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SPASK

ECE271B project

PyTorch implementation for self-supervised part segmentation via keypoint constraints - Application to histopathology study in cancer detection.

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Installation

The code is developed based on Pytorch v1.8+ with TensorboardX as visualization tools. We recommend to use conda env to run the code:

$ conda env create python3 -n spask_env
$ source spask_env/bin/activate
(spask_env)$ pip install -r requirements.txt

To deactivate the virtual environment, run $conda deactivate. To activate the environment again, run $ conda activate spask_env.

$ ./download_CelebA.sh

Download CelebA unaligned from here.

Train the model

$ CUDA_VISIBLE_DEVICES={GPU} python train.py -f exps/SPASK_K8_train_geometric.json where {GPU} is the GPU device number.

CAMELYON16 Dataset

The code for experiments on Camelyon16 dataset is in cam16 folder

References

Code is largely based on SCOPS paper

supplementary

License

Apache 2.0 license

spask's People

Contributors

deepaksridhar avatar

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