Image Classification using ANN & CNN

Overview

This project implements both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) models for image classification using the CIFAR-10 Dataset. The models are trained to classify images into 10 categories, including airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks.

Features

• 🖼 Image Classification – Classifies images into 10 predefined categories.
• 🤖 Artificial Neural Network (ANN) – Implements a basic deep learning model for classification.
• 🧠 Convolutional Neural Network (CNN) – Enhances feature extraction for improved accuracy.
• 📊 Data Preprocessing – Normalizes and augments images for better training performance.

Tech Stack

• Programming Language: Python
• Libraries: TensorFlow/Keras, NumPy, Matplotlib
• Dataset: CIFAR-10 Dataset

Usage

• Load and preprocess the CIFAR-10 dataset.
• Train the ANN model and evaluate its performance.
• Train the CNN model for improved classification accuracy.
• Compare results between ANN and CNN models.