---
license: mit
library_name: ultralytics
pipeline_tag: object-detection
tags:
  - object-detection
  - fire-hydrant
  - fire-hydrants
  - yolo
  - yolo26
  - gis
  - geospatial
  - computer-vision
  - street-view
  - infrastructure
---

# 🚒Fire Hydrant Detection

### An open-source YOLO object detection model for automatically detecting fire hydrants in street-level imagery.

[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-yellow)](https://huggingface.co/)
[![YOLO](https://img.shields.io/badge/YOLO-26-blue)](https://github.com/ultralytics/ultralytics)
[![Task](https://img.shields.io/badge/Task-Object%20Detection-green)](#)
[![Class](https://img.shields.io/badge/Class-Fire%20Hydrant-red)](#)

---

![val_batch1_labels](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/yJP32V4_Am_41iFdCM_Ar.jpeg)


## 🚒 Overview

This project provides a YOLO-based computer vision model designed to detect **fire hydrants in street-level imagery**.

The goal is to make it easier for GIS professionals, municipalities, utilities, researchers, and developers to automatically identify fire hydrants from imagery and support infrastructure inventory and mapping workflows.

The model was trained specifically for the visual characteristics of fire hydrants appearing in street-level imagery.

### What can it do?

Given an image such as:

> 📷 Street-level imagery

the model attempts to identify:

> 🚒 **Fire Hydrant**

and return its location using a bounding box and confidence score.

---

## 🎯 Model

| Property | Value |
|---|---|
| Architecture | YOLO26n |
| Task | Object Detection |
| Classes | 1 |
| Detected class | Fire Hydrant |
| Image size | 640 × 640 |
| Annotation format | YOLO |
| Framework | Ultralytics |
| Model file | `best.pt` |

---

## 📊 Performance


![BoxR_curve](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/pm5Y5BM3jTp8oeFgkc62f.png)
![confusion_matrix](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/qk5OychNJdkANQCCsQtX7.png)
![confusion_matrix_normalized](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/YE1N9wQzg8YCAtrXjGPb2.png)
![labels](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/eSwtAJFHm8_zF_G8m5R_r.jpeg)
![BoxF1_curve](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/jj39fuV7WP7I9OGJGgh9f.png)
![BoxP_curve](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/Mge3ZQd7duITtJe3i15fB.png)
![BoxPR_curve](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/KeXWOxjt7yoConHZd2rAf.png)

![results](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/7DHX82m7LHryJEsviwZUQ.png)

---

## 📸 Example Results

### Fire Hydrant Detection


![val_batch1_pred](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/SPd_O5xWgIwM8lB9Vf0IN.jpeg)

![val_batch2_pred](https://cdn-uploads.huggingface.co/production/uploads/6a85c37d567565e7056b19d9/YxIe1tEUs3HMUf9TZ5au_.jpeg)

# 🚀 Quick Start


## 1. Install Ultralytics

```bash
pip install ultralytics
```

## 2. Download the model

Download **best.pt** from this repository

## 3. Run inference

```bash
  from ultralytics import YOLO
  
  # Load the fire hydrant detection model
  model = YOLO("best.pt")
  
  # Run detection
  results = model("street_view.jpg")
  
  # Display the result
  results[0].show()

```
## 4. Python Example
You can adjust the confidence threshold depending on your application.
Ex : **conf=0.25** will generally produce more detections, while **conf=0.75** will require greater confidence before returning a detection.
```bash
from ultralytics import YOLO

model = YOLO("best.pt")

results = model.predict(
    source="street_view.jpg",
    conf=0.50,
    imgsz=640
)

for result in results:
    result.show()
```
# 🗂️ Dataset
The model was trained using a dataset of street-level images containing fire hydrants.
Dataset characteristics
Images: 1,123
Object class: Fire Hydrant
Annotation format: YOLO bounding boxes
Image source: Street-level imagery
Viewing directions: North, South, East, and West

The dataset was created specifically for fire hydrant detection and infrastructure mapping applications.

**⚠️ The original imagery used for training may be subject to third-party licensing and usage restrictions and is therefore not necessarily redistributed with this model.**

# 🏋️ Training
The model was trained using the Ultralytics YOLO framework.
``` bash
    yolo detect train \
    data=dataset.yaml \
    model=yolo26n.pt \
    epochs=100 \
    imgsz=640
```

# 🗺️ GIS & Utility Applications
This model can potentially support a variety of GIS and infrastructure workflows. (Current famous models like struggle to detect fire hydrants or simply do not have a class for it)

## 1. Municipal GIS

Automatically identify hydrants from street-level imagery and assist with updating infrastructure inventories.

## 2. Utility Mapping

Use computer vision to help locate hydrants that may be missing from existing GIS datasets.

## 3. Asset Inventory

Compare detected hydrants against existing GIS features to identify:

Potential missing hydrants
Potential duplicate features
Possible positional discrepancies
Areas requiring field verification

## 4. Field Verification

Use model detections as a starting point for GIS technicians and field crews.

# ⚠️ Limitations
This model should be considered an assistive computer vision tool, not a replacement for field verification or authoritative GIS data.

Performance may vary depending on:

Image quality
Lighting conditions
Weather
Camera angle
Distance from the hydrant
Hydrant design
Vegetation
Vehicles or other obstructions
Image resolution
Geographic location

Hydrants that are heavily occluded or visually different from those represented in the training data may be missed.

Always verify important infrastructure information against authoritative GIS or field data.

# 🌎 Geographic Considerations
The training imagery represents a particular geographic and environmental context.

  Because fire hydrant designs, colors, installation practices, vegetation, road environments, and imagery characteristics vary between regions, performance should be evaluated before deploying the model in a new geographic area.

# 🤝 Contributing
Contributions are welcome.

Possible areas for contribution include:

Additional training images
Improved annotations
New geographic regions
Model evaluation
False-positive analysis
False-negative analysis
GIS integration
Detection workflows

If you use this model in a project, feedback and performance results are appreciated

# 📜 License
This model is released under the MIT License.

Please review the licensing requirements of the underlying training imagery and any third-party components used with this project.