README.md

---
library_name: transformers
tags: [number plate detection, object detection, OCR, fine-tuned]
---

# 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:

```python
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

```bibtex
@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](your_repo_link).

## 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! 🎯