Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks
German Conference on Pattern Recognition (GCPR), 2019
Yauhen Babakhin, Artsiom Sanakoyeu, Hirotoshi Kitamura
https://arxiv.org/abs/1904.04445

Kaggle post about the solution: link.

The solution is available as a Docker container. The following dependecies should be installed:

• Python 3.5.2
• CUDA 9.0
• cuddn 7
• nvidia drivers v.384
• Docker
• nvidia-docker

Download and unzip competition data into data/ directory. One could specify local path to the new test images in SETTINGS.json file (NEW_TEST_IMAGES_DATA field). The competition test data is used by default.

To get the weights from the final stage models download them from google drive and unzip into corresponding bes/weights/ and phalanx/weights directories.

To build and start a docker container run:

cd docker 
./build.sh
./run.sh

1. train models from scratch

   a) trains all models from scratch

   b) expect this to run for about 16 days on a single GTX1080Ti

2. make prediction

   a) uses weights from the final stage models to make predictions

   b) expect this to run for 3.5 hours for 18,000 test images on a single GTX1080Ti

Commands to run each build are presented below:

./train.sh

./predict.sh

1. Model weights are saved in bes/weights and phalanx/weights for b.e.s. and phalanx models respectively

2. Individual model predictions before ensembling are stored in bes/predictions (lots of .png images) and phalanx/predictions (.npy files)

3. Scripts to generate initial folds and jigsaw mosaics are located in bes/datasets: generate_folds.py and Generate_Mosaic.R

If you find this code useful, please cite our paper:

@journal{tgsSaltBodiesSegmentation2019,
  title={Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks},
  author={Babakhin, Yauhen, and Sanakoyeu, Artsiom, and Kitamura, Hirotoshi},
  journal={German Conference on Pattern Recognition (GCPR)},
  year={2019}
}