YOLOS Small License Plate Detection Model
| Property | Value |
|---|---|
| Model Type | Vision Transformer (ViT) |
| Training Data | 735 annotated license plate images |
| Training Duration | 200 epochs |
| Average Precision | 47.9% (IoU=0.50:0.95) |
| Model Hub | Hugging Face |
What is yolos-small-rego-plates-detection?
This model is a specialized version of the YOLOS (You Only Look at One Sequence) architecture, fine-tuned specifically for detecting vehicles and license plates. Built on the Vision Transformer (ViT) framework and trained using the DETR loss function, it represents a modern approach to object detection that moves away from traditional convolutional architectures.
Implementation Details
The model was developed by fine-tuning the original YOLOS small model, which was pre-trained on ImageNet-1k and COCO 2017. The specialized training involved 735 annotated images with two target categories: vehicles and license plates. The training process ran for 200 epochs using Google Colab's GPU infrastructure.
- Achieves 75.2% AP at IoU=0.50
- Particularly strong performance on large objects with 80.4% AP
- Implements DETR-style training methodology
- Fully compatible with the Transformers library
Core Capabilities
- Dual-class detection (vehicles and license plates)
- Real-time object detection capabilities
- Strong performance on large-scale license plates
- Easy integration with PyTorch workflows
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of the YOLOS architecture with specialized training for license plate detection, offering a modern transformer-based approach to a traditionally CNN-dominated task. Its performance metrics are particularly strong for large objects, making it ideal for clear, front-facing license plate detection scenarios.
Q: What are the recommended use cases?
The model is best suited for automated vehicle and license plate detection in controlled environments, such as parking lots, toll booths, or security checkpoints. It performs optimally when dealing with larger, clearly visible plates, as indicated by its 80.4% AP on large objects.
