Gulam-Kibria-GK/Image-Classification-using-CNN

The project is about Image Classification system. This system has the ability to detect Images. It is a very simple and easy way to Classifi images using this system.

Image Classification using CNN

This project implements an Image Classification system using Convolutional Neural Networks (CNNs). The system is designed to detect and classify images, offering a simple and effective solution for image classification tasks.

Contents

1. Introduction
2. Files
3. Usage
4. Dependencies
5. Author

Introduction

The Image Classification using CNN project leverages deep learning techniques, specifically CNNs, to classify images into predefined categories. With this system, users can train a model to recognize patterns and features in images, enabling automated image classification tasks.

Files

1. Dataset: The dataset used for training the image classification model.
2. Image Classification using CNN.ipynb: Jupyter Notebook containing the Python code for building and training the CNN model.
3. Image_Classification_model_1.h5: Pre-trained model weights saved in HDF5 format.
4. README.md: This file, providing an overview of the project.

Usage

To use this project, follow these steps:

1. Ensure you have the necessary dependencies installed (see Dependencies).
2. Open and run the Image Classification using CNN.ipynb Jupyter Notebook.
3. Follow the instructions in the notebook to train and evaluate the CNN model.
4. Optionally, load the pre-trained model weights from Image_Classification_model_1.h5 for inference.

Dependencies

This project requires the following dependencies:

• Python 3.x
• TensorFlow
• Keras
• NumPy
• Matplotlib
• scikit-learn
• OpenCV
• Jupyter Notebook

You can install these dependencies using pip:

pip install tensorflow keras numpy matplotlib scikit-learn opencv-python jupyter

Author

Gulam Kibria Chowdhury
Software Developer || Competitive Programmer
Sylhet, Bangladesh
Gmail: gkchowdhury101@gmail.com