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Data Science Bowl 2018
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README.md

README.md

DSB_2018

Data Science Bowl 2018

Lucas U-Net Instructions:

  1. SSH into the server, using your own account
  2. Clone this repository into your folder
  3. source activate dsb (shared conda environment)
  4. cd lucas-u-net
  5. python train.py: this should create a .csv file that you can then submit on Kaggle.

(You might have to create an empty folder called numpy_dumps in "DSB_2018" if it gives you an error about the directory being non-existent. This is where the pre-processed data gets stored for later use.)

The raw data itself is stored at /hdd/datasets/datasciencebowl2018/.

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