# Model Configuration (Manuscript Section 4)
model:
  name: "CourtKeyNet"
  
  # Octave Feature Extractor
  ofe:
    channels_per_band: 64  # C for high/mid/low paths
    stem_channels: 64
    
  # Polar Transform Attention  
  pta:
    enabled: true
    radial_bins: 64      # Manuscript says fine-grained polar grid
    angular_bins: 128
    
  # Keypoint Localization
  heatmap_sigma: 8.0     # Increased from 2.0 to help convergence
  num_keypoints: 4
  feature_dim: 128
  
  # Transformer Refinement
  transformer:
    num_layers: 2
    num_heads: 4
    dim_feedforward: 512
    dropout: 0.1
    
  # Quadrilateral Constraint Module
  qcm:
    hidden_dims: [64, 128]
    output_dim: 128

# Training Configuration
train:
  epochs: 300
  batch_size: 47
  num_workers: 4
  imgsz: 640
  early_stopping_patience: 20  # Stop if no improvement for N epochs
  
  # Optimizer (Manuscript: AdamW)
  optimizer: "adamw"
  lr0: 0.00005 # Reduced further to prevent divergence with high loss weights
  lrf: 0.01  # Final LR multiplier
  weight_decay: 0.0005
  momentum: 0.937
  
  # LR Scheduler
  warmup_epochs: 5
  scheduler: "cosine"
  
  # Loss Weights (Manuscript Eq. 47)
  # Geometric losses disabled initially (set to 0) to let model learn keypoints first
  loss_weights:
    keypoint: 10.0     # Primary loss for coordinate accuracy
    heatmap: 20.0      # Heatmap supervision
    edge: 0.0          # Disabled initially - enable after model converges
    diagonal: 0.0      # Disabled initially
    angle: 0.0         # Disabled initially
  
  # Geometric loss warmup (linearly scale geo losses over N epochs)
  # Only relevant when edge/diagonal/angle > 0
  # geo_warmup_epochs: 10 #enabled in finetune only
  
  # Training Tricks
  mixed_precision: true
  grad_clip: 1.0
  ema_decay: 0.9999
  
  # Checkpointing
  save_interval: 5
  save_best: true
  
  # Paths
  project: "runs/courtkeynet"
  name: "exp"

# Validation
val:
  batch_size: 32
  interval: 1  # Validate every N epochs

# Weights & Biases Configuration
wandb:
  enabled: true
  project: "CourtKeyNet"
  entity: null  # Set to your wandb username or team name, or leave null for default
  name: CourtKeyNet    # Run name (auto-generated if null)
  tags: ["badminton", "court-detection", "keypoint"]
  log_freq: 100  # Log every N batches
  
# Device
device: "cuda"  # or "cpu"