image-classification-project

This project implements a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset into one of 10 categories. The goal is to build, train, and evaluate a deep learning model capable of accurately identifying images of airplanes, cars, animals, and more.

Dataset

The project uses the CIFAR-10 datset, which contains:

• 60,000 32x32 color images in 10 classes
• 50,000 training images and 10,000 test images
• Classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck

Features

• Data preprocessing and normalization
• One-hot encoding of labels
• CNN architecture with convolutional, pooling, and dense layers
• Early stopping during training to prevent overfitting
• Visualization of predictions with correct/incorrect labels
• Model evaluation on test data
• Saving the trained model

Tools / Libraries

• Python 3.x
• TensorFlow / Keras
• NumPy
• Matplotlib
• scikit-learn