Helmet Detection using YOLOv8

Welcome to the Helmet Detection project! This repository contains code for detecting helmets in images using the Ultralytics YOLOv8 model. This project is set up in a Colab notebook for ease of use and demonstrates how to utilize YOLOv8 for object detection tasks.

Table of Contents

• Overview
• Installation
• Usage
• Model Details
• Results
• Contributing
• License

Overview

The Helmet Detection project leverages the YOLOv8 model to identify helmets in images. This state-of-the-art object detection model is known for its speed and accuracy, making it suitable for real-time applications such as safety monitoring and compliance checks. The provided Colab notebook demonstrates how to load the model, perform inference on a set of images, and save the results.

Installation

To run the project, follow these steps:

1. Clone this repository:

   git clone https://github.com/td-02/helmet_detection.git

2. Install the necessary Python packages:

   Make sure you have Python installed, and then use pip to install the required dependencies:

   pip install ultralytics torch tensorflow

3. Open the Colab notebook:

   The project is designed to be used with the provided Colab notebook. You can open it in your browser to get started.

Usage

To use the project:

1. Open the Colab notebook in your browser.
2. Follow the instructions in the notebook to load the YOLOv8 model.
3. Perform inference on the provided images to detect helmets.
4. The detection results will be saved in the specified output directory for further analysis.

Model Details

• The project uses the Ultralytics YOLOv8 model, a modern object detection model that excels in speed and accuracy.
• The model's architecture and parameters can be found in the log output.
• The model is pretrained and ready to use, but can also be fine-tuned for specific datasets.

Results

• The detection results are saved in the specified output directory and can be visualized or further analyzed.
• You can review the output images to see the detection results, including the location of detected helmets.

Contributing

We welcome contributions to this project! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Submit a pull request with a detailed description of your changes.

Please follow the project's coding standards and guidelines when contributing. Contributions that enhance the project's capabilities and usability are especially appreciated.

License

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

Thank you for your interest in the Helmet Detection project! If you have any questions or issues, feel free to open an issue in this repository.