YOLOv8s Signature Detector
| Property | Value |
|---|---|
| License | AGPL-3.0 (Model), Apache 2.0 (Code) |
| Framework | YOLOv8s |
| Dataset Size | 2,819 images |
| Performance | mAP50: 94.50%, mAP50-95: 67.35% |
| Inference Time | CPU: 171.56ms, GPU(T4): 7.657ms |
What is yolov8s-signature-detector?
The yolov8s-signature-detector is a specialized computer vision model designed to detect handwritten signatures in document images. Built on the YOLOv8s architecture, it has been fine-tuned using a comprehensive dataset of 2,819 document images, achieving exceptional precision and recall rates for signature detection tasks.
Implementation Details
The model was developed through a rigorous process of architecture selection and hyperparameter optimization. Training was conducted on a Google Cloud Platform instance with NVIDIA Tesla T4 GPU, utilizing Python 3.10.12 and PyTorch 2.5.1. The training dataset was split into 70% training, 15% validation, and 15% testing sets, with images processed at 640x640 resolution.
- Extensive architecture comparison across 21 different models including YOLO variants, DETR, and RT-DETR
- Hyperparameter tuning using Optuna with 20 optimization trials
- Implementation support for multiple inference frameworks including ONNX Runtime and TensorRT
Core Capabilities
- High-precision signature detection with 94.74% precision and 89.72% recall
- Fast inference times: 171.56ms on CPU and 7.657ms on GPU (T4)
- Robust performance across various document types and signature styles
- Easy deployment options via Python API, CLI, or Triton Inference Server
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized balance between accuracy and speed, achieving over 94% precision while maintaining practical inference times. The extensive comparison across 21 architectures and careful hyperparameter tuning makes it particularly robust for real-world signature detection tasks.
Q: What are the recommended use cases?
The model is ideal for document processing systems, automated signature verification, digital document management, and any application requiring reliable signature detection in scanned documents. It can be deployed in both CPU and GPU environments, making it versatile for different infrastructure requirements.
