Image Classification using CNN Models

This repository contains code and resources for building and training Convolutional Neural Network (CNN) models for image classification. The implementation is done using popular deep learning frameworks like TensorFlow and Keras.

Table of Contents

• Introduction
• Requirements
• Usage
• Dataset
• Models
• Results

Introduction

This project aims to demonstrate the process of image classification using CNN models. It includes various CNN architectures and provides a structured way to train, evaluate, and compare their performance on a given dataset.

Requirements

To run the code in this repository, you need to have the following dependencies installed:

• Python (>=3.6)
• TensorFlow (>=2.0)
• Keras (>=2.2)

Usage

Clone the repository

git clone https://github.com/MahakArora/Image-Classification-using-CNN-models.git
cd Image-Classification-using-CNN-models

Dataset

The repository includes a sample dataset for testing purposes. However, for real-world applications, you may want to replace it with your own dataset

Models

Various CNN architectures, such as VGG16, ResNet, and custom models, are implemented in this project. Check the Models directory for the model implementations.

Results

The Results section provides insights into the performance of different models on the test dataset, including accuracy, precision, recall, and F1-score