# FLAME on STARCOP (AVIRIS-NG) — reference training configuration.
#
# Protocol: full-tile (512x512) training with the two-phase curriculum —
# aux-only score pretrain (pretrain_epochs), then end-to-end segmentation with
# the auxiliary mag1c-sas L1 loss decaying over decay_frac of the remaining
# epochs. Validation: pixel F1 at sigmoid > 0.5 with 3x3 morphological opening.

dataset: starcop

model:
  in_channels: 72                # SWIR 2122-2488 nm (AVIRIS-NG)
  width: 14
  modes1: 12
  modes2: 12
  n_fno_layers: 3
  n_ufno_layers: 3
  dropout_rate: 0.0
  seg_channels: [28, 14]
  seg_kernel_size: 3
  score_divisor: 1750.0
  rgb_divisor: 60.0
  norm_type: batch
  score_clamp: [0.0, 2.0]
  wv_range: [2122, 2488]
  spectrum_path: resources/starcop/ch4_spectrum.npy
  centers_path: resources/starcop/band_centers.npy
  baseline_stats_path: resources/starcop/baseline_stats.pt

train:
  uid: flame_starcop
  lr: 2.0e-3
  epochs: 80
  batch_size: 24                 # must be divisible by the number of GPUs
  n_workers: 4
  log_dir: logs
  seed: 42
  patience: 25
  ckpt_interval: 10
  prefetch_factor: 1
  vis_interval: 10
  pretrain_epochs: 10
  aux_weight: 1.0
  decay_frac: 0.5
  seg_loss: dice_bce
  warmup_epochs: 3
  warmup_start_factor: 0.1

data:
  train_data_path:
    - datasets/starcop
  val_data_path: datasets/starcop/STARCOP_allbands_Eval
  tile_size: 512
  npy_dir: ~                     # optional SWIR npy cache (scripts/build_starcop_npy_cache.py)
  val_max_tiles: ~
  wv_range: [2122, 2488]
  load_rgb: true
  augment: true

vis:
  n_val_plume: 15
  n_val_noplume: 5
  n_train: 10
  seed: 42