training:
  learning_rate: 5.0e-5
  batch_size: 8
  num_epochs: 50
  patch_size: 256
  scale_jitter_range: [0.8, 1.2]

model:
  fpn:
    enabled: true
    out_channels: 256
    levels: [2, 3, 4]
    norm: "bn"

  # 新增：可切换骨干网络配置（默认为 vgg16，保持与现有实现一致）
  backbone:
    name: "vgg16"           # 可选：vgg16 | resnet34 | efficientnet_b0
    pretrained: false        # 是否加载 ImageNet 预训练权重（如可用）

  # 新增：可选注意力机制（默认关闭，避免影响现有结果）
  attention:
    enabled: false
    type: "none"            # 可选：none | cbam | se
    places: []               # 插入位置：backbone_high | det_head | desc_head（数组）

matching:
  keypoint_threshold: 0.5
  ransac_reproj_threshold: 5.0
  min_inliers: 15
  pyramid_scales: [0.75, 1.0, 1.5]
  inference_window_size: 1024
  inference_stride: 768
  use_fpn: true
  nms:
    enabled: true
    radius: 4
    score_threshold: 0.5

evaluation:
  iou_threshold: 0.5

logging:
  use_tensorboard: true
  log_dir: "runs"
  experiment_name: "baseline"

paths:
  layout_dir: "path/to/layouts"
  save_dir: "path/to/save"
  val_img_dir: "path/to/val/images"
  val_ann_dir: "path/to/val/annotations"
  template_dir: "path/to/templates"
  model_path: "path/to/save/model_final.pth"

# 数据增强与合成数据配置（可选）
augment:
  elastic:
    enabled: false
    alpha: 40
    sigma: 6
    alpha_affine: 6
    prob: 0.3
  photometric:
    brightness_contrast: true
    gauss_noise: true

synthetic:
  enabled: false
  png_dir: "data/synthetic/png"
  ratio: 0.0  # 0~1，训练时混合的合成样本比例
  diffusion:
    enabled: false
    png_dir: "data/synthetic_diff/png"
    ratio: 0.0  # 0~1，训练时混合的扩散样本比例