Code for 2018 data science bowl, using UNet and soft dice loss.
Because of my poor laptop, i haven't run the project so there may be many bugs.
FPN and other model will be implement, i'll run this project after next term begins.
data
├── stage1_sample_submission.csv
├── stage1_test
├── stage1_train
└── stage1_train_labels.csv
| size | num |
|---|---|
| (256, 256, 3) | 334 |
| (256, 320, 3) | 112 |
| (520, 696, 3) | 92 |
| (360, 360, 3) | 91 |
| (1024, 1024, 3) | 16 |
| (512, 640, 3) | 13 |
| (603, 1272, 3) | 6 |
| (260, 347, 3) | 5 |
| (1040, 1388, 3) | 1 |
Kaggle18
├── config.py
├── data
│ ├── dataset.py
│ ├── __init__.py
│ └── Resize.py
├── main.py
├── model
│ ├── BasicModule.py
│ ├── checkpoints
│ ├── __init__.py
│ └── UNet.py
├── README.md
└── utils
├── __init__.py
├── Loss.py
├── saved_loss.csv
├── saved_lr.csv
└── util.py
thanks to Andrea for overview of data.
thanks to Yun Chen for data preprocess and net model.
thanks to Stephen Bailey for run-length encoding algorithm.