# Problem A: Few-Shot Defect Classification - Configuration
# Intel contest: 8 defect classes, grayscale images up to ~7000x5600
# Uses ONLY official challenge data from challenge/dataset/Dataset/Data/
# Dataset: defect1(253), defect2(178), defect3(9), defect4(14),
#          defect5(411), defect8(803), defect9(319), defect10(674), good(7135)
# Contest: classify into 8 DEFECT classes
#
# CRITICAL: defect3~defect9 (0.963 cosine sim) and defect4~defect8 (0.889)
# are nearly identical without training on them. ALL 8 classes must be in
# training so the backbone learns to separate these similar pairs.

data:
  root: "../challenge/dataset/Dataset/Data/"
  img_size: 518                  # DINOv2 native resolution (37x14 patches)
  defect_only: false
  # ALL 8 defect classes + good (class 0) in training
  train_classes: [0, 1, 2, 3, 4, 5, 8, 9, 10]
  test_classes: [3, 4]           # Monitor the hardest classes during validation
  all_classes: [0, 1, 2, 3, 4, 5, 8, 9, 10]

model:
  backbone: "dinov2"
  backbone_size: "large"           # DINOv2 ViT-L/14 (1024-dim, 304M params)
  freeze_backbone: true
  unfreeze_last_n: 6               # Fine-tune last 6 transformer blocks + norm
  grad_checkpointing: true
  proj_hidden: 768
  proj_dim: 512

training:
  n_way: 9                         # ALL 9 classes per episode (8 defect + good)
  k_shot: 5                        # Higher shot count for better prototypes
  n_query: 10                      # More queries = stronger gradient signal
  # Sampler uses replacement for rare classes (defect3=9, defect4=14)
  n_episodes_train: 500            # Fewer but harder 8-way episodes
  n_episodes_val: 100
  epochs: 100
  lr: 3.0e-4
  lr_backbone: 5.0e-6
  warmup_epochs: 5
  weight_decay: 1.0e-4
  use_amp: true
  gradient_clip: 1.0
  label_smoothing: 0.1             # Prevent overconfidence on easy classes
  patience: 20

evaluation:
  n_seeds: 5
  max_examples: 50
  kshot_values: [1, 3, 5, 10, 20]
  target_accuracy: 0.85

output:
  checkpoint_dir: "checkpoints/"
  results_dir: "outputs/"

seed: 42