The following dependecies should be installed:

• Python 3.7.3
• CUDA 10.1
• cuddn 7.5.1
• nvidia drivers v.430.40

• Download and unzip competition data

To get iamge data, run a script below:

python download_images.py

Alternatively, you can download from cloud.
Instruction is here

• To convert to png images, run a script below:

python convert_to_png.py

1. train models from scratch

   a) trains all models from scratch using 5 folds cross validation.

   b) expect this to run for about 17.5 hours(if epoch size is 40) on a single RTX2080Ti

2. make prediction

   a) uses weights from the final stage models to make prediction.

   b) uses 5 different weights to create 5 csv files and ensemble them.

Commands to run each build are presented below:

• creates model weights in ../weights
• creates ../output/v{version}/Unet_{encoder_name}_v{version}_fold{fold}.csv for each fold.
• creates ../output/v{version}/ensemble/Unet_{encoder_name}_v{version}_average.csv

./stage_1.sh

• creates model weights in ../weights

./train.sh

• predict using weights which is generated by specified version.
• creates ../output/v{version}/Unet_{encoder_name}_v{version}_fold{fold}.csv for each fold.
• creates ../output/v{version}/ensemble/Unet_{encoder_name}_v{version}_average.csv

./predict.sh

You can visualize your training process by:

tensorboard --logdir="{work_dir}/logs/v{version}/{fold}"

• combine stage 1 train and test set for retraining
• place stage 2 test set
• run script to covert dcm to png images(define the input path and output path like below)

python3 convert_to_png.py \
--test_path /home/rick/siim_data/siim-original/dicom-images-test/ \
--test_out_path /home/rick/siim_data/stage2/test_png/ \
--test_only True

• set stage 2 train image path Argument is "train_image_path'
  
• set stage 2 test set path in ./predict.py. Argument is "stage2_path".
  The path should be same as what you defined in 'convert_to_png.py'

./stage_2.sh