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Image Classification Dataset - RSK World

Image Data CNN Models Deep Learning 15 Categories Transfer Learning PNG/JPG

This project features a comprehensive Image Classification dataset designed for professional computer vision, deep learning, and machine learning applications. The dataset includes 10,000+ labeled images across 15 diverse categories: Animals, Vehicles, Nature, Food, Buildings, Fashion, Aircraft, Sports, Instruments, Electronics, Furniture, Plants, People, Art, and Tools. Pre-split into Train (70%), Validation (15%), and Test (15%) sets with 224x224 RGB images. The package includes 6 advanced Python scripts: data_loader.py, train_model.py (CNN with TensorFlow/Keras), augmentation.py, evaluate.py, advanced_utils.py (Grad-CAM, dataset analysis), and predict.py (ensemble models). Features benchmark results for ResNet50 (94% accuracy), VGG16 (92%), and EfficientNetB0 (95%). Includes interactive demo website with live AI prediction, Chart.js analytics dashboard, image gallery with category filters, dark/light theme toggle. Complete with metadata.json, statistics.csv, comprehensive README.md, and Jupyter notebook tutorial. Perfect for data scientists, researchers, students, and developers working on image recognition, object detection, transfer learning, and computer vision projects.

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Dataset Overview

Complete image classification dataset with 10,000+ labeled images across 15 diverse categories for CNN training and computer vision.

10,000+ labeled images
15 diverse categories
Animals, Vehicles, Nature, Food, Buildings
Fashion, Aircraft, Sports, Instruments
Electronics, Furniture, Plants, People, Art, Tools
Pre-split: Train (70%), Val (15%), Test (15%)
224x224 RGB images
PNG/JPG formats supported
Balanced class distribution
Professional labeling quality
Perfect for CNN & Transfer Learning

Dataset Structure & Files

Well-organized folder structure with training, validation, and test splits plus comprehensive metadata.

dataset/train/ - 7,000 images (70%)
dataset/validation/ - 1,500 images (15%)
dataset/test/ - 1,500 images (15%)
data/metadata.json - Dataset information
data/statistics.csv - Category statistics
6 Python scripts included
Jupyter notebook tutorial
Organized by category folders
Consistent naming convention
Easy to load with PyTorch/TensorFlow
Ready for immediate use

CNN Model Training

Complete model training pipeline with support for popular architectures and transfer learning.

TensorFlow/Keras integration
PyTorch support included
Custom CNN architecture example
Transfer learning ready
ResNet50, VGG16, EfficientNet
MobileNetV2, InceptionV3 support
Model checkpointing
Learning rate scheduling
Early stopping callbacks
Training progress visualization
Model export & deployment

Data Augmentation

Advanced image augmentation utilities to expand your training data and improve model generalization.

Rotation (up to 20°)
Width & height shift (20%)
Horizontal flip
Zoom (up to 20%)
Shear transformation
Brightness adjustment
Color jittering
Random cropping
Albumentations integration
Real-time augmentation
Custom augmentation pipelines

Model Evaluation

Comprehensive evaluation tools with metrics, visualizations, and performance analysis.

Accuracy & Loss curves
Confusion matrix visualization
Classification report (Precision, Recall, F1)
ROC curves & AUC scores
Per-class accuracy breakdown
Grad-CAM visualization
Prediction confidence analysis
Error analysis tools
Model comparison framework
Benchmark against baselines
Export evaluation reports

Benchmark Results

Pre-computed benchmark results for popular architectures to guide your model selection.

EfficientNetB0 - 95% accuracy
ResNet50 - 94% accuracy
VGG16 - 92% accuracy
F1 Scores: 0.91 - 0.94
Training times: 1.8 - 3.0 hours
GPU memory requirements
Inference speed benchmarks
Model size comparison
Transfer learning results
Fine-tuning recommendations
Hyperparameter suggestions

What You Get

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

10,000+ labeled images
6 Python scripts (1,500+ lines)
Jupyter notebook for tutorials
Interactive demo website
Live AI prediction demo
Chart.js analytics dashboard
Comprehensive README.md
RELEASE_NOTES.md version history
Requirements.txt for easy setup
MIT License included
Metadata & statistics files

Interactive Demo Website

Beautiful demo website with live AI prediction, analytics dashboard, and image gallery.

Modern dark-themed design
Live AI prediction demo
Upload images for classification
Real-time prediction results
Confidence score display
Image gallery with filters
Category-based filtering
Chart.js analytics dashboard
Dataset statistics visualization
Dark/Light theme toggle
Fully responsive layout

Python Scripts Included

Professional Python scripts for data loading, model training, augmentation, and evaluation.

data_loader.py - Load & preprocess images
train_model.py - CNN training pipeline
augmentation.py - Image augmentation
evaluate.py - Model evaluation & metrics
advanced_utils.py - Grad-CAM, analysis
predict.py - Prediction & ensemble models
__init__.py - Package initialization
Well-documented code
Type hints included
Easy to customize
Production-ready structure

Categories & Labels

Diverse categories covering real-world objects for comprehensive image classification training.

Animals - Dogs, cats, birds, wildlife
Vehicles - Cars, trucks, motorcycles
Nature - Forests, mountains, beaches
Food - Fruits, vegetables, dishes
Buildings - Houses, monuments, temples
Fashion - Clothing, accessories
Aircraft - Airplanes, helicopters
Sports - Equipment & activities
Electronics - Phones, laptops, cameras
Furniture - Chairs, tables, sofas
Plants - Flowers, trees, succulents

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
TensorFlow/Keras - Deep learning (Apache 2.0)
PyTorch - Deep learning framework (BSD License)
NumPy - Numerical computing (BSD License)
PIL/Pillow - Image processing (HPND License)
OpenCV - Computer vision (Apache 2.0)
Scikit-learn - Machine learning (BSD License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
MIT License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Image Classification Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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Image Classification Dataset

Dataset Overview

Dataset Structure & Files

CNN Model Training

Data Augmentation

Model Evaluation

Benchmark Results

What You Get

Interactive Demo Website

Python Scripts Included

Categories & Labels

Credits & Acknowledgments

Support & Contact

Featured Content

Additional Sponsored Content

Download Free Source Code

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Categories

Technologies

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