yolov11 license plate detection

Providermorsetechlab
Categoryobject-detection
Licenseagpl-3.0
Downloads70.8K
Stars0

Overview

YOLOv11 for license plate detection is a specialized object detection model optimized for high-precision vehicle identification. For developers, this means a streamlined pipeline for extracting registration data from diverse traffic environments. It leverages the latest YOLO architectural improvements to balance inference speed with localization accuracy, making it suitable for both edge deployment on embedded cameras and scalable cloud-based processing. Unlike general-purpose detection models, this version is tuned specifically for the aspect ratios and visual features of license plates, reducing false positives in complex urban backgrounds. Integration is straightforward for those familiar with the Ultralytics ecosystem, allowing for rapid deployment via Python or C++ wrappers.

Highlights

  • High-precision localization for vehicle registration plates
  • Optimized for real-time edge and cloud inference
  • Seamless integration with standard YOLOv11 workflows
  • Reduced false positives in complex urban environments
  • Distributed under the flexible AGPL-3.0 license

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("morsetechlab/yolov11-license-plate-detection")
tokenizer = AutoTokenizer.from_pretrained("morsetechlab/yolov11-license-plate-detection")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download morsetechlab/yolov11-license-plate-detection

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download morsetechlab/yolov11-license-plate-detection config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('morsetechlab/yolov11-license-plate-detection')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/morsetechlab/yolov11-license-plate-detection

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/morsetechlab/yolov11-license-plate-detection

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('morsetechlab/yolov11-license-plate-detection')
tokenizer = AutoTokenizer.from_pretrained('morsetechlab/yolov11-license-plate-detection')

Full Documentation

来源: HuggingFace

---
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)

⚠️ Important Notice: Dataset Contamination

The upstream Roboflow dataset (license-plate-recognition-rxg4e) contains train/test contamination — the same source images appear in both the training and test splits with only minor manual augmentation applied (see Discussion #2 for concrete examples). As a result:

  • The reported metrics below are likely overestimated, because the test set is not a true held-out evaluation.
  • Real-world generalization performance is expected to be lower than the numbers in the table.
  • Treat all evaluation figures with caution and validate the model on your own held-out data before production use.

A clean re-split with perceptual-hash deduplication, group-aware splitting, and a re-trained v2 release with honest metrics is planned. See Roadmap below.

🚀 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)

> ⚠️ These metrics are computed on a contaminated test split (see notice above) and should not be interpreted as a reliable measure of generalization.

| 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

🐛 Known Limitations

  • Train/test leakage in upstream dataset — see notice above. Metrics are inflated.
  • Fixed 640×640 inference resizes large images — small or distant plates in high-resolution inputs (e.g. 1200×2400) may be missed. Workarounds: use a larger imgsz (e.g. 1280 or 1600), rectangular inference, or tile-based inference with SAHI. See Discussion #1.
  • Trained primarily on automotive license plates; performance on motorcycles, non-Latin scripts, or unusual plate formats is not guaranteed.

🗺️ Roadmap (v2)

1. Deduplicate the source dataset with perceptual hashing (pHash / dHash) to identify near-duplicate and augmented-variant pairs.
2. Re-split with group-aware logic so augmented variants of the same source image stay in the same fold.
3. Retrain across all model sizes and publish honest evaluation metrics.
4. Add an independent external test set for a more realistic generalization signal.

Contributions, cleaner datasets, or external benchmark suggestions are welcome via Discussions.

📦 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

View official source
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