predict_train.sh

CUDA_VISIBLE_DEVICES='0,1' python predict_multithreaded.py \
  --gpu=0,1\
  --weights=weights/mobilenet-loss-bce_dice-fold_3-10240.000010-23-0.0035732-0.9965180-99.6974207.h5\
  --test_data_dir=input/train\
  --pred_mask_dir=predicted_masks/mobilenet_val_from_fold_3\
  --fold=3\
  --folds_source=folds.csv\
  --dataset_dir=input\
  --network='mobilenet'\
  --preprocessing_function='tf'\
  --predict_on_val=True
CUDA_VISIBLE_DEVICES='0,1' python predict_multithreaded.py \
  --gpu=0,1\
  --weights=weights/mobilenet-loss-bce_dice-fold_0-10240.000010-1-0.0034889-0.9967070-99.7011537.h5\
  --test_data_dir=input/train\
  --pred_mask_dir=predicted_masks/mobilenet_val_from_fold_0\
  --fold=0\
  --folds_source=folds.csv\
  --dataset_dir=input\
  --network='mobilenet'\
  --preprocessing_function='tf'\
  --predict_on_val=True
CUDA_VISIBLE_DEVICES='0' python train.py\
  --gpu=0\
  --seed=80\
  --fold=0\
  --n_folds=5\
  --dataset_dir='input'\
  --network='simple_unet'\
  --preprocessing_function='tf'\
  --learning_rate=0.001\
  --loss_function='bce_dice'\
  --folds_source='folds.csv'\
  --train_data_dir_name='train'\
  --val_data_dir_name='train'\
  --val_mask_dir='gif_train_masks'\
  --train_mask_dir='gif_train_masks'\
  --stacked_channels_dir='predicted_masks/ensemble_mobilenet_and_resnet'\
  --stacked_channels=10\
  --net_alias='stacked_mobnet_and_resnet'