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rtree-c

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    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 does not provide the training code.

    paper link

    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

    # 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

    python train.py train --exp-config ../config_base.yaml

    For evaluation, try

    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.