amakropoulos / nerve-segmentation Goto Github PK

This toolkit was used for the Kaggle Ultrasound nerve segmentation challenge.

The training data need to be stored in a 2D image format (e.g. png, tif) inside a train directory.

• For segmentation the train directory has to contain a mask for each image:
  if the image is stored as train/image1.tif the mask will be train/image1_mask.tif

• For autoencoders only the images will be inside the train directory (no masks).

The testing data are stored as images inside a test directory (no masks).

Training is performed with the train.py with parameters:

Testing is performed with the test.py. Test.py has the same parameters as above (apart from -train) and additionally

Additional parameters can be viewed by running the test.py with the --help option.

The config_default.py contains the default parameters used for training.
This can be overriden for the different experiments if required.
In order to setup a new experiment the user needs to:

• create a directory with the experiment name inside the "params" folder (e.g. params/unet-small)
• write a config.py file inside the experiment directory (e.g. params/unet-small/config.py) with the parameters he wants overriden (e.g. depth=4). Parameters that are not specified retain their original values from the config_default.py
• run the training/testing specifying the experiment as the version (e.g. python train.py -v unet-small )