---
license: apache-2.0
datasets:
- Mels22/SigDetectVerifyFlow
metrics:
- accuracy
- precision
- recall
base_model:
- Ultralytics/YOLO11
tags:
- Signature
- Detection
- Verification
---

# Signature Detection and Verification
This repository provides two models as part of a full signature authentication pipeline:
1. **Detection Model (YOLOv11s-based)**:
   A lightweight object detection model fine-tuned to detect signature regions in scanned documents. The model takes full document images as input and returns bounding boxes of all detected signatures.

2. **Verification Model (Siamese CNN)**:
   A Siamese network trained to determine whether two given signatures (a query signature cropped from a document and a reference signature) belong to the same person. It outputs a binary prediction: 0 = genuine, 1 = forged.

These models are designed to work together in a real-world flow:
→ detect signature regions from documents → crop a specific query signature → compare it to a reference sample using the verification model.

**Developed by**: [@Mels22](https://huggingface.co/Mels22) and [@JoeCao](https://huggingface.co/JoeCao)

## Model Architecture
### Detection Model
- *Base architecture*: YOLO11s
- *Trained on*: [SignverOD: A Dataset Signature Object Detection](https://www.kaggle.com/datasets/victordibia/signverod)
- *Fine-tuned on*: [Mels22/SigDetectVerifyFlow](https://huggingface.co/datasets/Mels22/SigDetectVerifyFlow) (1 class: 'signature')

### Verification Model
- *Architecture*: Convolution Siamese Network
- *Loss function*: Contrastive Loss
- *Trained on*: [Mels22/SigDetectVerifyFlow](https://huggingface.co/datasets/Mels22/SigDetectVerifyFlow)

### For more details on the training process and architecture, please visit our repo Github at **[Signature-Detect-To-Verify](https://github.com/Melios22/Signature-Detect-To-Verify)**.

## Training hyperparameters and Results
### Detection Model
The following hyperparameters were used during training:
- Epochs: 50
- Optimizer: AdamW
- Batch size: 16
- Image size: 768

Results:
- Precision: 0.9025
- Recall: 0.7934
- mAP@50: 0.8222
- mAP@50-95: 0.4771

### Verification Model
The following hyperparameters were used during training:
- Epochs: 15
- Optimizer: AdamW
- Batch size: 32
- Image size: 105x105
- Learning rate: 1e-4
- Embedding size: 256

Results:
- Accuracy: 100%

## Testing the Full Pipeline

We evaluated the end-to-end performance by integrating both the detection and verification models into a complete flow.

- Detection metrics remain consistent with individual evaluation.
- End-to-end accuracy (detection + verification): 0.5743