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---
language: en
license: agpl-3.0
tags:
- computer-vision
- object-detection
- license-plate
- yolov11
- ultralytics
- finetuned
datasets:
- roboflow/license-plate-recognition-rxg4e
metrics:
- precision
- recall
- mAP@50
- mAP@50-95
---
# 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)](https://universe.roboflow.com/roboflow-universe-projects/license-plate-recognition-rxg4e/dataset/11)
## 🚀 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](README.md)
## 📦 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):
```python
from ultralytics import YOLO
model = YOLO('yolov11x-license-plate.pt')
results = model.predict(source='image.jpg')
```
## 📜 License
- Base Model (YOLOv11): AGPLv3 by [Ultralytics](https://github.com/ultralytics/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](https://www.gnu.org/licenses/agpl-3.0.en.html)
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