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:
| U-net | LB score |
|---|---|
| 128x128 | 0.990 |
| 256x256 | 0.992 |
| 512x512 | 0.995 |
| 1024x1024 | 0.996 |
- 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.pyto 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.