https://github.com/ybabakhin/kaggle_salt_bes_phalanx

Winning solution for the Kaggle TGS Salt Identification Challenge.
https://github.com/ybabakhin/kaggle_salt_bes_phalanx

deep-learning image-segmentation keras pseudo-labeling pytorch semantic-segmentation u-net

Last synced: 6 days ago
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Winning solution for the Kaggle TGS Salt Identification Challenge.

• Host: GitHub
• URL: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
• Owner: ybabakhin
• Created: 2018-08-02T20:20:32.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-08-08T22:11:51.000Z (over 5 years ago)
• Last Synced: 2024-12-31T19:12:48.537Z (13 days ago)
• Topics: deep-learning, image-segmentation, keras, pseudo-labeling, pytorch, semantic-segmentation, u-net
• Language: Python
• Homepage: https://arxiv.org/abs/1904.04445
• Size: 6.68 MB
• Stars: 353
• Watchers: 10
• Forks: 84
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# 1st Place Solution for the Kaggle TGS Salt Identification Challenge (b.e.s. & phalanx)

## Paper describing the solution: 

**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](https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/69291).

## ENVIRONMENT

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](https://www.docker.com/)

* [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)

## DATA SETUP

Download and unzip [competition data](https://www.kaggle.com/c/tgs-salt-identification-challenge/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.

## WEIGHTS SETUP

To get the weights from the final stage models download them from [google drive](https://drive.google.com/file/d/12iXDUhBTC6596MLAC2aiN-GDVqBbGBWh/view?usp=sharing) and unzip into corresponding `bes/weights/` and `phalanx/weights` directories.

## DOCKER SETUP

To build and start a docker container run:

```bash

cd docker 

./build.sh

./run.sh

```

## MODEL BUILD

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:

### 1. train models (creates model weights in bes/weights and phalanx/weights)

```bash

./train.sh

```

### 2. make prediction (creates predictions/test_prediction.csv)

```bash

./predict.sh

```

## ADDITIONAL NOTES

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

## CITATION

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}

}

```