TGS Salt Identification Challenge

This project represents my contribution to the Kaggle's TGS Salt Identification Challenge (https://www.kaggle.com/c/tgs-salt-identification-challenge) held in the second part of the 2018.

More details can be found in the following paper: https://doi.org/10.3390/ijgi9010024

To be able to use the code please follow listed instructions:

1. Download the competition data from the following page:
   https://www.kaggle.com/c/tgs-salt-identification-challenge/data

2. Copy data into "data" subfolder. Unpack "train.zip" to "train" folder and "test.zip" to "test" folder. The folder structure should be like this:

   data/depths.csv  
   data/sample_submission.csv  
   data/train.csv  
   data/test/images/*.png  
   data/train/images/*.png  
   data/train/masks/*.png  
   

3. Execute prepare_data.py to create:

   train_x_fixed.npy  
   train_y_fixed.npy  
   test_x.npy  
   

4. Optionally open model.py and set desired model_type:

   model_type = 'my_res_unet'  
   # model_type = 'unet'  
   # model_type = 'fpn'  
   # model_type = 'linknet'  
   # model_type = 'pspnet'  
   

5. Execute train.py to train 5 models. In case of a problem, adjust batch size:

   batch_size = 32  
   

6. Execute prepare_submission.py to create submission CSV file (5 trained models are expected to be found in tmp folder). In case of a problem, adjust batch size:

   batch_size = 64  
   

7. Execute visualize_outputs.py to visualize trained models outputs (5 trained models are expected to be found in tmp folder). In case of a problem, adjust batch size:

   batch_size = 64