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):
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