Y4S1-Machine-Learning

CIFAR-10 Image Classification Enhancement Using AlexNet and Advanced Optimization Techniques

This repository contains our comprehensive study and implementation for enhancing image classification performance on the CIFAR-10 dataset, a staple in the computer vision field. Our project embarks on an exploration of leveraging advanced convolutional neural network architectures and optimization techniques to achieve significant improvements in classification accuracy and model efficiency.

Project Overview

• Dataset: CIFAR-10, featuring 60,000 32x32 color images across 10 distinct classes.
• Initial Setup: A basic CNN architecture with Stochastic Gradient Descent (SGD) optimization.
• Advanced Implementation: Transition to the more sophisticated AlexNet architecture, paired with innovative optimization techniques.
• Optimization Techniques:
  • SGD with momentum
  • Adam optimizer coupled with dropout layers for enhanced regularization and adaptive learning rate capabilities.

Key Features

1. AlexNet + SGD Implementation:

   • Explores the impact of replacing a basic CNN with AlexNet.
   • Evaluates the effectiveness of SGD optimization in deep learning architectures.

2. AlexNet + Adam + Dropout Implementation:

   • Demonstrates the advantages of the Adam optimizer's adaptive learning rates.
   • Incorporates dropout layers to combat overfitting and encourage robust feature learning.

3. Performance Analysis:

   • Detailed accuracy and loss metrics for both implementations.
   • Insights from confusion matrices and class-wise performance evaluation.
   • Comparative study showcasing the improvements in accuracy and generalization when transitioning from SGD to Adam with dropout.

Repository Contents

• Source code for AlexNet implementations with SGD and Adam + Dropout optimizations.
• Performance evaluation scripts and visualization tools.
• Detailed documentation explaining the methodologies, results, and insights.

Key Results

• Improved overall test accuracy and reduction in overfitting.
• Enhanced understanding and treatment of complex datasets like CIFAR-10.
• Practical insights into the efficacy of deeper architectures and sophisticated training algorithms.

Future Directions

• Further hyperparameter tuning and explorations with newer architectures.
• Incorporating state-of-the-art practices to elevate model performance.