Enhance dataset card: Add paper/code links, task categories, detailed description, and sample usage

README.md

@@ -1,10 +1,81 @@
 ---
 license: cc-by-nc-4.0
+task_categories:
+- video-classification
+tags:
+- aesthetics
+- video-quality
+- multimedia
+language:
+- en
 ---
 
-The data from our video dataset is stored in **VADB.zip**
+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).
 
-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*.
+The VADB dataset is designed to provide comprehensive resources for researchers and developers interested in video aesthetics analysis, computer vision, and multimedia content assessment.
 
-[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.
+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
+    ```