metadata

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)

🚀 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