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--- |
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license: apache-2.0 |
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datasets: |
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- Mels22/SigDetectVerifyFlow |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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base_model: |
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- Ultralytics/YOLO11 |
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tags: |
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- Signature |
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- Detection |
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- Verification |
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--- |
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# Signature Detection and Verification |
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This repository provides two models as part of a full signature authentication pipeline: |
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1. **Detection Model (YOLOv11s-based)**: |
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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. |
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2. **Verification Model (Siamese CNN)**: |
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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. |
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These models are designed to work together in a real-world flow: |
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→ detect signature regions from documents → crop a specific query signature → compare it to a reference sample using the verification model. |
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**Developed by**: [@Mels22](https://huggingface.co/Mels22) and [@JoeCao](https://huggingface.co/JoeCao) |
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## Model Architecture |
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### Detection Model |
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- *Base architecture*: YOLO11s |
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- *Trained on*: [SignverOD: A Dataset Signature Object Detection](https://www.kaggle.com/datasets/victordibia/signverod) |
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- *Fine-tuned on*: [Mels22/SigDetectVerifyFlow](https://huggingface.co/datasets/Mels22/SigDetectVerifyFlow) (1 class: 'signature') |
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### Verification Model |
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- *Architecture*: Convolution Siamese Network |
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- *Loss function*: Contrastive Loss |
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- *Trained on*: [Mels22/SigDetectVerifyFlow](https://huggingface.co/datasets/Mels22/SigDetectVerifyFlow) |
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### 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)**. |
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## Training hyperparameters and Results |
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### Detection Model |
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The following hyperparameters were used during training: |
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- Epochs: 50 |
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- Optimizer: AdamW |
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- Batch size: 16 |
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- Image size: 768 |
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Results: |
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- Precision: 0.9025 |
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- Recall: 0.7934 |
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- mAP@50: 0.8222 |
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- mAP@50-95: 0.4771 |
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### Verification Model |
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The following hyperparameters were used during training: |
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- Epochs: 15 |
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- Optimizer: AdamW |
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- Batch size: 32 |
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- Image size: 105x105 |
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- Learning rate: 1e-4 |
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- Embedding size: 256 |
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Results: |
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- Accuracy: 100% |
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## Testing the Full Pipeline |
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We evaluated the end-to-end performance by integrating both the detection and verification models into a complete flow. |
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- Detection metrics remain consistent with individual evaluation. |
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- End-to-end accuracy (detection + verification): 0.5743 |
