Image Classification with CNN

This repository contains a comprehensive implementation of image classification using Convolutional Neural Networks (CNN). The model architecture includes multiple convolutional layers followed by pooling layers, fully connected layers, and dropout for regularization. The project demonstrates training CNNs using popular datasets like CIFAR-10 and MNIST. Code is optimized for easy modification and extension to other datasets and real-time image classification tasks. The repository also includes preprocessing techniques, data augmentation methods, and evaluation strategies like confusion matrices and classification reports.