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)

| Metric | Value | |---------------|---------| | Precision | 0.9893 | | Recall | 0.9508 | | mAP@50 | 0.9813 | | mAP@50-95 | 0.7260 |

> 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):

📜 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