---
license: agpl-3.0
language:
- en
metrics:
- precision
- accuracy
- f1
- recall
tags:
- object-detection
- computer-vision
- yolo
- yolov11
- helmet-detection
- safety
- traffic-monitoring
- cctv
pipeline_tag: object-detection
library_name: ultralytics
base_model:
- Ultralytics/YOLO11
---

# Helmet and No-Helmet Detection — YOLO11s

A YOLO11s object detection model trained for detecting **helmets** and
**no-helmet instances** in images.

The training dataset was obtained from Roboflow and was originally
created by another Roboflow user. The dataset was forked and used for
training this model. Dataset attribution and licensing information are
provided below.

## Model Description

This model is a custom-trained **Ultralytics YOLO11s** object detection
model.

The model predicts two classes:

| Class ID | Class |
|----------|-------|
| 0 | helmet |
| 1 | no-helmet |

The model accepts images at a nominal resolution of **640 × 640 pixels**
and produces bounding-box detections for the two classes.

### Model Architecture

- Architecture: YOLO11s
- Task: Object Detection
- Parameters: 9,428,566
- Layers: 181
- GFLOPs: 21.6
- Input image size: 640 × 640
- Number of classes: 2

The checkpoint identifies the architecture as YOLO11s and reports
181 layers, 9,428,566 parameters, and 21.6 GFLOPs.

## Intended Use

This model is intended for research, experimentation, and computer
vision applications involving helmet compliance detection.

Potential applications include:

- Helmet detection in CCTV footage
- Road safety monitoring
- Traffic violation detection
- Industrial safety monitoring
- Motorcycle helmet compliance analysis
- Computer vision research

The model is particularly intended as a component of a larger
computer vision pipeline rather than as a complete traffic-violation
system.

For example:
```text
    CCTV Image
         |
         v
    Helmet Detector
         |
         +---- helmet
         |
         +---- no-helmet
         |
         v
    Person / Vehicle Association
         |
         v
    Number Plate Detection
         |
         v
    Violation Processing
```
This model itself only performs helmet/no-helmet object detection.

## Classes

The class mapping stored in the trained checkpoint is:

    0: helmet
    1: no-helmet

The model should therefore be interpreted using this class mapping
when processing its predictions.

## Training

The model was trained using a dataset exported from Roboflow.

The original dataset was not created by the author of this model.
Instead, the dataset was forked from an existing Roboflow dataset and
subsequently used for training.

The checkpoint stores the following training configuration.

### Training Configuration

| Parameter | Value |
|-----------|-------|
| Task | Detection |
| Image size | 640 |
| Batch size | 16 |
| Epochs configured | 100 |
| Pretrained | True |
| Optimizer | Auto |
| Workers | 8 |
| AMP | True |
| Seed | 0 |
| Deterministic | True |
| Patience | 10 |
| Validation | True |
| Validation split | val |

The checkpoint was produced using Ultralytics version 8.3.233.

The stored checkpoint metadata identifies the model as an
`ultralytics.nn.tasks.DetectionModel`.

## Data Augmentation

The stored training configuration includes the following
augmentation settings:

| Augmentation | Value |
|--------------|-------|
| Mosaic | 1.0 |
| MixUp | 0.12 |
| Copy-Paste | 0.05 |
| Horizontal Flip | 0.5 |
| Scale | 0.6 |
| Rotation | 4.0 |
| Translation | 0.1 |
| Shear | 1.0 |
| Perspective | 0.0004 |

These values are reported from the training configuration stored
inside the model checkpoint.

## Dataset

### Original Dataset

The training dataset was obtained from Roboflow.

**Original dataset:**  
[![Helmet-detection_yolov8-8jenr](https://img.shields.io/badge/helmet%20detection-yolov8%208jenr(Dataset)-00000)](https://app.roboflow.com/smolry/helmet-detection_yolov8-8jenr/1/images)

**Original dataset author:**  
[![Smolry](https://img.shields.io/badge/Smolry-00000)](https://app.roboflow.com/smolry)

**Roboflow project:**  
[![helmet-detection_yolov8-8jenr](https://img.shields.io/badge/helmet%20detection-yolov8%208jenr-00000)](https://app.roboflow.com/smolry/helmet-detection_yolov8-8jenr)

**Dataset version:**  
[v1 2026-01-25 2:39am]

The dataset was forked from the original Roboflow project and used as
the basis for training this model.

### Dataset Attribution

This model does not claim ownership of the original dataset.

The dataset and its annotations remain subject to the original
dataset's license and attribution requirements.

Users of this model should consult the original dataset page and
license before redistributing the dataset, annotations, or derived
datasets.


## Data Preprocessing

The model was trained using the YOLO-compatible dataset configuration
exported from Roboflow.

The checkpoint references the following dataset configuration:

    /content/Helmet-and-Non-Helmet-Detection--2/data.yaml

The original training environment was hosted in Google Colab /
Google Drive according to paths recorded in the checkpoint.

## Model Input

The model expects an image input and was trained using:

    640 × 640

Ultralytics handles the necessary image preprocessing during normal
inference.

## Inference

Install Ultralytics:

    pip install ultralytics

Load the model:

```python
from ultralytics import YOLO

model = YOLO("best.pt")

results = model("image.jpg", imgsz=640)

for result in results:
    result.show()