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/.