---
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](https://github.com/ultralytics/ultralytics) 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](https://github.com/ultralytics/ultralytics)
- **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 sharathu0123@gmail.com for collaborations or suggestions.


##  Example Usage

Install dependencies:
```bash
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()