Fire Detection using Convolutional Neural Networks (CNN)
This repository contains code for a Fire Detection system implemented using Convolutional Neural Networks (CNNs). The system is designed to classify images as either containing fire or not containing fire. Table of Contents
Introduction
Requirements
Usage
File Structure
Results
License
Introduction
The goal of this project is to develop a CNN-based model capable of accurately detecting fire in images. The dataset used for training and testing the model consists of images categorized into two classes: 'fire' and 'non-fire'. The CNN model is trained on this dataset to learn the distinguishing features between fire and non-fire images. Requirements
To run the code, you need to have the following dependencies installed:
Python 3.x
TensorFlow
Keras
Pandas
NumPy
Matplotlib
Seaborn
OpenCV
Plotly
Scikit-learn
Usage
Clone the repository to your local machine.
Ensure all the necessary dependencies are installed (see Requirements).
Run the fire_detection.py script.
File Structure
fire_detection.py: Main Python script containing the code for fire detection using CNNs.
README.md: Documentation file providing an overview of the project, usage instructions, and other relevant information.
requirements.txt: Text file listing all the Python dependencies required to run the code.
fire_dataset: Directory containing the dataset with two subdirectories: fire_images and non_fire_images, each containing respective images.
Results
Upon running the fire_detection.py script, the CNN model is trained on the dataset, and its performance is evaluated. Additionally, the script allows for predicting whether a given input image contains fire or not.
Fire Dataset