content: experiment_name = "KK_unet_default"  # configuration name
  
  [processing]
  base_hardware = "EDDIE"   # Where the program is run [EDDIE/PC]
  core = "GPU"    # Which processor is used [GPU/CPU]
  pe = 1          # How many parallel environments (cores) needed
  memory = 64     # Required memory per core in GBytes
  
  [data]
  n_channels = 1
  batch_size = 4  # Batch size for dataloader
  num_workers = 1 # parallel threads to use to speed up data processing
  img_scale = 0.5 # TODO: is this necessary to be 0.5?
  validation = 10 # % of the data that is used as validation (0-100)
  bilinear = false
  base_dir = './'
  split_training_dataset = false
  apply_augmentations = false
  padding = false
  
  [model]
  model_name = 'milesial-UNet'   # model architecture name
  
  classes = 2     # Number of possible segmentation classes
  bilinear = false # Use bilinear upsampling within down/up blocks in unet network
  
  [training]
  loss = 'both'
  lr = 1e-5       # learning rate
  epochs = 10     # Number of epochs to run
  amp = false     # Use mixed precision TODO: change variable name to something more descriptive
  load = false    # Load model from a .pth file (Bool/Path)
  optimizer_type = 'adam' # Use adam optimizer
  scheduler = false  # No scheduler is used
  save_checkpoint = true # Whether model checkpoints are saved
  
  