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README.md

@@ -7,6 +7,7 @@ tags:
 - eo-sar
 - pytorch
 - segmentation
+- safetensors
 datasets:
 - doron333/change-detection-dataset
 metrics:
@@ -14,6 +15,7 @@ metrics:
 - precision
 - recall
 - f1
+library_name: generic
 ---
 
 # Binary Change Detection (EO-SAR Fusion)
@@ -24,6 +26,7 @@ This model is a **Siamese UNet** designed for binary change detection using fuse
 The architecture uses dual weight-shared **ResNet-34** encoders to extract multi-modal features from pre-event RGB (EO) and post-event grayscale (SAR) images. Feature differences are fused via skip connections into a UNet decoder.
 
 - **Encoder:** ResNet-34 (Pre-trained on ImageNet)
+- **Weights Format:** Safetensors (Modern, fast, and secure)
 - **Loss Function:** Combined Focal Loss + Dice Loss (Optimized for 58:1 class imbalance)
 - **Input Resolution:** 256x256
 
@@ -38,3 +41,12 @@ This model is intended for rapid disaster damage assessment where pre-event opti
 
 ## How to Use
 The model expects a 3-channel EO image (Pre-event) and a 1-channel SAR image (Post-event) as inputs.
+
+```python
+from safetensors.torch import load_file
+# from src.model import SiameseUNet
+
+# model = SiameseUNet()
+# weights = load_file("model.safetensors")
+# model.load_state_dict(weights)
+```