RSK World - Video Classification Dataset - Project Files Browser | RSK World

index.html dataset_summary.json dataset_statistics.json train.csv audio_statistics.json test.csv

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    Project: Video Classification Dataset
    Author: Molla Samser
    Designer & Tester: Rima Khatun
    Website: https://rskworld.in
    Email: help@rskworld.in, support@rskworld.in
    Phone: +91 93305 39277
    Organization: RSK World
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    <div class="container">
        <header>
            <h1><i class="fas fa-video"></i> Video Classification Dataset</h1>
            <p>Labeled video clips across multiple categories for video understanding and classification tasks</p>
            <span class="difficulty-badge">Advanced</span>
        </header>
        
        <div class="content">
            <div class="section">
                <h2>Overview</h2>
                <p>This dataset includes labeled video clips across multiple categories for video classification tasks. Perfect for video understanding, video categorization, and video deep learning applications.</p>
            </div>
            
            <div class="section">
                <h2>Core Features</h2>
                <div class="features">
                    <div class="feature-card">
                        <h3>Multiple Categories</h3>
                        <p>Videos organized across multiple categories for comprehensive classification tasks.</p>
                    </div>
                    <div class="feature-card">
                        <h3>Labeled Clips</h3>
                        <p>All video clips are properly labeled and organized for easy access.</p>
                    </div>
                    <div class="feature-card">
                        <h3>Train/Test Sets</h3>
                        <p>Pre-organized training and test sets for immediate use in machine learning projects.</p>
                    </div>
                    <div class="feature-card">
                        <h3>Frame Extraction</h3>
                        <p>Built-in utilities for extracting frames from videos at specified intervals.</p>
                    </div>
                    <div class="feature-card">
                        <h3>Ready for Models</h3>
                        <p>Preprocessed and ready to use with popular video classification models.</p>
                    </div>
                    <div class="feature-card">
                        <h3>OpenCV & FFmpeg</h3>
                        <p>Comprehensive tools using OpenCV and FFmpeg for video processing.</p>
                    </div>
                </div>
            </div>
            
            <div class="section">
                <h2>🚀 Advanced Features</h2>
                <div class="advanced-feature">
                    <h4><span class="feature-icon">1</span>Video Data Augmentation</h4>
                    <p>Automatically augment video frames with flipping, rotation, brightness, and contrast adjustments to increase dataset diversity and improve model generalization.</p>
                </div>
                <div class="advanced-feature">
                    <h4><span class="feature-icon">2</span>Intelligent Key Frame Extraction</h4>
                    <p>Extract key frames using multiple methods: uniform sampling, scene change detection, or random selection for optimal feature representation.</p>
                </div>
                <div class="advanced-feature">
                    <h4><span class="feature-icon">3</span>Batch Processing</h4>
                    <p>Process multiple videos efficiently in batches with memory optimization and progress tracking for large-scale datasets.</p>
                </div>
                <div class="advanced-feature">
                    <h4><span class="feature-icon">4</span>Video Quality Analysis</h4>
                    <p>Automatically analyze video quality metrics including sharpness, brightness, resolution, and generate quality scores for dataset curation.</p>
                </div>
                <div class="advanced-feature">
                    <h4><span class="feature-icon">5</span>Video Summary Generation</h4>
                    <p>Create concise summary videos from long videos by extracting and combining key frames for quick preview and analysis.</p>
                </div>
                <div class="advanced-feature">
                    <h4><span class="feature-icon">6</span>Comprehensive Dataset Reports</h4>
                    <p>Generate detailed analytics reports with statistics, category distributions, and quality metrics for dataset management.</p>
                </div>
            </div>
            
            <div class="section">
                <h2>✨ Unique Features</h2>
                <div class="unique-feature">
                    <h4><span class="feature-icon">★</span>Duplicate Video Detection</h4>
                    <p>Automatically detect duplicate or similar videos using perceptual hashing to maintain dataset quality and avoid redundancy.</p>
                </div>
                <div class="unique-feature">
                    <h4><span class="feature-icon">★</span>Smart Video Splitting</h4>
                    <p>Intelligently split long videos into shorter segments with configurable duration and overlap for better training data preparation.</p>
                </div>
                <div class="unique-feature">
                    <h4><span class="feature-icon">★</span>Auto Thumbnail Generation</h4>
                    <p>Automatically generate high-quality thumbnails from videos using multiple methods: middle frame, first frame, or best quality frame selection.</p>
                </div>
                <div class="unique-feature">
                    <h4><span class="feature-icon">★</span>Video Montage Creation</h4>
                    <p>Create stunning montage videos from multiple sources arranged in customizable grid layouts for visualization and presentation.</p>
                </div>
                <div class="unique-feature">
                    <h4><span class="feature-icon">★</span>Dataset Balance Analysis</h4>
                    <p>Analyze and get recommendations for dataset balance across categories to ensure optimal training conditions.</p>
                </div>
                <div class="unique-feature">
                    <h4><span class="feature-icon">★</span>Auto-Categorization (ML-Ready)</h4>
                    <p>Framework for automatic video categorization using machine learning models with easy integration points.</p>
                </div>
            </div>
            
            <div class="section">
                <h2>Technologies</h2>
                <div class="tech-badges">
                    <span class="badge">MP4</span>
                    <span class="badge">MOV</span>
                    <span class="badge">OpenCV</span>
                    <span class="badge">FFmpeg</span>
                    <span class="badge">Video Processing</span>
                    <span class="badge">Python</span>
                    <span class="badge">Machine Learning</span>
                </div>
            </div>
            
