datasets:
  batch_size: 16

  collate_fn:
    name: "CollateFn"

  folder:
    name: "Folder"

  transformers:
    names: ["DirectResize", "TwoFlip", "ToTensor", "Normalize"]
    DirectResize:
      size: (256, 128)
      interpolation: 3
    Normalize:
      mean: [0.485, 0.456, 0.406]
      std: [0.229, 0.224, 0.225]

model:
  name: "ft_net"
  ft_net:
    load_checkpoint: "/data/my_model_zoo/res50_market1501.pth"

extract:
  assemble: 1

  extractor:
    name: "ReIDSeries"
    ReIDSeries:
      extract_features: ["output"]

  splitter:
    name: "Identity"

  aggregators:
    names: ["GAP"]

index:
  query_fea_dir: "/data/features/best_features/market/query"
  gallery_fea_dir: "/data/features/best_features/market/gallery"

  feature_names: ['output']

  dim_processors:
    names: ["L2Normalize", "PCA", "L2Normalize"]
    PCA:
      proj_dim: 512  # the dimension after reduction. If it is 0, then no reduction will be done.
      whiten: False  # whether do whiten when using PCA.
      train_fea_dir: "/data/features/best_features/market/gallery"  # path of the features for training PCA.
      l2: True  # whether do l2-normalization on the training features.

  feature_enhancer:
    name: "Identity"

  metric:
    name: "KNN"

  re_ranker:
    name: "KReciprocal"
    KReciprocal:
      k1: 20  # hyper-parameter for calculating jaccard distance.
      k2: 6  # hyper-parameter for calculating local query expansion.
      lambda_value: 0.3  # hyper-parameter for calculating the final distance.

evaluate:
  evaluator:
    name: "ReIDOverAll"