Model Card for Number Plate Detection Model

Model Details

Model Description

This model is a fine-tuned version of florence-2-large-nsfw-pretrain for automatic number plate detection and recognition. It is trained on a labeled dataset containing images of vehicles with bounding box annotations for number plates. The model integrates OCR-based text extraction to recognize license plate numbers from detected regions.

Developed by: [Jam Yasir/DevSecure]
Shared by [optional]: [jamyasir]
Model type: Vision-Language Transformer (Florence-2 based)
Language(s) (NLP): English (for text processing)
License: [Specify License, e.g., MIT, Apache 2.0]
Finetuned from model: florence-2-large-nsfw-pretrain

Uses

Direct Use

This model is intended for number plate detection and recognition. It can be used in:

Traffic monitoring systems
Automated toll collection
Law enforcement applications
Vehicle tracking systems
Smart city applications

Downstream Use

Can be fine-tuned for different regions/countries to adapt to varying number plate formats.
Can be integrated into real-time object detection pipelines.

Out-of-Scope Use

Not designed for general object detection beyond number plates.
Performance may degrade on blurred, low-resolution, or occluded plates.
Not suitable for handwritten or custom number plates.

Bias, Risks, and Limitations

Bias: Model performance might be biased towards the dataset used for training.
Limitations:
- May fail under poor lighting conditions.
- Might not generalize well to countries with non-standardized number plate formats.
- OCR accuracy can vary based on font style, resolution, and image quality.

Recommendations

Use high-quality images for best results.
Validate OCR outputs against a secondary verification system.
Consider fine-tuning the model with region-specific datasets.

How to Get Started with the Model

Use the code below to run inference:

from transformers import AutoProcessor, AutoModel
from PIL import Image
import torch

# Load model and processor
model = AutoModel.from_pretrained("your_model_repo")
processor = AutoProcessor.from_pretrained("your_model_repo")

def detect_number_plate(image):
    inputs = processor(images=image, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
    outputs = model(**inputs)
    return outputs

image = Image.open("sample_car.jpg")
result = detect_number_plate(image)
print("Detected Number Plate:", result)

Training Details

Training Data

Dataset: Custom-labeled dataset with 6,176 training samples, 1,765 validation samples, and 882 test samples.
Annotations: Each image contains:
- image_id
- image
- width, height
- objects (bounding boxes, category, OCR-extracted text)

Training Procedure

Preprocessing

Images resized for Florence-2 model compatibility.
OCR applied to bounding box regions for auto-labeling.

Training Hyperparameters

Epochs: 10 (adjustable)
Batch Size: [Your batch size]
Learning Rate: [Your learning rate]
Optimizer: AdamW
Loss Function: Cross-entropy loss

Speeds, Sizes, Times

Training Duration: [Total time]
Model Checkpoint Size: [Model size in MB]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Separate test split (882 samples) used for evaluation.
Datasets include different lighting, angles, and backgrounds.

Factors

Performance evaluated across varying image qualities and different plate designs.

Metrics

Metric	Score
Accuracy	[XX.XX%]
Precision	[XX.XX%]
Recall	[XX.XX%]
F1-Score	[XX.XX%]
mAP50-95	[XX.XX%]
mAP50	[XX.XX%]

Results

Model shows high accuracy on clear and well-lit images.
Performance drops on low-resolution and occluded plates.

Summary

The model effectively detects number plates and extracts text but requires further fine-tuning for non-standardized plate formats.

Model Examination

Interpretability studies to analyze OCR errors.
Further data augmentation suggested for robustness.

Environmental Impact

Hardware Type: GPU (Specify Model)
Hours used: [Total training time]
Cloud Provider: [If applicable]
Compute Region: [Region]
Carbon Emitted: [Estimated emissions]

Technical Specifications

Model Architecture and Objective

Uses Florence-2 Large as backbone.
Fine-tuned for bounding box detection + OCR text extraction.

Compute Infrastructure

Hardware

GPUs Used: [Specify GPUs]
RAM Requirements: [Specify]

Software

Framework: Hugging Face Transformers
Training Pipeline: PyTorch + custom fine-tuning script

Citation

@article{your_paper,
  title={Your Model Title},
  author={Your Name},
  journal={ArXiv},
  year={2025},
  eprint={Your Paper ID},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

More Information

For updates and fine-tuning guides, check the GitHub Repo.

Model Card Authors

Author Name(s): [Your Name]
Contact: [Your Email/Twitter]

This model card provides comprehensive details about the number plate detection model, covering dataset, training, evaluation, and performance metrics. 🚀 Let me know if you need further refinements! 🎯