---
{}
---

# Transfer learning using Hybrid Semantic Change Detection Data


We provide the weights used in the experiments of our CVPR'25 paper [The Change You Want To Detect](https://arxiv.org/abs/2503.15683).

## Dual U-Net

The model is a relatively simple Dual U-Net composed of two nearly identical parallel U-Nets. One responsible for semantic segmentation, the other for binary change detection. Besides using skip connections in each U-Net, extracted features from the "semantic encoder" are also transmitted to the "change detection decoder".

Both images are sequentially and independently passed through the "semantic U-Net", that produce a semantic map for each image, and extracted features are stored.
Then images are concatenated, passed through the "change detection U-Net" while injecting the previously stored features and a binary change map is produced.

The backbones are ResNet-50 pretrained on ImageNet.

## Model checkpoint

Here we provide the weights for our Dual U-Net that have been obtained after a pre-training on our Hybrid Semantic Change Dataset **FSC-180k**. They can be used for fine-tuning or inference on real change dataset using [our code](https://github.com/yb23/HySCDG) and specifically passing the .ckpt file as `--run_id` variable.