HurricaneOD - Thai License Plate Detector 🚗🔍

Fast and Accurate Thai License Plate Detection powered by YOLOv8

Try Demo • Report Issue • Request Feature

📋 Model Description

HurricaneOD (Hurricane Object Detector) is a high-performance YOLOv8-based object detection model specifically fine-tuned for detecting Thai license plates in vehicle images. This model is optimized for:

🚗 Vehicle Detection: Cars, motorcycles, trucks, buses
🔍 License Plate Localization: Accurate bounding box detection
⚡ Real-time Processing: Fast inference (< 50ms per image)
📱 Edge Deployment: Lightweight nano model (6MB)

Key Features

Feature	Description
🎯 Accuracy	Optimized for Thai license plates
⚡ Speed	Real-time detection (~50ms/image)
💾 Size	Lightweight (6MB)
🌐 Platform	CPU & GPU support
🔧 Easy to Use	Simple API with Ultralytics

🚀 Quick Start

Installation

pip install ultralytics huggingface_hub pillow

Download Model

from huggingface_hub import hf_hub_download

# Download model weights
model_path = hf_hub_download(
    repo_id="Rattatammanoon/hurricaneod-thai-plate-detector",
    filename="HurricaneOD_beta.pt"
)

print(f"Model downloaded to: {model_path}")

Basic Usage

from ultralytics import YOLO
from PIL import Image

# Load model
model = YOLO(model_path)

# Detect license plates in image
results = model.predict(
    "car_image.jpg",
    conf=0.25,      # Confidence threshold
    iou=0.45,       # IoU threshold for NMS
    verbose=False
)

# Process results
for result in results:
    boxes = result.boxes
    for box in boxes:
        # Get bounding box coordinates
        coords = box.xyxy[0].tolist()
        x1, y1, x2, y2 = coords
        confidence = box.conf[0].item()
        class_id = box.cls[0].item()
        
        print(f"Detected plate: [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}] (conf: {confidence:.2f})")
        
        # Crop license plate region
        img = Image.open("car_image.jpg")
        plate_crop = img.crop((x1, y1, x2, y2))
        plate_crop.save("detected_plate.jpg")

Integration with Hurricane OCR

Combine with Hurricane OCR for complete plate reading:

from ultralytics import YOLO
from transformers import AutoProcessor, AutoModelForVision2Seq
from peft import PeftModel
from PIL import Image
import torch

# 1. Load plate detector (HurricaneOD)
detector = YOLO(model_path)

# 2. Load OCR model (Hurricane OCR)
ocr_processor = AutoProcessor.from_pretrained("scb10x/typhoon-ocr1.5-2b")
ocr_base = AutoModelForVision2Seq.from_pretrained(
    "scb10x/typhoon-ocr1.5-2b",
    torch_dtype=torch.float16,
    device_map="auto"
)
ocr_model = PeftModel.from_pretrained(ocr_base, "Rattatammanoon/hurricane-ocr-v1")
ocr_model.eval()

# 3. Complete pipeline: Detection → OCR
img = Image.open("car_image.jpg")

# Detect plate
results = detector.predict(img, conf=0.25)
if results and len(results[0].boxes) > 0:
    box = results[0].boxes[0]
    coords = box.xyxy[0].tolist()
    x1, y1, x2, y2 = coords
    
    # Crop plate
    plate_crop = img.crop((x1, y1, x2, y2))
    
    # Run OCR
    pixel_values = ocr_processor(images=plate_crop, return_tensors="pt").pixel_values
    with torch.no_grad():
        generated_ids = ocr_model.generate(pixel_values, max_length=512)
    text = ocr_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    print(f"📍 Detected plate at: ({x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f})")
    print(f"📝 OCR Result:\n{text}")

📊 Training Details

Parameter	Value
Base Model	YOLOv8n (Nano)
Training Epochs	100
Batch Size	16
Image Size	640x640
Learning Rate	0.01
Training Date	2025-12-18T09:03:21.059407
Framework	Ultralytics YOLOv8

Model Architecture

Backbone: YOLOv8n CSPDarknet
Neck: PANet (Path Aggregation Network)
Head: YOLOv8 Detection Head
Parameters: ~3.2M
Model Size: ~6MB

