thaw-slump-segmentation/config_example.yml at master · initze/thaw-slump-segmentation · GitHub

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# Model Specification
model:
  # Model Architecture. Available:
  # Unet, UnetPlusPlus, Unet3Plus, MAnet, Linknet, FPN, PSPNet, DeepLabV3, DeepLabV3Plus, PAN]
  architecture: Unet
  # Model Encoder. Examples:
  # resnet18, resnet34, resnet50, resnet101, resnet152
  # Check https://github.com/qubvel/segmentation_models.pytorch#encoders for the full list of available encoders
  encoder: resnet34
  # Encoder weights to use (if transfer learning is desired)
  # `imagenet` is available for all encoders, some of them have more options available
  # `random` initializes the weights randomly
  # Check https://github.com/qubvel/segmentation_models.pytorch#encoders for the
  # full list of weights available for each encoder
  encoder_weights: imagenet
# Loss Function to use. Available:
# JaccardLoss, DiceLoss, FocalLoss, LovaszLoss, SoftBCEWithLogitsLoss
loss_function: FocalLoss
# Data Configuration
data_threads: 4  # Number of threads for data loading, must be 0 on Windows
data_sources:  # Enabled input features
  - planet
  - ndvi
  - tcvis
  - relative_elevation
  - slope
datasets:
  train:
    augment: true
    augment_types:
    - HorizontalFlip
    - VerticalFlip
    - Blur
    - RandomRotate90
    - RandomBrightnessContrast
    - MultiplicativeNoise
    shuffle: true
    scenes:
      - "20190618_201847_1035"
      - "20190618_201848_1035"
      - "20190623_200555_0e19"
  val:
    augment: false
    shuffle: false
    scenes:
      - "20190727_160426_104e"
  test:
    augment: false
    shuffle: false
    scenes:
      - "20190709_042959_08_1057"
# Training Parameters
batch_size: 4
learning_rate: 0.01
# Learning rate scheduler. Available:
# ExponentialLR, StepLR (https://pytorch.org/docs/stable/optim.html)
# if no lr_step_size given then lr_step_size=10, gamma=0.1 for StepLR and gamma=0.9 for ExponentialLR
learning_rate_scheduler: StepLR
lr_step_size: 10
lr_gamma: 0.1

# Training Schedule
schedule:
  - phase: Training
    epochs: 30
    steps:
      - train_on: train
      - validate_on: val
      - log_images
# Visualization Configuration
visualization_tiles:
  20190727_160426_104e: [5, 52, 75, 87, 113, 139, 239, 270, 277, 291, 305]