README.md

metadata

license: apache-2.0
language:
  - en
base_model:
  - Ultralytics/YOLOv8
tags:
  - yolov8
  - object-detection
  - computer-vision
  - deep-learning
  - road-safety-ai

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()