💡 Performance Tips

Tip	Description
✅ Confidence Threshold	Use 0.25-0.5 for best balance
✅ Image Size	Input 640x640 for optimal speed/accuracy
✅ Batch Processing	Process multiple images for efficiency
✅ GPU Acceleration	Use CUDA for 10x faster inference
⚠️ Image Quality	Better lighting = better detection

Batch Processing

# Process multiple images efficiently
images = ["car1.jpg", "car2.jpg", "car3.jpg"]
results = model.predict(images, conf=0.25, batch=8)

for i, result in enumerate(results):
    print(f"Image {i+1}: {len(result.boxes)} plates detected")

Video Processing

# Process video stream
results = model.predict(
    source="traffic_video.mp4",
    conf=0.25,
    stream=True,  # Stream results for memory efficiency
    save=True     # Save annotated video
)

for result in results:
    # Process each frame
    boxes = result.boxes
    print(f"Frame: {len(boxes)} plates detected")

🎯 Use Cases

🚗 Parking Management: Automated entry/exit systems
🚦 Traffic Monitoring: Speed cameras, red light detection
🏢 Access Control: Gated communities, corporate parking
📊 Fleet Management: Vehicle tracking and management
🚓 Law Enforcement: Automated plate recognition systems

📈 Benchmark

Device	Inference Time	FPS
NVIDIA RTX 3060	~10ms	~100
CPU (Intel i7)	~50ms	~20
CPU (Apple M1)	~30ms	~33

Measured on 640x640 images

🔧 Advanced Usage

Custom Confidence Threshold

# Adjust confidence for your use case
results = model.predict(
    "image.jpg",
    conf=0.5,       # Higher = fewer false positives
    iou=0.45,       # NMS threshold
    max_det=10      # Maximum detections per image
)

Export to Other Formats

# Export to ONNX for deployment
model.export(format="onnx")

# Export to TensorRT for NVIDIA GPUs
model.export(format="engine")

# Export to CoreML for iOS
model.export(format="coreml")

# Export to TFLite for mobile
model.export(format="tflite")

Training Your Own Model

from ultralytics import YOLO

# Load pretrained model
model = YOLO("Rattatammanoon/hurricaneod-thai-plate-detector")

# Fine-tune on your dataset
results = model.train(
    data="your_data.yaml",
    epochs=100,
    imgsz=640,
    batch=16,
    device=0  # GPU ID
)

⚠️ Limitations

Optimized for Thai license plates (both old and new formats)
Performance may vary with:
- Very small plates (< 50px width)
- Heavily occluded or damaged plates
- Extreme angles or perspectives
- Poor lighting conditions

📄 License

This model is licensed under Apache 2.0. See LICENSE for details.

✅ Commercial use allowed
✅ Modification allowed
✅ Distribution allowed
❗ Must include license and copyright notice

📚 Citation

If you use HurricaneOD in your research or project, please cite:

@misc{hurricaneod-beta-2025,
  author = {HurricaneOD Team},
  title = {HurricaneOD - Thai License Plate Detector},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/Rattatammanoon/hurricaneod-thai-plate-detector}}
}

🙏 Acknowledgments

This project builds upon:

YOLOv8: Ultralytics YOLOv8
Base Model: YOLOv8n pretrained on COCO dataset
Training Framework: Ultralytics YOLO

Special thanks to the Ultralytics team for developing YOLOv8! 🙏

📧 Contact & Support

👨‍💻 GitHub: @topzson
🐛 Issues: Report here
💬 Discussions: HuggingFace Discussions

🔗 Related Models

🔤 Hurricane OCR: Rattatammanoon/hurricane-ocr-v1 - Thai License Plate OCR
🌊 Typhoon OCR: scb10x/typhoon-ocr1.5-2b - Base OCR Model

Made with ❤️ for Thai License Plate Detection

Star ⭐ this model if you find it useful!

🏠 Model Card • 📖 Documentation • 🚀 Try Demo

Downloads last month: 11

Collection including Rattatammanoon/hurricane-od-thai-plate-detector

HURRICANE_OCR_BETA

Collection

2 items • Updated 10 days ago • 1