🚦 Traffic Sign Recognition ML Model (YOLOv8)

🧠 Model Summary

This machine learning model is developed to detect and classify traffic signs using the YOLOv8 (You Only Look Once v8) object detection architecture. Trained on a dataset containing over 30,000 labeled images of traffic signs, this model can accurately identify a wide variety of road signs for use in autonomous driving, smart city solutions, and advanced driver-assistance systems (ADAS).

Architecture: YOLOv8
Task: Object Detection & Multi-class Classification
Dataset: 30,000+ labeled traffic sign images
Fine-tunable: ✅ Yes
License: MIT

🚀 Usage Example

from ultralytics import YOLO

# Load the trained YOLOv8 model
model = YOLO("path/to/best.pt")  # Replace with actual model path

# Run inference on a test image
results = model("path/to/traffic_image.jpg")

# Visualize results
results.show()

# Access predictions
for r in results:
    boxes = r.boxes
    for box in boxes:
        print(f"Class: {int(box.cls[0])}, Confidence: {box.conf[0]:.2f}")