Update README.md

README.md

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----
-license: mit
----
+---
+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