materials/article_3D CNNs as solution for inverse EEG problem (Machine Learning 2023 Course.pdf
materials/presentation_3D CNN for inverse EEG problem.pdf
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Load model weights and sample set:
gdown --id 1RlA_DInYxlvsQuDimHUYvtadrlnXrQgx
gdown --id 1T_IpJMqIh78x4rSrQBiFZLz65-0pPk_-
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Run the script for prediction:
mkdir output
python preprocessing\unarchive_h5.py --h5_filename_to_unarchive "dense-162dip_parcell-64_GRID-64_paired_scale-False_DILATION-None-4000.h5"
python training_script.py --model "VNet" --model_name "VNet_trained_CrossEntropy_DILATION-None-4000_epoch-120.pt" --h5_file_path "dense-162dip_parcell-64_GRID-64_paired_scale-False_DILATION-None-4000_unarch.h5" --out_dir "output" --predict_only 1
python plot/visualize.py --model_name "output/vnet_CrossEntropy_prediction.h5"
Download:
gdown --id 1hRmT670aQNItDEGuEaUPvpphw6aDxNZu
Unarchive:
python preprocessing/unarchive_h5.py --filename "dense-162dip_parcell-64_GRID-64_paired_scale-False-2000.h5"
EEG ML Project
├── report # report deliverables
│ ├── presentation.pdf
│ └── report.pdf
├── plot
│ └── visualize.py
├── predictor # contains models schemes and neutron files
│ ├── utils.py #
│ └── buildingblocks.py # realization of UNet architecture on pytorch
├── configs # different run configs
│ ├── config_vnet_DILATION-2-predict.json #
│ ├── config_vnet_DILATION-2.json #
│ ├── config_vnet_DILATION-None-predict.json #
│ └── config_vnet_DILATION-None.json #
├── preprocessing
│ ├── coords_to_voxels.ipynb #
│ ├── data_to_h5_dtype.py #
│ └── unarchive_h5.py # unarchives the dataset in h5 format
├── CustomImageDataset.py #
├── metric_for_dataset.ipynb # calcules the metric of the model preiction
├── models.py #
├── predict_set.py #
├── requirements.txt
└── README.md