YOLOv5m License Plate Detection Model
| Property | Value |
|---|---|
| Author | keremberke |
| Framework | PyTorch |
| Accuracy | 98.83% mAP@0.5 |
| Downloads | 11,973 |
What is yolov5m-license-plate?
The yolov5m-license-plate is a specialized object detection model built on the YOLOv5 architecture, specifically trained for identifying and localizing license plates in images. With an impressive mAP@0.5 of 98.83%, it offers robust performance for automotive and surveillance applications.
Implementation Details
Built using YOLOv5 version 7.0.6, this model implements sophisticated object detection capabilities with configurable parameters for confidence thresholds, IoU thresholds, and maximum detection limits. The model supports both standard inference and test-time augmentation for enhanced accuracy.
- Configurable confidence threshold (default: 0.25)
- Adjustable IoU threshold (default: 0.45)
- Support for class-agnostic NMS
- Maximum detection limit of 1000 objects per image
Core Capabilities
- Real-time license plate detection
- Support for both single image and batch processing
- Custom training capability on user datasets
- Test-time augmentation for improved accuracy
- Flexible output formats with bounding box coordinates and confidence scores
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on license plate detection with near-perfect accuracy and its easy integration capabilities through the YOLOv5 framework. It's particularly valuable for automated surveillance and traffic monitoring systems.
Q: What are the recommended use cases?
The model is ideal for traffic monitoring systems, parking management solutions, toll collection systems, and any application requiring automated license plate detection. It can be easily integrated into existing systems or fine-tuned for specific use cases.
