devxyasir/florence-finetuned-license-plate-detection

Number Plate Detection & Recognition Model (Florence-2 Fine-Tuned)

Model Overview

This model is a fine-tuned version of Microsoft's Florence-2 Large (florence2-base-ft) adapted for automatic number plate detection and recognition. It processes vehicle images to localize number plates with bounding boxes and applies OCR to extract the license plate text, enabling high-accuracy license plate reading.

The model leverages transformer-based vision-language architectures and is trained on a custom dataset of vehicle images with annotated license plates.

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Uses

Intended Use Cases

• Real-time traffic monitoring and control systems
• Automated toll collection and parking management
• Law enforcement for vehicle identification
• Smart city infrastructure and vehicle tracking solutions

Potential Downstream Applications

• Region-specific fine-tuning to handle various license plate formats worldwide
• Integration with object detection pipelines for multi-object recognition tasks
• Use in embedded devices with GPU acceleration for rapid inference

Limitations & Out-of-Scope Uses

• Not optimized for general object detection beyond license plates
• Performance may degrade with poor lighting, motion blur, or occluded plates
• Does not reliably recognize handwritten or decorative/customized plates
• Model accuracy is affected by the quality and diversity of training data

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Dataset Information

• Dataset source: Custom-labeled dataset with 6,176 training, 1,765 validation, and 882 test images
• Annotations: Each sample includes image metadata, bounding boxes for license plates, and OCR-extracted text labels
• Data diversity: Various lighting conditions, vehicle angles, and plate styles
• Preprocessing: Images resized and normalized to match Florence-2 input requirements; bounding boxes used to isolate plate regions

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Training Details

• Base model: florence2-base-ft
• Fine-tuning: Combined bounding box detection with OCR text extraction
• Hyperparameters:
  • Epochs: 10 (configurable)
  • Optimizer: AdamW
  • Loss: Cross-entropy
  • Batch size & learning rate: Adjusted per hardware capability
• Hardware: GPU-accelerated training (specify GPU model)
• Training duration: 6 hrs (Colab GPU)
• Model size: 1.08GB

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Evaluation

Evaluation Notes

• High accuracy on clear, high-quality images
• Performance declines on low-resolution, occluded, or angled plates
• Future work: augment dataset for robustness and support non-standard plates

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Usage

Load and run inference with the model as follows:

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

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "devxyasir/florence-finetuned-license-plate-detection"
processor = AutoProcessor.from_pretrained(model_name)
model = AutoModelForObjectDetection.from_pretrained(model_name).to(device)

def detect_number_plate(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = processor(images=image, return_tensors="pt").to(device)
    outputs = model(**inputs)
    # Process outputs (bounding boxes, scores, OCR text) as needed
    return outputs

result = detect_number_plate("path/to/car_image.jpg")
print(result)

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Model Limitations and Bias

• Model may favor license plate styles prevalent in the training dataset
• Not guaranteed to perform equally across all geographic regions
• Sensitive to image quality and environmental factors
• Bias can be mitigated by expanding training datasets and applying data augmentation

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Environmental Impact

• Training performed on [GPU model] over [total training hours]
• Estimated carbon footprint: [Insert estimate if available]
• Recommendations for future improvements include model pruning and mixed-precision training

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Citation

If you use this model, please cite:

@article{your_paper_2025,
  title={Fine-tuning Florence-2 for License Plate Detection and Recognition},
  author={Muhammad Yasir},
  year={2024}
}

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Authors & Contact

Muhammad Yasir AI/ML Engineer | Web & Security Developer 📧 [email protected] 🌐 Portfolio 🤖 Hugging Face 💻 GitHub

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For further questions, please open an issue or contact the author directly.

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