multi-temporal-crop-classification-baseline/config/default_config.yaml at main · easierdata/multi-temporal-crop-classification-baseline · GitHub

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# Tensorboard logging directory
# Saves to repo root directory by default
tensorboard_log_path: ./tensorboard-logs

# Custom dataset params
# `dataset_path` is the source directory containing the training dataset content.
# `dataset_dir` is the name of the directory containing the images that will be used for training, validation, or inference.
# The csv files should contain the chip IDs for training, validation, and testing.
dataset_path: /path/to/training/dataset/directory
dataset_dir: <Enter name of the directory containing the images>
train_csv_path: /path/to/csv/file/with/chip_ids/filename.csv
validation_csv_path: /path/to/csv/file/with/chip_ids/filename.csv
inference_csv_path: /path/to/csv/file/with/chip_ids/filename.csv
apply_normalization: true
normal_strategy: z_value
stat_procedure: gpb
global_stats:
  min: [123.0, 311.0, 186.0, 414.0, 229.0, 159.0]
  max: [1106.0, 1618.0, 2131.0, 5016.0, 4470.0, 3602.0]
  mean: [454.5323083450189, 766.3169205335796, 824.5324240866765, 3067.7790788433, 2518.4688185922337, 1602.9963290088454]
  std: [265.0570796218974, 326.36202026620924, 524.754933401874, 964.2766895627702, 914.4499387205872, 861.2833172293738]
transformations:
  - v_flip
  - h_flip
  - d_flip
  - rotate
aug_params:
  rotation_degree: [-180, -90, 90, 180]

# DataLoader
train_BatchSize: 10
val_test_BatchSize: 3

# Model initialization params
n_classes: 14
input_channels: 18
filter_config: [64, 128, 256, 512, 1024, 1024]
use_skipAtt: false
train_dropout_rate: 0.15

# Model compiler params
working_dir: /path/to/working/directory/to/store/results
out_dir: <Enter name of the directory to store output results>
class_mapping:
  0: Unknown
  1: Natural Vegetation
  2: Forest
  3: Corn
  4: Soybeans
  5: Wetlands
  6: Developed/Barren
  7: Open Water
  8: Winter Wheat
  9: Alfalfa
  10: Fallow/Idle Cropland
  11: Cotton
  12: Sorghum
  13: Other
# Currently, multiple GPU's are not supported.
# If you have multiple GPU's, please specify the device ID of the one you want to use.
gpuDevices:
  - 0
init_type: kaiming
params_init: null
freeze_params: null

# Model fitting
epochs: 1
optimizer: sam
LR: 0.011
LR_policy: PolynomialLR
criterion:
  name: TverskyFocalLoss
  parameters:
    ignore_index: 0
    gamma: 0.9
    # weight:
    #   - 0.0182553
    #   - 0.03123664
    #   - 0.02590038
    #   - 0.03026126
    #   - 0.04142966
    #   - 0.04371284
    #   - 0.15352935
    #   - 0.07286951
    #   - 0.10277024
    #   - 0.10736637
    #   - 0.1447082
    #   - 0.17132445
    #   - 0.0566358

momentum: 0.95
checkpoint_interval: 20
resume: false
resume_epoch: null
lr_prams:
  # StepLR & MultiStepLR
  step_size: 3
  milestones:
    - 5
    - 10
    - 20
    - 35
    - 50
    - 70
    - 90
  gamma: 0.98
  # ReduceLROnPlateau
  mode: triangular
  factor: 0.8
  patience: 3
  threshold: 0.0001
  threshold_mode: rel
  min_lr: 3.0e-06
  # PolynomialLR
  max_decay_steps: 80
  min_learning_rate: 1.0e-04
  power: 0.85
  # CyclicLR
  base_lr: 3.0e-05
  max_lr: 0.01
  step_size_up: 1100

# Accuracy assessment
val_metric_fname: validate_metrics_global_z_gpb.csv