devxyasir/florence-finetuned-license-plate-detection

Absolutely, Yasir! Here's an enhanced, professional, and detailed Hugging Face model card README for your Number Plate Detection & Recognition model, incorporating all key info you provided — polished and ready to publish on the Hugging Face Hub:

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---
library_name: transformers
tags:
  - number plate detection
  - object detection
  - OCR
  - fine-tuned
  - vision-language
license: mit
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# Number Plate Detection & Recognition Model (Florence-2 Fine-Tuned)

## Model Overview

This model is a fine-tuned version of **Microsoft's Florence-2 Large** (`florence-2-large-nsfw-pretrain`) 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:** `florence-2-large-nsfw-pretrain`  
- **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:** [Insert total time]  
- **Model size:** [Insert size in MB]  

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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:

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

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

model_name = "your-hf-username/number-plate-florence2"
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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