3-train-segmentation-unet.json

{
  "seed": 0,
  "run_name": "3-train-segmentation-unet",
  "description": "Train a UNET for segmentation on ground truth reconstructions",

  "train_dataset": "ScarSeg",
  "validation_dataset": "ScarSeg",
  "split_ratio": [4, 1, 1],
  "input_mode": "2d",
  "dataset_mode": "segmentation",

  "undersampling": {
    "comment": "Undersampling is not active in dataset_mode segmentation",
    "sampling_scheme": "varden",
    "acceleration_factor": 8,
    "variable_acceleration": false
  },

  "runner_type": "standard",
  "application": "segmentation",

  "model": {
    "name": "UNET",
      "num_inputs": 2,
      "num_outputs": 2,
      "num_layers_per_scale": 2,
      "encode_filters": [32, 64, 128, 256, 512],
      "decode_filters": [256, 128, 64, 32],
      "use_bn": true,
      "upsampling_mode": "pixelshuffle",
      "padding": "reflection",
      "output_activation": "none"
  },

  "loss_name": "CrossEntropy",

  "optimizer": {
    "name": "Adam",
    "learning_rate": 0.0002
  },

  "num_epochs": 200,
  "batch_size": 8,
  "epochs_per_validation": 1,
  "epochs_per_checkpoint": 1,
  "steps_per_train_summary": 20,
  "num_periodic_checkpoints": 1,
  "num_data_workers": 4,

  "train_metrics": ["dice_class_0", "dice_class_1"],
  "validation_metrics": ["dice_class_0", "dice_class_1"],
  "validation_checkpoint_metrics": ["dice_class_1"],

  "use_tensorboard": true,
  "num_image_summaries": 1
}