Model Details

Model Description

This model is trained to detect whether a person is wearing a helmet or not, using YOLOv8. This is a custom-trained YOLOv8 model that detects whether a person is wearing a helmet or not. The goal is to improve road safety and ensure helmet compliance using computer vision.

  • Developed by: sharathhhhh
  • Model type: Object detection
  • Language(s) (NLP): English
  • License: Apache license 2.0
  • Finetuned from model [optional]: YOLOv8

Model Details

  • Model: YOLOv8
  • Framework: Ultralytics YOLO
  • Backbone: CSPDarknet
  • Trained for: Helmet detection on riders using CCTV/video surveillance
  • Input size: 640x640
  • Classes:
    • with_helmet
    • without_helmet

Training Configuration

  • Epochs: 28
  • Optimizer: SGD (default)
  • Loss: YOLOv8 objectness + box + class
  • Image Size: 640x640
  • Batch Size: 16

Feel free to reach out on LinkedIn(sharathup) or email me at [email protected] for collaborations or suggestions.

Example Usage

Install dependencies:

pip install ultralytics


#Load model and predict:

from ultralytics import YOLO
model = YOLO("your-username/helmet-detection-yolov8")

# Predict on image
results = model("rider.jpg")

# Display results
results[0].show()
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