Image Classification Using CNN

Image Classification Using CNN

Built a CNN-based image classification system on CIFAR-10 with VGGNet-inspired architecture from a paper, achieving 90% accuracy. Included model visualization, training pipeline, and regularization techniques.

Overview

This project implements a Convolutional Neural Network (CNN) for image classification using the CIFAR-10 dataset. The model is inspired by the VGGNet architecture and incorporates modern deep learning techniques for optimal performance.

Key Features

• High Accuracy: Achieved 90% accuracy on CIFAR-10 test set
• VGGNet-inspired Architecture: Deep convolutional layers with small filters
• Model Visualization: Comprehensive visualization of training metrics and model performance
• Regularization Techniques: Dropout, batch normalization, and data augmentation
• Complete Pipeline: End-to-end training and evaluation pipeline

Technical Implementation

Architecture Details

• Multiple convolutional layers with 3x3 filters
• Batch normalization after each convolutional layer
• ReLU activation functions
• Max pooling for downsampling
• Fully connected layers for classification
• Dropout for regularization

Training Pipeline

• Data augmentation for better generalization
• Learning rate scheduling
• Early stopping to prevent overfitting
• Model checkpointing for best weights

Results

The model demonstrates strong performance across all 10 CIFAR-10 classes:

• Training Accuracy: 92%
• Validation Accuracy: 90%
• Test Accuracy: 90%

Technologies Used

• PyTorch: Deep learning framework
• Python: Programming language
• CIFAR-10: Dataset
• Matplotlib: Visualization
• NumPy: Numerical computing

Repository

Check out the complete implementation and detailed documentation:

View Project on GitHub

The repository includes:

• Complete source code
• Training notebooks
• Model visualization scripts
• Detailed documentation
• Results and analysis