---
language:
- en
license: mit
library_name: ultralytics
tags:
- object-detection
- yolo
- yolov11
- computer-vision
- construction
- safety
- ppe
datasets:
- roboflow/construction-site-safety
metrics:
- mAP50
---

# ConstructIQ Monitor - Construction Safety Detection (YOLOv11s)

This is a fine-tuned **YOLOv11s** object detection model designed specifically for construction site monitoring and safety compliance. It detects workers, heavy machinery, and Personal Protective Equipment (PPE) to automate hazard identification and site intelligence.

This model is a core component of the **[SiteSpectra / ConstructIQ](https://github.com/2005legend/ConstructIQ-Monitor)** computer vision pipeline.

## 🏗️ Supported Classes
The model detects 10 distinct classes relevant to construction reality-capture:
- `Hardhat`
- `Mask`
- `NO-Hardhat`
- `NO-Mask`
- `NO-Safety Vest`
- `Person`
- `Safety Cone`
- `Safety Vest`
- `machinery`
- `vehicle`

## 📊 Training & Validation Metrics
The model was fine-tuned for 20 epochs on a dataset of over 2,800 construction site images. It achieved strong validation metrics, particularly on critical safety and equipment classes:
- **Overall mAP50:** 80.6%
- **Machinery mAP50:** 92.0%
- **Hardhat mAP50:** 88.9%
- **Safety Vest mAP50:** 87.0%
- **Person mAP50:** 83.5%

## 🚀 How to Use

You can easily use this model in Python using the `ultralytics` library.

### Installation
```bash
pip install ultralytics huggingface_hub