| 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() | |
| ``` | |