mirror of https://github.com/PyRetri/PyRetri.git
71 lines
1.6 KiB
YAML
71 lines
1.6 KiB
YAML
# retrieval settings
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datasets:
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batch_size: 16
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collate_fn:
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name: "CollateFn"
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folder:
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name: "Folder"
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transformers:
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names: ["DirectResize", "TwoFlip", "ToTensor", "Normalize"]
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DirectResize:
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size: (256, 128)
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interpolation: 3
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Normalize:
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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model:
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name: "ft_net"
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ft_net:
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load_checkpoint: "/home/songrenjie/projects/reID_baseline/model/ft_ResNet50/res50_duke.pth"
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extract:
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assemble: 1
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extractor:
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name: "ReIDSeries"
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ReIDSeries:
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extract_features: ["output"]
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splitter:
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name: "Identity"
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aggregators:
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names: ["GAP"]
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index:
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query_fea_dir: "/data/features/best_features/duke/query"
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gallery_fea_dir: "/data/features/best_features/duke/gallery"
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feature_names: ['output']
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dim_processors:
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names: ["L2Normalize", "PCA", "L2Normalize"]
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PCA:
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proj_dim: 512 # the dimension after reduction. If it is 0, then no reduction will be done.
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whiten: False # whether do whiten when using PCA.
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train_fea_dir: "/data/features/best_features/duke/gallery" # path of the features for training PCA.
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l2: True # whether do l2-normalization on the training features.
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feature_enhancer:
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name: "Identity"
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metric:
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name: "KNN"
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re_ranker:
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name: "KReciprocal"
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KReciprocal:
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k1: 20 # hyper-parameter for calculating jaccard distance.
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k2: 6 # hyper-parameter for calculating local query expansion.
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lambda_value: 0.3 # hyper-parameter for calculating the final distance.
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evaluate:
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evaluator:
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name: "ReIDOverAll"
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