Yolov8n Nlf Head Detection

🚀 keremberke/yolov8n-nlf-head-detection

This is an object - detection model based on YOLOv8, which can be used for head detection in NFL - related scenarios, providing reliable object - detection capabilities.

✨ Features

• Object Detection: Specifically designed for object detection tasks, especially for detecting heads in NFL scenarios.
• Supported Labels: Can identify multiple types of helmets, including 'Helmet', 'Helmet - Blurred', 'Helmet - Difficult', 'Helmet - Partial', and 'Helmet - Sideline'.

📦 Installation

Install ultralyticsplus:

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-nlf-head-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

Supported Labels

['Helmet', 'Helmet-Blurred', 'Helmet-Difficult', 'Helmet-Partial', 'Helmet-Sideline']

Model Metrics

More Models

More models available at: awesome - yolov8 - models