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--- |
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language: en |
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license: agpl-3.0 |
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tags: |
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- computer-vision |
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- object-detection |
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- license-plate |
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- yolov11 |
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- ultralytics |
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- finetuned |
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datasets: |
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- roboflow/license-plate-recognition-rxg4e |
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metrics: |
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- precision |
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- recall |
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- mAP@50 |
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- mAP@50-95 |
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--- |
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# YOLOv11-License-Plate Detection |
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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: |
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[License Plate Recognition Dataset (10,125 images)](https://universe.roboflow.com/roboflow-universe-projects/license-plate-recognition-rxg4e/dataset/11) |
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## 🚀 Use Cases |
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- Smart Parking Systems |
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- Tollgate / Access Control Automation |
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- Traffic Surveillance & Enforcement |
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- ALPR with OCR Integration |
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## 🏋️ Training Details |
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- Base Model: YOLOv11 (`n`, `s`, `m`, `l`, `x`) |
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- Training Epochs: 300 |
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- Input Size: 640x640 |
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- Optimizer: SGD (Ultralytics default) |
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- Device: NVIDIA A100 |
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- Data Format: YOLOv5-compatible (images + labels in txt) |
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## 📊 Evaluation Metrics (YOLOv11x) |
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| Metric | Value | |
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|---------------|---------| |
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| Precision | 0.9893 | |
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| Recall | 0.9508 | |
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| mAP@50 | 0.9813 | |
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| mAP@50-95 | 0.7260 | |
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> For full table across models (n to x), please see the [README](README.md) |
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## 📦 Model Variants |
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- PyTorch (.pt) — for use with Ultralytics CLI and Python API |
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- ONNX (.onnx) — for cross-platform inference |
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## 🧠 How to Use |
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With Python (Ultralytics API): |
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```python |
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from ultralytics import YOLO |
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model = YOLO('yolov11x-license-plate.pt') |
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results = model.predict(source='image.jpg') |
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``` |
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## 📜 License |
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- Base Model (YOLOv11): AGPLv3 by [Ultralytics](https://github.com/ultralytics/ultralytics) |
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- Dataset: CC BY 4.0 by Roboflow Universe |
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- This model: AGPLv3 (due to YOLOv11 license inheritance) |
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## ✅ License Compliance Reminder |
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In accordance with the AGPLv3 license: |
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- If you **use this model** in a service or project, you must **open source** the code that uses it. |
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- Please give proper attribution to Roboflow, Ultralytics, and MorseTechLab when using or deploying. |
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For license details, refer to [GNU AGPLv3 License](https://www.gnu.org/licenses/agpl-3.0.en.html) |
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