content: experiment_name = "MA_unet_internal_testing"  # configuration name
  base_dir = '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/segmentation_models'
  
  [processing]
  base_hardware = "MA_mac"   # Where the program is run [EDDIE/PC]
  device = "GPU"    # Which processor is used [GPU/CPU]
  pe = 1          # How many parallel environments (cores) needed
  
  [data]
  n_channels = 1 # channels to output to model
  batch_size = 1  # Batch size for dataloader
  num_workers = 1 # parallel threads to use to speed up data processing
  dir_train_mask = '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/dummy_set/masks_train'
  dir_train_img = '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/dummy_set/images_train'
  
  #dir_train_mask = ['/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/dummy_set/masks_train',
  #    '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/maximal_set/lsdb_gels/masks',
  #    '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/maximal_set/matthew_gels/masks',
  #    '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/maximal_set/matthew_gels_2/masks']
  
  #dir_train_img = ['/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/dummy_set/images_train',
  #    '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/maximal_set/lsdb_gels/images',
  #    '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/maximal_set/matthew_gels/images',
  #    '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/maximal_set/matthew_gels_2/images']
  
  dir_val_img = '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/dummy_set/images_val'
  dir_val_mask = '/Users/matt/Documents/PhD/research_output/Automatic_Gel_Analyzer/data/processed_gels/dummy_set/masks_val'
  split_training_dataset = false
  apply_augmentations = true
  padding = false # pads all image with zeros to same size to allow for a batch size higher than 1
  individual_padding = true
  
  [model]
  model_name = 'smp_unet'   # model architecture name
  classes = 2     # Number of possible segmentation classes
  encoder_name = 'resnet18'
  [training]
  loss = ['dice', 'unet_weighted_crossentropy']
  #loss_component_weighting = [0.5, 1.0]
  #class_loss_weighting = true
  #class_loss_weight_damper = [1.0, 0.5]
  lr = 1e-5       # learning rate
  epochs = 2     # Number of epochs to run
  grad_scaler = false     # Use mixed precision for faster training
  load_checkpoint = false    # Load model from a .pth file (Bool/epoch ID)
  optimizer_type = 'adam' # Use adam optimizer
  scheduler_type = 'CosineAnnealingWarmRestarts'  # No scheduler is used
  save_checkpoint = true # Whether model checkpoints are saved
  checkpoint_frequency = 1 # How often checkpoints are saved (per epoch)
  model_cleanup_frequency = 20 # How often old checkpoints are deleted (in epochs)
  wandb_track = false # Whether to track training with wandb
  [training.scheduler_specs]
  restart_period = 100
  