yolov8n-ppe-detection-6classes

🦺 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.

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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

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2. 🏷️ Classes & Dataset Distribution

Class List with Image Counts

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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.

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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

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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

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6. 📊 Evaluation & Metrics

Overall Performance

Per-Class mAP@50

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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

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8. 🚀 How to Use

A) Inference with Ultralytics (.pt)

from ultralytics import YOLO

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

results[0].show()  # visualize