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language: en
license: mit
tags:
- change-detection
- remote-sensing
- eo-sar
- pytorch
- segmentation
- safetensors
datasets:
- doron333/change-detection-dataset
metrics:
- iou
- precision
- recall
- f1
library_name: generic
---
# Binary Change Detection (EO-SAR Fusion)
This model is a **Siamese UNet** designed for binary change detection using fused Electro-Optical (EO) and Synthetic Aperture Radar (SAR) satellite imagery.
## Model Description
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
- **Loss Function:** Combined Focal Loss + Dice Loss
- **Input Resolution:** 256x256
## Training Results (Final Run)
- **Best Validation IoU:** 24.74%
- **Precision:** 26.45%
- **Recall:** 79.29%
- **F1 Score:** 39.67%
## How to Use
To use this model, ensure you have the `src/` folder from the [GitHub repository](https://github.com/rishii100/Binary-Change-Detection) in your local directory.
```python
import torch
from safetensors.torch import load_file
from src.model import SiameseUNet
# 1. Initialize architecture
model = SiameseUNet(pretrained=False)
# 2. Load professional weights
weights = load_file("model.safetensors")
model.load_state_dict(weights)
# 3. Ready for inference
model.eval()
```
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