            <div class="section">
                <h2>📖 How to Use - Step by Step Guide</h2>
                <div class="step-by-step">
                    <div class="step">
                        <h3>Step 1: Install Dependencies</h3>
                        <p>First, install all required Python packages:</p>
                        <div class="code-block">
                            <code>pip install -r requirements.txt</code>
                        </div>
                        <p>This installs OpenCV, NumPy, and other essential libraries for video processing.</p>
                    </div>
                    
                    <div class="step">
                        <h3>Step 2: Create Directory Structure</h3>
                        <p>Set up the folder structure for organizing your videos:</p>
                        <div class="code-block">
                            <code>python scripts/download_sample_data.py --create-structure</code>
                        </div>
                        <p>This creates the <span class="code-inline">raw_videos/</span> directory with category folders (action, comedy, drama, sports, etc.)</p>
                    </div>
                    
                    <div class="step">
                        <h3>Step 3: Add Your Videos</h3>
                        <p>Place your video files in the appropriate category folders:</p>
                        <div class="code-block">
                            <code>
raw_videos/<br>
├── action/<br>
│   └── your_video.mp4<br>
├── comedy/<br>
│   └── your_video.mp4<br>
└── ...
                            </code>
                        </div>
                        <p><strong>Tip:</strong> You can also use the interactive mode: <span class="code-inline">python scripts/add_videos.py --interactive</span></p>
                    </div>
                    
                    <div class="step">
                        <h3>Step 4: Organize Dataset</h3>
                        <p>Automatically split videos into train (70%), test (20%), and validation (10%) sets:</p>
                        <div class="code-block">
                            <code>python scripts/organize_dataset.py --input raw_videos --output data</code>
                        </div>
                        <p>This organizes your videos into the <span class="code-inline">data/train/</span>, <span class="code-inline">data/test/</span>, and <span class="code-inline">data/validation/</span> directories.</p>
                    </div>
                    
                    <div class="step">
                        <h3>Step 5: Process Videos (Optional)</h3>
                        <p>Resize and normalize videos for consistent format:</p>
                        <div class="code-block">
                            <code>python scripts/process_videos.py --input raw_videos --output data/train</code>
                        </div>
                        <p>This ensures all videos have uniform resolution (224x224) and format.</p>
                    </div>
                    
                    <div class="step">
                        <h3>Step 6: Extract Frames (Optional)</h3>
                        <p>Extract frames from videos for frame-based models:</p>
                        <div class="code-block">
                            <code>python scripts/extract_frames.py --input data/train --output frames/train</code>
                        </div>
                        <p>Extracts frames at 1 frame per second (configurable in <span class="code-inline">config.yaml</span>).</p>
                    </div>
                    
                    <div class="step">
                        <h3>Step 7: Verify Dataset</h3>
                        <p>Check your dataset statistics and metadata:</p>
                        <div class="code-block">
                            <code>python scripts/create_sample_metadata.py --summary</code>
                        </div>
                        <p>This generates a comprehensive report of your dataset including video counts per category.</p>
                    </div>
                    
                    <div class="step">
                        <h3>Step 8: Use the Dataset</h3>
                        <p>Start using your dataset in Python:</p>
                        <div class="code-block">
                            <code>
from utils.dataset_utils import get_videos_by_category<br><br>
# Get videos by category<br>
videos = get_videos_by_category('data/train')<br>
print(videos)
                            </code>
                        </div>
                        <p>See <span class="code-inline">examples/video_loader_example.py</span> for complete usage examples.</p>
                    </div>
                </div>
            </div>
            
            <div class="section">
                <h2>Quick Start Commands</h2>
                <p>For experienced users, here's a quick reference:</p>
                <div class="code-block">
                    <code>
# Install dependencies<br>
pip install -r requirements.txt<br><br>
# Create structure<br>
python scripts/download_sample_data.py --create-structure<br><br>
# Organize dataset<br>
python scripts/organize_dataset.py --input raw_videos --output data<br><br>
# Extract frames<br>
python scripts/extract_frames.py --input data/train --output frames/train
                    </code>
                </div>
            </div>
            
            <div class="section">
                <h2>Documentation</h2>
                <p>For detailed usage instructions, examples, and API documentation, please refer to:</p>
                <ul style="margin-left: 30px; margin-top: 10px;">
                    <li><a href="README.md">README.md</a> - Project overview and setup</li>
                    <li><a href="USAGE.md">USAGE.md</a> - Detailed usage guide</li>
                    <li><a href="examples/">Examples</a> - Code examples and tutorials</li>
                </ul>
            </div>
            
            <div class="section">
                <h2>Get Started</h2>
                <a href="README.md" class="btn">View README</a>
                <a href="USAGE.md" class="btn btn-secondary">Usage Guide</a>
                <a href="https://rskworld.in" class="btn btn-secondary">Visit RSK World</a>
            </div>
        </div>
        
        <footer>
            <h3>RSK World</h3>
            <p>Free Programming Resources & Source Code</p>
            <div class="contact-info">
                <p><strong>Contact Information:</strong></p>
                <p>
                    <a href="https://rskworld.in">Website: https://rskworld.in</a> | 
                    <a href="mailto:help@rskworld.in">Email: help@rskworld.in</a> | 
                    <a href="tel:+919330539277">Phone: +91 93305 39277</a>
                </p>
                <p style="margin-top: 15px;">
                    <strong>Founder:</strong> Molla Samser | 
                    <strong>Designer & Tester:</strong> Rima Khatun
                </p>
                <p style="margin-top: 10px; font-size: 0.9em; opacity: 0.8;">
                    Content used for educational purposes only. View Disclaimer at <a href="https://rskworld.in">rskworld.in</a>
                </p>
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