CIFAR-10-Image-Classification

CIFAR-10 Image Classification with Custom Neural Network Architecture

A deep learning project implementing a custom neural network architecture for CIFAR-10 image classification, achieving 86.84% test accuracy through iterative improvements and optimisation techniques.

📋 Project Overview

This project demonstrates the development and optimisation of a neural network architecture called RashadNet for classifying images in the CIFAR-10 dataset. The architecture features a design with parallel convolutional paths and dynamic feature weighting, combined with modern training techniques to achieve strong performance.

🎯 Results

Model Version	Key Features	Test Accuracy
Baseline	3 blocks, basic architecture	42.10%
First Wave	Data augmentation, batch norm, dropout, 6 blocks	56.14%
Final Model	Label smoothing, cosine annealing, MaxPool, 10 blocks	86.84%

🔧 Technologies Used

• Python 3.11
• PyTorch: Deep learning framework
• torchvision: Dataset loading and image transformations
• matplotlib: Visualisation
• CUDA: GPU acceleration

📊 Dataset

The CIFAR-10 dataset contains:

• 60,000 32×32 colour images
• 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
• 50,000 training images
• 10,000 test images

🚀 Key Features & Techniques

Data Augmentation

• Random horizontal flips
• Random cropping with padding
• Normalisation to [-1, 1] range

Regularisation

• Batch normalisation after convolutions
• Dropout (p=0.3) in output block
• Label smoothing (0.1)

Training Optimisation

• Adam optimiser
• Cosine annealing learning rate scheduler
• Cross-entropy loss with label smoothing

Architecture Enhancements

• Deeper network (10 intermediate blocks)
• Wider network (64 base channels)
• MaxPool downsampling for feature preservation

📁 Project Structure

CIFAR-10 Image Classification/
├── html_export/
│   └── CIFAR10_image_classification.html     # Notebook exported as HTML
├── notebook/
│   └── CIFAR10_image_classification.ipynb    # Main Jupyter notebook
└── README.md                                   # Project documentation

🔬 Methodology

The project follows a structured approach:

1. Dataset Preparation: Loading and preprocessing CIFAR-10 with PyTorch DataLoaders
2. Basic Architecture: Implementing the custom RashadNet with intermediate blocks
3. Training & Testing: Establishing baseline performance and evaluation metrics
4. Iterative Improvements: Systematic enhancements through two major update waves

🙏 Acknowledgements

• CIFAR-10 dataset creators
• PyTorch community and documentation
• Inspiration from modern CNN architectures (ResNet, DenseNet, etc.)