mirror of https://github.com/PyRetri/PyRetri.git
84 lines
2.8 KiB
YAML
84 lines
2.8 KiB
YAML
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# retrieval settings
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datasets:
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# number of images in a batch.
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batch_size: 16
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# function for stacking images in a batch.
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collate_fn:
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name: "CollateFn" # name of the collate_fn.
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# function for loading images.
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folder:
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name: "Folder" # name of the folder.
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# a list of data augmentation functions.
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transformers:
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names: ["ShorterResize", "CenterCrop", "ToTensor", "Normalize"] # names of transformers.
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ShorterResize:
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size: 256 # target size of the shorter edge.
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CenterCrop:
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size: 224 # target size of the crop img.
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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: "resnet50" # name of the model.
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resnet50:
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load_checkpoint: "torchvision://resnet50" # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.
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extract:
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# way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop). 0 means concat these features and 1 means sum these features.
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assemble: 0
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# function for assigning output features.
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extractor:
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name: "ResSeries" # name of the extractor.
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ResSeries:
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extract_features: ["pool5"] # name of the output feature map. If it is ["all"], then all available features will be output.
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# function for splitting the output features (e.g. PCB).
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splitter:
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name: "Identity" # name of the function for splitting features.
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# a list of pooling functions.
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aggregators:
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names: ["SCDA"] # names of aggregators.
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index:
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# path of the query set features and gallery set features.
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query_fea_dir: "/data/features/best_features/cub/query"
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gallery_fea_dir: "/data/features/best_features/cub/gallery"
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# name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
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# If there are multiple elements in the list, they will be concatenated on the channel-wise.
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feature_names: ["pool5_SCDA"]
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# a list of dimension process functions.
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dim_processors:
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names: ["L2Normalize", "PCA", "L2Normalize"] # names of dimension processors.
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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/cub/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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# function for enhancing the quality of features.
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feature_enhancer:
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name: "Identity" # name of the feature enhancer.
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# function for calculating the distance between query features and gallery features.
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metric:
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name: "KNN" # name of the metric.
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# function for re-ranking the results.
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re_ranker:
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name: "Identity" # name of the re-ranker.
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evaluate:
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# function for evaluating results.
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evaluator:
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name: "OverAll" # name of the evaluator.
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