Yolov8n Hard Hat Detection

🚀 keremberke/yolov8n-hard-hat-detection

This model is designed for hard hat detection in vision tasks, leveraging the power of YOLOv8 and the ultralytics library.

🚀 Quick Start

This guide will show you how to use the keremberke/yolov8n-hard-hat-detection model for object detection.

✨ Features

• Object Detection: Specifically trained for hard hat detection in images.
• Supported Labels: ['Hardhat', 'NO-Hardhat']

📦 Installation

First, you need to install the necessary libraries:

pip install ultralyticsplus==0.0.24 ultralytics==8.0.23

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8n-hard-hat-detection')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()

📚 Documentation

Model Information

Metrics

The model has been evaluated on the validation split of the keremberke/hard-hat-detection dataset. The following metrics are available:

More models available at: awesome-yolov8-models