{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"* **preprocessing stage 1**: https://www.kaggle.com/code/doyeonkimm/windowing-preprocessing-technique-dataset\n* **stage 1 (multilabel)**: https://www.kaggle.com/code/doyeonkimm/stage-1-window-multilabel\n* **preprocessing stage 2**:\n    1. [preprocess Yolov5] https://www.kaggle.com/code/doyeonkimm/preprocess-cropping-vertebrae-with-yolo\n    1. [train Yolov5] https://www.kaggle.com/code/doyeonkimm/train-infer-cropping-vertebrae-with-yolo\n    1. [Convert obtained Yolov5 output from jpg -> npz files. The output folders utilized in 'Cropping voxel'] https://www.kaggle.com/code/doyeonkimm/convert-to-npz-file-test-icon/notebook\n    1. [Cropping voxel, for processing vert_list.csv] https://www.kaggle.com/code/doyeonkimm/stage2-preprocessing-sagittal-view-labels-only/notebook\n    1. [Cropping voxel] https://www.kaggle.com/code/doyeonkimm/preprocessing-stage-2-cropping-voxel/notebook\n* **stage 2**: https://www.kaggle.com/code/doyeonkimm/stage-2-cracknet-lstm-yolo-window","metadata":{}}]}