This solution was based on Heng CherKeng's code for PyTorch. I kindly thank him for sharing his work. 128x128, 256x256, 512x512 and 1024x1024 U-nets are implemented. Public LB scores for each U-net are:

• Added loss with weighted boundary (thanks to lyakaap)

• Added Hue/Saturation/Value augmentation.
• Switched to RMSprop optimizer as default.
• Added multithreaded inference with inference and data loading done on separate threads. This reduced inference time by 40% in my tests. You can run test_submit_multithreaded.py to try it.

• Added 1024x1024 U-net
• Not using predict_generator anymore due to memory constraints with large input

• Using Binary Crossentropy Dice Loss in place of Binary Crossentropy
• Callbacks now use val_dice_loss as a metric in place of val_loss

• Keras 2.0 w/ TF backend
• sklearn
• cv2
• tqdm
• h5py

Place 'train', 'train_masks' and 'test' data folders in the 'input' folder.

Convert training masks to .png format. You can do this with:

mogrify -format png *.gif

in the 'train_masks' data folder.

Run python train.py to train the model.

Run python test_submit.py to make predictions on test data and generate submission.