docs/en/datasets/detect/construction-ppe.md
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The Ultralytics Construction-PPE dataset is an object detection dataset of 1,416 images (1,132 for training, 143 for validation, and 141 for testing) labeled across 11 classes for detecting personal protective equipment — helmets, gloves, vests, boots, and goggles — and flagging missing gear on construction sites. Curated from real construction environments, it includes both compliant and non-compliant cases, making it a practical resource for training computer vision models that monitor workplace safety.
<p align="center"> <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lFaVnrhMmaE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen> </iframe><strong>Watch:</strong> How to train Ultralytics YOLO on Personal Protective Equipment Dataset | VisionAI in Construction 👷
</p>The Construction-PPE dataset contains 1,416 images split into three predefined subsets, defined by the construction-ppe.yaml configuration:
| Split | Images | Annotations |
|---|---|---|
| Train | 1,132 | Yes |
| Validation | 143 | Yes |
| Test | 141 | Yes |
Every image is annotated in the Ultralytics YOLO format, ensuring compatibility with state-of-the-art object detection and tracking pipelines.
The dataset provides 11 classes covering worn gear, missing gear, and people:
helmet, gloves, vest, boots, gogglesno_helmet, no_gloves, no_boots, no_gogglePerson, nonePairing worn and missing labels lets a model both detect properly worn gear and flag safety violations. Note that vest has no dedicated missing-vest label.
Construction is one of the most hazardous industries, and the challenge is usually enforcement rather than a lack of regulation. Health-and-safety teams are stretched thin and cannot watch every corner of a busy, ever-changing site in real time.
Computer-vision-based PPE detection helps close that gap. By automatically checking whether workers wear the required helmets, vests, and other gear, it enforces safety rules consistently across sites and surfaces leading indicators of risk — revealing compliance trends before incidents occur. PPE detection can also flag unauthorized site intruders, who are typically the first to appear without proper safety gear.
Construction-PPE powers a variety of safety-focused computer vision applications:
To label, train, and deploy a PPE detection model without managing local infrastructure, run the full workflow in your browser with Ultralytics Platform.
The Construction-PPE dataset includes a YAML configuration file that defines the train, validation, and test image paths along with the full list of object classes. You can access the construction-ppe.yaml file directly in the Ultralytics repository here: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/construction-ppe.yaml
!!! example "ultralytics/cfg/datasets/construction-ppe.yaml"
```yaml
--8<-- "ultralytics/cfg/datasets/construction-ppe.yaml"
```
You can train a YOLO26n model on the Construction-PPE dataset for 100 epochs with an image size of 640. The following examples show how to get started quickly. For more options and advanced configurations, see the Training guide.
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load pretrained model
model = YOLO("yolo26n.pt")
# Train the model on Construction-PPE dataset
model.train(data="construction-ppe.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
yolo detect train data=construction-ppe.yaml model=yolo26n.pt epochs=100 imgsz=640
```
The dataset captures construction workers across varied environments, lighting conditions, and postures. Both compliant and non-compliant cases are included.
Construction-PPE is developed and released under the AGPL-3.0 License, supporting open-source research and commercial applications with proper attribution.
If you use this dataset in your research, please cite it:
!!! quote ""
=== "BibTeX"
```bibtex
@dataset{Dalvi_Construction_PPE_Dataset_2025,
author = {Mrunmayee Dalvi and Niyati Singh and Sahil Bhingarde and Ketaki Chalke},
title = {Construction-PPE: Personal Protective Equipment Detection Dataset},
month = {January},
year = {2025},
version = {1.0.0},
license = {AGPL-3.0},
url = {https://docs.ultralytics.com/datasets/detect/construction-ppe/},
publisher = {Ultralytics}
}
```
Unlike generic construction datasets, Construction-PPE explicitly includes missing-equipment classes (no_helmet, no_gloves, no_boots, no_goggle). This dual-labeling approach lets a model not only detect worn PPE but also flag violations in real time.
The Construction-PPE dataset has 11 classes: five worn-PPE items (helmet, gloves, vest, boots, goggles), four missing-PPE labels (no_helmet, no_gloves, no_boots, no_goggle), plus Person and a generic none class. Note that vest has no dedicated missing-vest label.
The Construction-PPE dataset contains 1,416 images across 11 classes — 1,132 for training, 143 for validation, and 141 for testing. See the Dataset Structure section for the full split and class breakdown.
The dataset (178.4 MB) downloads automatically the first time you train with data="construction-ppe.yaml" — no manual step is required. Ultralytics fetches and unpacks it to your local datasets directory. You can browse related datasets in the detection datasets overview.
To train a YOLO26 model using the Construction-PPE dataset, you can use the following code snippets:
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="construction-ppe.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo detect train data=construction-ppe.yaml model=yolo26n.pt epochs=100 imgsz=640
```
Yes. Images are curated from real construction sites under diverse conditions, which makes the dataset highly effective for building deployable workplace safety monitoring systems.
The dataset enables real-time detection of personal protective equipment, helping monitor worker safety on construction sites. With classes for both worn and missing gear, it supports AI systems that can automatically flag safety violations, generate compliance insights, and reduce risks. It also provides a practical resource for developing computer vision solutions in workplace safety, robotics, and academic research.