification
tags:
- aesthetics
- video-quality
- multimedia
language:
- en
---

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).

The VADB dataset is designed to provide comprehensive resources for researchers and developers interested in video aesthetics analysis, computer vision, and multimedia content assessment.

The associated code and models can be found on GitHub: [https://github.com/BestiVictory/VADB](https://github.com/BestiVictory/VADB)

## Dataset Details
The data from our video dataset is stored in **VADB.zip**.

The VADB dataset includes:
*   **7,881 videos** covering diverse video styles and content categories.
*   **Detailed language comments** for each video.
*   **Aesthetic scores across 7-11 dimensions**, comprehensively covering the aesthetic attribute features of videos.
*   **Rich objective tags**, annotating video shooting techniques and other objective dimensions.

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`.

[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.

## Getting Started & Sample Usage

### 🚀 Install Dependencies
First, install the required dependencies (From CLIP):
```bash
conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
pip install ftfy regex tqdm
pip install opencv-python boto3 requests pandas
```

### 📦 Download the Dataset
Download the VADB dataset from Hugging Face:
```bash
git lfs install
git clone https://huggingface.co/datasets/BestiVictoryLab/VADB
```

### 🧠 VADB-Net Scoring Models
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.

The repository is structured into three main components for different scoring tasks:

#### 1. Overall Aesthetic Score
-   **Folder**: `1TotalScore`
-   **Model**: Predicts the overall aesthetic score of videos
-   **Usage**:
    ```bash
    cd 1TotalScore
    python 1TotalScore.py
    ```

#### 2. General Attribute Scores
-   **Folder**: `2GeneralAttribute`
-   **Model**: Evaluates general aesthetic attributes of videos
-   **Evaluation Dimensions**: Composition, Shot Size, Lighting, Visual Tone, Color, Depth of Field
-   **Usage**:
    ```bash
    cd 2GeneralAttribute
    python 2GeneralAttribute.py
    ```

#### 3. Human-Centric Attribute Scores
-   **Folder**: `3HumanAttribute`
-   **Model**: Focuses on evaluating specific aesthetic attributes of human subjects
-   **Evaluation Dimensions**: Expression, Movement, Costume, Makeup
-   **Usage**:
    ```bash
    cd 3HumanAttribute
    python 3HumanAttribute.py
    ```