Fire Detection

This repository contains code for a Fire Detection system using deep learning techniques. The system utilizes the ResNet50 architecture pre-trained on the ImageNet dataset and is fine-tuned for the task of detecting fires in images.

Overview

The project consists of the following main components:

1. Data Collection: The dataset used for training, validation, and testing is organized into directories containing images of fire and non-fire scenarios.

2. Data Preprocessing: Data augmentation techniques are applied to generate additional training samples and enhance the diversity of the dataset.

3. Model Training: A deep learning model based on the ResNet50 architecture is trained using the preprocessed data. The training script can be found in the training.py file.

4. Model Evaluation: The trained model is evaluated on a separate test dataset to assess its performance in detecting fires.

5. Prediction: The trained model can be used to predict whether a given image contains a fire or not. An example of how to use the model is provided in the Model_Use.ipynb notebook.

6. Deployment: The deployment folder contains a Main.py file which serves as the entry point for deploying the model locally. To deploy the model, run the following command:

streamlit run deployment/Main.py

Usage

1. Clone the repository:

git clone https://github.com/datharv07/Fire-Detection.git

2. Install the required dependencies:

pip install -r requirements.txt

3. Train the model:

python training.ipynb

4. Make predictions:

python Model Use .ipynb path_to_image

Replace path_to_image with the path to the image you want to make predictions on.

Screenshots

Requirements

The following Python libraries are required to run the code:

• tensorflow
• keras
• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn
• opencv-python
• tqdm
• pillow
• scikit-image

These dependencies can be installed using the requirements.txt file provided in the repository.

Contributors

• Daware Atharv
• Kaggel Data Set