seasonet_exp5_v1.yml

batch_size: 128
losses: {
  with_ce_criterion: true,
  weights: {
    ce: 1.0,
  }
}
dataset:
  root_path: data/SeasoNet/data
  cls: datasets.seasonet.SeasoNetDataset
  kwargs: {
    seasons: ['Spring'],
    bands: ['10m_RGB', '10m_IR',],
    grids:[1],
  }
  collate_fn: mods.v5.collate_fn
  stats: {
      mean: [444.21923828125, 715.9031372070312, 813.4345703125, 2604.867919921875],
      std: [279.85552978515625, 385.3569641113281, 648.458984375, 796.9918212890625],
      min: [-1025.0, -3112.0, -5122.0, -3851.0],
      max: [14748.0, 14960.0, 16472.0, 16109.0]
  }
  collate_kwargs: {}
  denorm: mods.v5.uncollate_fn
  printable: mods.v5.printable
optim: {
  learning_rate: 0.0001,
  model_betas: [0.9, 0.999],
  model_eps: 0.00000001,
  model_weight_decay: 0
}
semantic_segm: {
  pad_before: [4, 4, 4, 4],
  in_channels: 4,
  type: FarSeg,
  model: {
    backbone: resnet50,
  }
}
train: semantic_segm.training.train
mean_std: mods.v5.get_mean_std
metrics: {
  eval_every: 1
}
visualize: {
  model: semantic_segm.model.build_model,
  checkpoint: chk_loader.load_state_dict_model_only,
}