Code for paper 'Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation' early accepted by MICCAI 2019.
This is a PyTorch(1.0.1.post2) implementation of BEAL. The code was tested with Anaconda and Python 3.7.1.
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
After installing the dependency:
pip install pyyaml
pip install pytz
pip install tensorboardX==1.4 matplotlib pillow
pip install tqdm
conda install scipy==1.1.0
conda install -c conda-forge opencv
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Clone the repo:
git clone https://github.com/EmmaW8/BEAL.git cd BEAL
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Install dependencies:
For PyTorch dependency, see pytorch.org for more details.
For custom dependencies:
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Configure your dataset path in train.py with parameter '--data-dir'.
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You can train deeplab v3+ using mobilenetv2 or others as backbone.
To train it, please do:
python train.py -g 0 --data-dir /data/ssd/public/sjwang/fundus_data/domain_adaptation --batch-size 8 --datasetT RIM-ONE_r3
To test it, please do: Download the weights can put them into the log folder from link.
python test.py --model-file ./logs/DGS_weights.tar --dataset Drishti-GS