CNN Model Training with CIFAR-10 Dataset

This project demonstrates the training of a Convolutional Neural Network (CNN) on the CIFAR-10 dataset using TensorFlow and Keras. The notebook includes data preprocessing, model definition, training, and evaluation.

Table of Contents

• Project Overview
• Features
• Techniques Used
• Installation
• Usage
• Results
• Contributing
• License

Project Overview

The goal of this project is to build and train a CNN model to classify images from the CIFAR-10 dataset into one of ten categories. The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class.

Features

• Data loading and preprocessing
• CNN model definition with multiple convolutional layers
• Model compilation and training
• Evaluation of model performance
• Saving and loading the trained model
• Visualization of training results

Techniques Used

Data Preprocessing

• Normalization of image pixel values to the range [0, 1]
• Conversion of labels to one-hot encoding

Model Definition

• Use of Sequential model from Keras
• Multiple convolutional layers with ReLU activation
• MaxPooling layers to reduce spatial dimensions
• Dropout layers to prevent overfitting
• Flattening layer to convert 2D feature maps to 1D feature vectors
• Dense layers for classification

Training

• Compilation of the model with RMSprop optimizer and categorical cross-entropy loss
• Training the model with a validation split to monitor performance
• Saving the trained model to disk

Evaluation

• Plotting training and validation loss and accuracy
• Loading the saved model for inference

Installation

To run this project, you need to have Python and the following libraries installed:

• TensorFlow
• Keras
• NumPy
• Matplotlib

You can install the required libraries using pip:

pip install tensorflow keras numpy matplotlib