RSK World - Video Classification Dataset | Video ML | Video AI | Video Deep Learning | RSK World

Video Classification Dataset - RSK World

Video Classification 8 Categories Machine Learning Labeled Videos Python OpenCV Ready

This project features a comprehensive Video Classification dataset designed for professional video categorization, video understanding, and video deep learning applications. The dataset includes labeled video clips across 8 categories (action, comedy, drama, sports, documentary, news, music, education), organized into training/test/validation splits, frame extraction utilities, video processing scripts, and Python examples. Includes powerful Python scripts: extract_frames.py for frame extraction, process_videos.py for video processing, organize_dataset.py for dataset organization, add_videos.py for adding videos, and classification_example.py for model training. The package includes interactive demo website, comprehensive README.md, DATASET_GUIDE.md, QUICK_START.md, and MIT License. Perfect for data scientists, researchers, students, and developers working on video classification, video categorization, video understanding, and video deep learning projects.

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Dataset Overview

Complete video classification dataset with labeled video clips across 8 categories for video AI and machine learning.

Labeled video clips across 8 categories
8 categories: action, comedy, drama, sports, documentary, news, music, education
5-10 seconds per video clip
MP4 and MOV formats
224x224 or higher resolution
24-30 FPS frame rate
Pre-labeled with category tags
Balanced class distribution
Organized video clips
Training/validation/test split included
Frame extraction utilities available
Perfect for video classification & ML training

Dataset Structure & Files

Well-organized folder structure with video files organized by category, training/test/validation splits, and metadata.

data/ - Video files organized by category (MP4/MOV)
data/train/ - Training videos by category (action, comedy, drama, sports, documentary, news, music, education)
data/test/ - Test videos by category
data/validation/ - Validation videos by category
raw_videos/ - Place your videos here before organizing
frames/ - Extracted frames directory
scripts/ - Python scripts (extract_frames.py, process_videos.py, organize_dataset.py, add_videos.py)
utils/ - Utility modules (video_utils.py, dataset_utils.py)
examples/ - Example code (classification_example.py, video_loader_example.py)
Consistent naming convention
Easy to load with PyTorch DataLoader
OpenCV/FFmpeg ready format

Machine Learning Training

Complete training pipeline with support for video classification models, frame extraction, and deep learning frameworks.

Video classification models
Frame-based models support
Temporal feature extraction
OpenCV video processing
PyTorch support
TensorFlow/Keras compatibility
Video frame extraction utilities
Batch processing support
Dataset organization tools
Video processing utilities
Performance metrics
Model training examples
Video loader utilities

Multiple File Formats

Dataset available in multiple formats for maximum compatibility with different video processing tools and frameworks.

MP4 format (H.264 codec)
MOV format support
AVI and MKV support
OpenCV compatible
FFmpeg compatible
Frame extraction (JPG)
224x224 or higher resolution
24-30 FPS frame rate
Standard video formats
Compatible with all video libraries
PyTorch DataLoader ready

Analysis & Visualization

Comprehensive analysis tools with visualization capabilities and interactive video explorer.

Interactive Video Explorer
Category distribution charts
Video frame visualization
Dataset statistics
Video information extraction
Category-based filtering
Performance benchmarking
Video metadata tools
Frame extraction visualization
Dataset organization tools
Interactive Demo Website

Compatible Frameworks

Works with all major video AI and machine learning frameworks out of the box.

PyTorch deep learning
TensorFlow/Keras
OpenCV video processing
FFmpeg video encoding/decoding
NumPy numerical computing
pandas data manipulation
matplotlib visualization
Jupyter Notebook support
Video classification models
Frame-based models
Neural Network models

What You Get

Complete package with all files needed for professional video classification projects.

Labeled video clips across 8 categories
Python utility scripts
extract_frames.py - Frame extraction
process_videos.py - Video processing
organize_dataset.py - Dataset organization
add_videos.py - Add videos to dataset
classification_example.py - Model training example
video_loader_example.py - Video loading example
Interactive demo website
Video processing utilities
Complete documentation (README, DATASET_GUIDE, QUICK_START)
MIT License

Interactive Demo Website

Beautiful demo website with video explorer, live video player, analytics dashboard, and comprehensive guide.

Modern animated design
Interactive Video Explorer
Live Video Player
Analytics Dashboard
Filter by video category
Video frame visualization
Category distribution charts
Dataset statistics display
Step-by-step usage guide
Dark theme with gradients
Fully responsive layout

Python Scripts Included

Professional Python scripts for video processing, frame extraction, and dataset organization.

extract_frames.py - Extract frames from videos
process_videos.py - Process and normalize videos
organize_dataset.py - Organize dataset into train/test/validation
add_videos.py - Add videos to dataset (interactive & batch modes)
download_sample_data.py - Download helper and structure creation
create_sample_metadata.py - Generate dataset metadata
classification_example.py - Video classification model example
video_loader_example.py - Video loading example
Batch processing support
Video processing utilities
Dataset organization tools
Complete code examples

Dataset Features

Comprehensive video classification dataset with 8 categories and various video characteristics.

8 categories - action, comedy, drama, sports, documentary, news, music, education
Diverse video styles - Robust training
Real video clips - Generalizable models
Balanced class distribution
5-10 second clips - Consistent format
High-quality video recordings
Accurate category labels
Training/validation/test split included
Frame extraction utilities
Easy to extend dataset
Organized folder structure