This repository contains code for training and using a helmet detection algorithm using YOLOv7.

The dataset used for training the algorithm is not included in this repository. You can use your own dataset or download a helmet detection dataset from https://www.kaggle.com/datasets/andrewmvd/helmet-detection

To set up the environment for running the algorithm, follow these steps:

1. Clone this repository: git clone https://github.com/Jawabreh0/Helmet-Detection.

2. Navigate to the cloned repository: cd Helmet-Detection.

3. Download the YOLOv7 weights: wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt.

4. Download the YOLOv7x weights: wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt.

5. Install the required packages: pip install -r requirements.txt.

To train the algorithm, run the following command:

python train.py --data data.yaml --cfg models/custom_yolov7.yaml --weights yolov7.pt

You can adjust the batch size, number of epochs, and other parameters by modifying the command above.

To detect helmets in an image, run the following command:

python detect.py --weights runs/train/exp/weights/best.pt --img 640 --conf 0.4 --source path/to/image.jpg

This will output an image with bounding boxes around the detected helmets.

The trained model achieved an average precision of 0.85 on the test set.

This project is licensed under the MIT License