DavidDrill69/yolov11-license-plate-detection

YOLOv11-License-Plate Detection

This is a fine-tuned version of YOLOv11 (n, s, m, l, x) specialized for License Plate Detection, using a public dataset from Roboflow Universe: License Plate Recognition Dataset (10,125 images)

🚀 Use Cases

• —Smart Parking Systems
• —Tollgate / Access Control Automation
• —Traffic Surveillance & Enforcement
• —ALPR with OCR Integration

🏋️ Training Details

• —Base Model: YOLOv11 (n, s, m, l, x)
• —Training Epochs: 300
• —Input Size: 640x640
• —Optimizer: SGD (Ultralytics default)
• —Device: NVIDIA A100
• —Data Format: YOLOv5-compatible (images + labels in txt)

📊 Evaluation Metrics (YOLOv11x)

For full table across models (n to x), please see the README

📦 Model Variants

• —PyTorch (.pt) — for use with Ultralytics CLI and Python API
• —ONNX (.onnx) — for cross-platform inference

🧠 How to Use

With Python (Ultralytics API):

from ultralytics import YOLO
model = YOLO('yolov11x-license-plate.pt')
results = model.predict(source='image.jpg')

📜 License

• —Base Model (YOLOv11): AGPLv3 by Ultralytics
• —Dataset: CC BY 4.0 by Roboflow Universe
• —This model: AGPLv3 (due to YOLOv11 license inheritance)

✅ License Compliance Reminder

In accordance with the AGPLv3 license:

• —If you use this model in a service or project, you must open source the code that uses it.
• —Please give proper attribution to Roboflow, Ultralytics, and MorseTechLab when using or deploying.

For license details, refer to GNU AGPLv3 License