data:
  # TODO: Update this path for your HPC cluster, e.g.:
  # root: "/scratch/$USER/immunogold_data/Gold Particle Labelling"
  root: "/Users/mpcr/Downloads/Max Planck Data/Gold Particle Labelling"
  analyzed_dir: "analyzed synapses"
  test_dir: "labeled replica - Test Data from Synapses"
  synapse_ids: ["S1", "S4", "S7", "S8", "S13", "S15", "S22", "S25", "S27", "S29"]
  # S7 and S15 have no 6nm annotations — exclude from 6nm evaluation
  incomplete_6nm: ["S7", "S15"]
  raw_dir: "data/raw"
  annotation_dir: "data/annotations"
  weights_dir: "weights"
  patch_size: 512
  stride: 2
  bead_classes: ["6nm", "12nm"]

heatmap:
  sigmas:
    6nm: 1.0
    12nm: 1.5

model:
  backbone: "resnet50"
  pretrained_weights: "weights/cem500k_mocov2_resnet50.pth.tar"
  cem500k_url: "https://zenodo.org/records/6453140/files/cem500k_mocov2_resnet50_200ep.pth.tar?download=1"
  bifpn_channels: 128
  bifpn_rounds: 2
  num_classes: 2

training:
  batch_size: 8
  n_seeds: 5
  n_snapshot_epochs: [150, 170, 190]
  hard_mining_fraction: 0.7
  copy_paste_per_class: 5

  phases:
    phase1:
      epochs: 60
      lr: 1.0e-3
      weight_decay: 1.0e-4
      freeze_encoder: true
    phase2:
      epochs: 60
      lr_layer3: 1.0e-5
      lr_layer4: 5.0e-5
      lr_decoder: 5.0e-4
      weight_decay: 1.0e-4
    phase3:
      epochs: 80
      lr_stem: 1.0e-6
      lr_layer1: 5.0e-6
      lr_layer2: 1.0e-5
      lr_layer3: 5.0e-5
      lr_layer4: 1.0e-4
      lr_decoder: 2.0e-4
      weight_decay: 1.0e-4
      eta_min: 1.0e-7

  loss:
    focal_alpha: 2
    focal_beta: 4
    lambda_offset: 1.0

  early_stopping:
    patience: 20
    monitor: "val_f1_mean"

postprocessing:
  nms_kernel_size:
    6nm: 3
    12nm: 5
  confidence_threshold_sweep:
    start: 0.05
    stop: 0.95
    step: 0.01
  mask_filter_margin_px: 5
  cross_class_nms_distance_px: 8

evaluation:
  match_radii_px:
    6nm: 9
    12nm: 15
  loocv_val_offset: 1

tta:
  enabled: true
  n_geometric: 8      # D4 group: 4 rotations x 2 reflections
  intensity_factors: [0.9, 1.1]

ensemble:
  n_seeds: 5
  snapshot_epochs: [150, 170, 190]
  # Total ensemble members per fold: 5 seeds x 3 snapshots = 15