metadata
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 in your local directory.
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()