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README.md

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+# Technical Documentation for the Text-to-Video Dataset “VidData”
+
+## 1. Introduction
+This dataset contains 1006 annotated videos of everyday scenes, used for training and evaluating AI models in video generation and recognition. It is structured to meet the needs of Text-to-Video models and motion analysis.
+
+## 2. Dataset Specifications
+
+### 2.1. Generation Criteria
+- **Maximum video duration**: 10 seconds maximum
+- **Video themes**:
+  - Walking
+  - Exercising
+  - Writing
+  - Shopping
+  - Sleeping
+  - Meditating
+  - Working
+  - Studying
+  - Driving
+  - Washing
+  - Gardening
+  - Calling
+  - Listening
+  - Organizing
+  - Planning
+  - Relaxing
+  - Teaching
+- **Video size**: 512×512 pixels
+
+### 2.2. Dataset Organization
+The dataset is organized under a main folder called VidData, which includes three essential parts:
+    data/train/: Contains a VidData.csv file, likely storing metadata or structured details about the videos.
+    video/: Holds the video files (e.g., ---_iRTHryQ_13_0to241.mp4), named in a specific format, possibly indicating segments or unique identifiers.
+    readme.md: Provides documentation about the dataset's structure and usage.
+This structure clearly separates raw video data, metadata (CSV), and documentation, ensuring efficient organization for analysis and processing.
+    data/train/: Contains CSV files with video-related metadata.
+    video/: Stores the actual video files.
+## 3. Data Structure
+The dataset is stored as a CSV file and includes the following columns:
+
+| Column                  | Type    | Description                          |
+|-------------------------|---------|--------------------------------------|
+| video                   | string  | Video file name                      |
+| caption                 | string  | Textual description of the video     |
+| temporal consistency score | float64 | Temporal consistency score           |
+| fps                     | float64 | Frame per second                     |
+| frame                   | int64   | Number of frames in the video        |
+| seconds                 | float64 | Video duration in seconds            |
+| motion score            | float64 | Motion score                         |
+| camera motion           | string  | Type of camera motion (e.g., pan_left) |
+
+## 4. Libraries Used
+
+### 4.1. Library Examples
+Here are some example libraries that can be used when analyzing this data:
+- **OpenCV**: Video manipulation and processing (reading, writing, frame extraction, contour detection, filtering, etc.).
+- **Scikit-Image**: Calculating the Structural Similarity Index (SSIM) for image quality evaluation and various image transformations (segmentation, filtering, etc.).
+- **NumPy**: Efficient manipulation of matrices and arrays, essential for calculations on images and videos.
+- **Pandas**: Managing and structuring metadata associated with videos (e.g., file names, timestamps, annotations).
+- **Matplotlib/Seaborn**: Visualizing analysis results as graphs.
+
+### 4.2. Installing Dependencies
+Follow the instructions below to install the required libraries:
+1. Create a `requirements.txt` file and add the following:
+   opencv-python==4.8.1.78 # Video manipulation and processing
+   scikit-image==0.22.0 # SSIM calculation and image transformations
+   numpy==1.26.2 # Efficient manipulation of matrices and arrays
+   pandas==2.1.4 # Managing and structuring metadata
+   matplotlib==3.8.2 # Visualizing analysis results
+   seaborn==0.12.2 # Advanced visualization with enhanced graphics
+2. Run the command: `pip install -r requirements.txt`
+
+**Note**: Only include the libraries you need in `requirements.txt`.
+
+## 5. Using the Dataset
+
+### 5.1. Primary Applications
+#### 5.1.1. Text-to-Video Generation
+- Train models to generate video based on textual input.
+- Benchmark performance by comparing generated video against dataset entities.
+
+#### 5.1.2. Video Description Models
+- Evaluate models designed to generate textual descriptions from videos.
+
+#### 5.1.3. Temporal Consistency Analysis
+- Test model for maintaining smoothness and coherence in video generation.
+
+### 5.2. Example Workflow
+- Load the dataset using Python:
+## 4. Libraries Used
+```python
+import pandas as pd
+
+dataset = pd.read_csv('VidData.csv')
+print(dataset.head())
+
+##Access video metadata:
+video = dataset.iloc[0]  # First entry
+print(f"Video Name: {video['video_name']}")
+print(f"Caption: {video['Caption']}")
+print(f"Duration: {video['duration_seconds']} seconds")
+
+##Filter video based on motion:
+high_motion_videos = dataset[dataset['motion_score'] > 1.0]
+print(high_motion_videos)
+```
+
+## 6. File Format
+The dataset is delivered in CSV format, with each column representing a video and its metadata.
+
+## 7. Sample Entry:
+
+| video\_name | caption                                            | temporal\_consistency\_score | fps | frames | duration\_seconds | motion\_score | camera\_motion |
+| ----------- | ------------------------------------------------------ | ---------------------------- | --- | ------ | ----------------- | ------------- | -------------- |
+| E\_1.mp4    | The video shows a soccer player kicking a soccer ball. | 0.948826                     | 30  | 195    | 6.5               | 0.826522      | 1.105807       |
+
+
+## 8. Contact
+For inquiries, please contact:
+
+- **Email**: [info@databoost.us](mailto\:info@databoost.us)
+- **Website**: [databoost.us](https://databoost.us)