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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Model tree for sharathhhhh/safetyHelmet-detection-yolov8
Base model
Ultralytics/YOLOv8