CIFAR-10 Image Classification using Convolutional Neural Networks

Project Overview

This project implements an image classification model using Convolutional Neural Networks (CNNs) to classify images from the CIFAR-10 dataset. The model achieves an accuracy of 80% after training for 20 epochs.

Table of Contents

• Dataset
• Model Architecture
• Requirements
• Installation
• Training
• Results

Dataset

The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes:

• Airplane
• Automobile
• Bird
• Cat
• Deer
• Dog
• Frog
• Horse
• Ship
• Truck

Each class contains 6,000 images, with 50,000 training images and 10,000 test images

Model Architecture

The CNN model consists of:

• Convolutional layers for feature extraction
• Max pooling layers for downsampling
• Fully connected layers for classification
• ReLU activation functions
• Batch normalization for improved training stability

Requirements

• Python 3.8+
• PyTorch 1.10+
• torchvision
• matplotlib
• numpy
• seaborn

Installation

To run this project, you need to install the following packages:

pip install numpy
pip install pandas
pip install matplotlib
pip install torch torchvision torchaudio
pip install seaborn
pip install scikit-learn==3.5.0

Training

Training parameters:

• Epochs: 20
• Batch Size: 64
• Learning Rate: 0.001
• Optimizer: Adam
• Loss Function: Cross-Entropy Loss

Per Class Metrics

Results

• Final Accuracy: 82%
• Training Time: Approximately 15-20 minutes (depends on hardware)
• Validation Accuracy: 78-82%