Lanyard Detector - School Safety AI

This model detects whether a person is wearing a lanyard/ID badge around their neck.

🎯 Use Case

Problem: Manual lanyard checking at school entrances is time-consuming.

Solution: Automated computer vision system for real-time lanyard detection.

SDG Impact: SDG 16 (Peace, Justice & Strong Institutions) - School safety

📊 Model Details

Architecture: MobileNetV2 (Transfer Learning)
Input: RGB images (224x224)
Output: Binary classification
- Class 0: has_lanyard
- Class 1: no_lanyard
Framework: PyTorch

🚀 Quick Start

import torch
from torchvision import transforms, models
from PIL import Image

# Load model
checkpoint = torch.load('pytorch_model.pth', map_location='cpu')
model = models.mobilenet_v2()
model.classifier[1] = torch.nn.Linear(1280, 2)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Predict
img = Image.open('test.jpg')
img_tensor = transform(img).unsqueeze(0)

with torch.no_grad():
    output = model(img_tensor)
    probs = torch.softmax(output, dim=1)
    pred_class = output.argmax(1).item()
    
classes = ['has_lanyard', 'no_lanyard']
print(f"Prediction: {classes[pred_class]}")
print(f"Confidence: {probs[0][pred_class]*100:.1f}%")

📝 Training Details

Custom dataset with data augmentation
70% train / 15% val / 15% test split
Adam optimizer (lr=0.001)
15 epochs

⚡ Deployment

Deploy via:

Featherless AI (Hugging Face partner)
ONNX Runtime
TorchScript
Edge devices

⚠️ Limitations

Best with frontal/slight side angles
Requires adequate lighting
May struggle with very small/distant lanyards

📄 License

MIT License

Developed for: Goals in Code Hackathon 2026
SDG: 16 - Peace, Justice & Strong Institutions

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