Weapon Detection using Computer Vision and Deep Learning.

How to Execute the Code:

Prerequisites

• Python 3.x installed.
• PyTorch and OpenCV libraries installed.
• YOLOv8 model downloaded.
• Access to the GitHub repository of the project. (Project code will be attached as zip file)

Installation Steps

Environment Setup

Ensure Python 3.x is installed on your system. Install PyTorch: Visit the PyTorch official website and follow the installation instructions suitable for your system. Install OpenCV: pip install opencv-python install the YOLOv8: Install the ultralytics package from PyPI: pip install ultralytics Install YOLOv5 dependencies: check the official doc

Data Preparation

Download the annotated firearm dataset to a known directory. Preprocess the dataset as necessary (resizing, normalization, etc.).

Model Training (If applicable)

Navigate to your training script directory. Execute the training script: python NN_modelTester.py --data --cfg --weights --epochs

Running the Weapon Detection System

Open the weapon detection script. Configure the script to link to the video feed source and the trained model. Run the script: python Detection.py

Real-Time Detection

The system will process the video feed in real-time. Detected weapons will be identified with bounding boxes.

Troubleshooting

Confirm all dependencies are properly installed. Verify the dataset paths in the scripts. For YOLOv8-related issues, refer to the official YOLOv8 GitHub repository's issues section.