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Nexus3/
yolov8n-ppe-detection-6classes

Name: yolov8n-ppe-detection-6classes
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🦺 YOLOv8n PPE Detection (6 Classes)

A lightweight and fast Personal Protective Equipment (PPE) detection model trained on 6 safety classes, optimized for edge devices such as NVIDIA Jetson Orin, ONNX Runtime, and TensorRT deployment.

1. 📌 Model Details

Model name: YOLOv8n PPE Detection – 6 Classes
Architecture: Ultralytics YOLOv8n (Nano)
Task: Object Detection (PPE / Safety Gear)
Framework: PyTorch (Ultralytics), exported to ONNX
Input size: 1 × 3 × 640 × 640
Output shape (ONNX): 1 × 10 × 8400
- 4 channels → bounding box (x, y, w, h)
- 6 channels → class logits
Number of classes: 6

2. 🏷️ Classes & Dataset Distribution

Class List with Image Counts

ID	Class Name	Image Count
0	Gloves	2,693
1	Vest	4,418
2	goggles	1,431
3	helmet	2,703
4	mask	2,763
5	safety_shoe	2,006

3. 🎯 Intended Use

Primary use-cases

Real-time PPE compliance monitoring
Construction site safety analytics
Industrial / warehouse safety automation
Worker equipment detection on:
- Helmets
- Safety shoes
- Reflective vests
- Gloves
- Masks
- Goggles

Target Platforms

NVIDIA Jetson (Orin Nano, Orin NX, Xavier NX)
ONNX Runtime
TensorRT (FP32 / FP16 / INT8)
NVIDIA DeepStream SDK
Python (Ultralytics inference)

Not intended for

Legal enforcement without human review
Medical or clinical decisions
Surveillance without consent
Environments restricted by privacy regulations

⚠️ Human supervision is mandatory for high-risk applications.

4. 📂 Training Data

Dataset source: Custom PPE dataset (Roboflow format)
Annotation format: YOLO TXT
Data types: Indoor/outdoor industrial environments
Split:
- Train: majority
- Val: held-out validation
- Test: separate set for unbiased evaluation

Dataset is slightly imbalanced:

Vest is most common
safety_shoe & goggles have fewer samples

5. ⚙️ Training Procedure

Base model: yolov8n.pt (COCO pretrained)
Framework: Ultralytics YOLOv8
Hyperparameters:
- epochs = 50
- imgsz = 640
- batch = 32
- optimizer = auto
Augmentations:
- Mosaic
- HSV color shift
- Random flip
- Scaling
- Translation

6. 📊 Evaluation & Metrics

Overall Performance

Metric	Value
mAP@50	~0.81
mAP@50–95	~0.53
Precision	~0.80
Recall	~0.74

Per-Class mAP@50

Class	mAP@50
Gloves	~0.69
Vest	~0.90
goggles	~0.90
helmet	~0.90
mask	~0.80
safety_shoe	~0.64

7. ⚠️ Limitations & Ethical Considerations

Technical

Sensitive to low-light & motion blur
Small distant objects can reduce accuracy
PPE variations (colors, styles, regional differences) may affect performance

Bias & Data Risks

Dataset may not represent all ethnicities, industries, or regions globally
Model may fail on unseen uniform types or PPE color variations

Responsible AI Guidance

Always include human review
Follow local privacy laws (GDPR, workplace regulations)
Use only for transparent, ethical monitoring

8. 🚀 How to Use

A) Inference with Ultralytics (.pt)

python

from ultralytics import YOLO

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

results[0].show()  # visualize

Nexus3/yolov8n-ppe-detection-6classes