# Default configuration for WireSegHR (segmentation-only)
backbone: mit_b2
pretrained: true  # Uses HF SegFormer weights if available

coarse:
  train_size: 512
  test_size: 1024

fine:
  patch_size: 512
  overlap: 128

conditioning:
  cond_from: coarse_logits_1x1
  cond_crop: patch  # per published method (method_yq)

minmax:
  enable: true
  kernel: 6  # fixed 6x6 luminance min/max

label:
  coarse_downsample: maxpool

inference:
  alpha: 0.01
  prob_threshold: 0.5  # default inference threshold per paper tuning
  fine_patch_size: 1024
  stitch: avg_logits

eval:
  max_samples: 12
  fine_batch: 16

optim:
  iters: 5000
  batch_size: 4
  lr: 6e-5
  weight_decay: 0.01
  schedule: poly
  power: 1.0
  precision: bf16  # one of: fp32, fp16, bf16

# training housekeeping
seed: 42
out_dir: runs/wireseghr
eval_interval: 200
ckpt_interval: 400
resume: runs/wireseghr/ckpt_4800.pt  # optional

# dataset paths (placeholders)
data:
  train_images: dataset/train/images
  train_masks: dataset/train/gts
  val_images: dataset/val/images
  val_masks: dataset/val/gts
  test_images: dataset/test/images
  test_masks: dataset/test/gts