# Fully Convolutional Siamese Networks for Change Detection This is an unofficial implementation of the paper > Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch. (2018, October). Fully convolutional siamese networks for change detection. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 4063-4067). IEEE. as the [official repo](https://github.com/rcdaudt/fully_convolutional_change_detection) does not provide the training code. [paper link](https://ieeexplore.ieee.org/abstract/document/8451652) # Prerequisites > opencv-python==4.1.1 pytorch==1.2.0 pyyaml==5.1.2 scikit-image==0.15.0 scikit-learn==0.21.3 scipy==1.3.1 tqdm==4.35.0 Tested on Python 3.7.4, Ubuntu 16.04 # Basic Usage ```bash # The network definition scripts are from the original repo git clone --recurse-submodules git@github.com:Bobholamovic/FCN-CD-PyTorch.git cd FCN-CD-PyTorch mkdir exp cd src ``` In `src/constants.py`, change the dataset directories to your own. In `config_base.yaml`, feel free to modify the configurations. For training, try ```bash python train.py train --exp-config ../config_base.yaml ``` For evaluation, try ```bash python train.py val --exp-config ../config_base.yaml --resume path_to_checkpoint ``` You can find the checkpoints in `exp/base/weights/`, the log files in `exp/base/logs`, and the output change maps in `exp/outs`.