https://github.com/ayodimeji1/ai_convolutional_neural_network

https://github.com/ayodimeji1/ai_convolutional_neural_network

artificial-intelligence classification deep-neural-networks keras machine-learning matplotlib tensorflow

Last synced: 17 days ago
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• Host: GitHub
• URL: https://github.com/ayodimeji1/ai_convolutional_neural_network
• Owner: Ayodimeji1
• Created: 2024-11-04T05:58:46.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-04T06:16:32.000Z (2 months ago)
• Last Synced: 2024-12-22T05:14:18.618Z (17 days ago)
• Topics: artificial-intelligence, classification, deep-neural-networks, keras, machine-learning, matplotlib, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.97 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Convolutional Neural Network (CNN) Model for Image Classification

## Overview

This project is a Convolutional Neural Network (CNN) built using TensorFlow and Keras for image classification tasks. The primary focus of the model is to classify images into different categories using a deep learning approach. The project is developed using Python and includes essential exploration, dataset preparation, model training, and evaluation steps.

## Table of Contents

1. [Introduction](#introduction)

2. [Installation](#installation)

3. [Dataset](#dataset)

4. [Model Architecture](#model-architecture)

5. [Training and Evaluation](#training-and-evaluation)

6. [Results](#results)

7. [Usage](#usage)

10. [License](#license)

## Installation

Ensure that Python is installed on your system. Follow these steps to set up the environment:

1. Clone this repository.

 ```

git clone https://github.com/Ayodimeji1/CNN.git

```

2. Install the necessary dependencies: 

## Dataset

The dataset used in this project consists of images of flowers, split into five categories. The data is loaded using TensorFlow's `tf.keras.utils.image_dataset_from_directory` method.

- **Number of images**: 3670

- **Number of categories**: 5

- **Image size**: Resized to 256x256 pixels

## Model Architecture

The CNN is built using TensorFlow's Keras API and consists of multiple convolutional and pooling layers followed by dense layers for classification. The key components include:

- **Convolutional Layers**: Extract spatial features.

- **Pooling Layers**: Reduce dimensionality.

- **Dense Layers**: Perform the final classification.

## Training and Evaluation

The model is trained on the dataset with a specified batch size and uses a validation split to monitor performance. The notebook contains details about the training configurations and metrics used for evaluation.

## Results

Details about the model's performance, including accuracy and loss plots, are shown in the notebook. 

## Usage

To use this model for your own dataset:

1. Ensure your images are organized in a directory structure similar to `dataset_name/class_name/image.jpg`.

2. Adjust the dataset path in the code:

   ```

   python

   dataset = tf.keras.utils.image_dataset_from_directory(

       'your_dataset_path', batch_size=500,

       image_size=(256, 256))

   ```

3. Run the training cells in the provided notebook.

## Dependencies

- Python

- TensorFlow

- Keras

- NumPy

- Matplotlib

## License

This project is licensed under the MIT License. See the `LICENSE` file for more details.