upload

configs/caltech.yaml

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+# retrieval settings
+
+datasets:
+  # number of images in a batch.
+  batch_size: 16
+
+  # function for stacking images in a batch.
+  collate_fn:
+    name: "CollateFn"  # name of the collate_fn.
+
+  # function for loading images.
+  folder:
+    name: "Folder"  # name of the folder.
+
+  # a list of data augmentation functions.
+  transformers:
+    names: ["PadResize", "ToTensor", "Normalize"]  # names of transformers.
+    PadResize:
+      size: 224  # target size of the longer edge.
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "resnet50"  # name of the model.
+  resnet50:
+    load_checkpoint: "torchvision://resnet50"  # path of the model checkpoint. If it is started with "torchvision://", the model will be loaded from torchvision.
+
+extract:
+  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop). 0 means concat these features and 1 means sum these features.
+  assemble: 0
+
+  # function for assigning output features.
+  extractor:
+    name: "ResSeries"  # name of the extractor.
+    ResSeries:
+      extract_features: ["pool5"]  # name of the output feature map. If it is ["all"], then all available features will be output.
+
+  # function for splitting the output features (e.g. PCB).
+  splitter:
+    name: "Identity"  # name of the function for splitting features.
+
+  # a list of pooling functions.
+  aggregators:
+    names: ["GeM"]  # names of aggregators.
+
+index:
+  # path of the query set features and gallery set features.
+  query_fea_dir: "/data/features/best_features/caltech/query"
+  gallery_fea_dir: "/data/features/best_features/caltech/gallery"
+
+  # name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
+  # If there are multiple elements in the list, they will be concatenated on the channel-wise.
+  feature_names: ["pool5_GeM"]
+
+  # a list of dimension process functions.
+  dim_processors:
+    names: ["L2Normalize", "PCA", "L2Normalize"]  # names of dimension processors.
+    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/caltech/gallery"  # path of the features for training PCA.
+      l2: True  # whether do l2-normalization on the training features.
+
+  # function for enhancing the quality of features.
+  feature_enhancer:
+    name: "Identity"  # name of the feature enhancer.
+
+  # function for calculating the distance between query features and gallery features.
+  metric:
+    name: "KNN"  # name of the metric.
+
+  # function for re-ranking the results.
+  re_ranker:
+    name: "Identity"  # name of the re-ranker.
+
+evaluate:
+  # function for evaluating results.
+  evaluator:
+    name: "OverAll"  # name of the evaluator.
+

configs/caltech_w_tricks.yaml

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+# retrieval settings
+
+datasets:
+  batch_size: 16
+
+  collate_fn:
+    name: "CollateFn"
+
+  folder:
+    name: "Folder"
+
+  transformers:
+    names: ["PadResize", "ToTensor", "Normalize"]
+    PadResize:
+      size: 224
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "resnet50"
+  resnet50:
+    load_checkpoint: "torchvision://resnet50"
+
+extract:
+  assemble: 0
+
+  extractor:
+    name: "ResSeries"
+    ResSeries:
+      extract_features: ["pool5"]
+
+  splitter:
+    name: "Identity"
+
+  aggregators:
+    names: ["GeM"]
+
+index:
+  query_fea_dir: "/data/features/best_features/caltech/query"
+  gallery_fea_dir: "/data/features/best_features/caltech/gallery"
+
+  feature_names: ["pool5_GeM"]
+
+  dim_processors:
+    names: ["L2Normalize", "PCA", "L2Normalize"]
+    PCA:
+      proj_dim: 512
+      whiten: False
+      train_fea_dir: "/data/features/best_features/caltech/gallery"
+      l2: True
+
+  feature_enhancer:
+    name: "DBA"
+    DBA:
+      enhance_k: 10  # number of the nearest points to be calculated.
+
+  metric:
+    name: "KNN"
+
+  re_ranker:
+    name: "QEKR"
+    QEKR:
+      qe_times: 1  # number of query expansion times.
+      qe_k: 10  # number of the neighbors to be combined.
+      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: "OverAll"

configs/cub.yaml

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+# retrieval settings
+
+datasets:
+  # number of images in a batch.
+  batch_size: 16
+
+  # function for stacking images in a batch.
+  collate_fn:
+    name: "CollateFn"  # name of the collate_fn.
+
+  # function for loading images.
+  folder:
+    name: "Folder"  # name of the folder.
+
+  # a list of data augmentation functions.
+  transformers:
+    names: ["ShorterResize", "CenterCrop", "ToTensor", "Normalize"]  # names of transformers.
+    ShorterResize:
+      size: 256  # target size of the shorter edge.
+    CenterCrop:
+      size: 224  # target size of the crop img.
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "resnet50"  # name of the model.
+  resnet50:
+    load_checkpoint: "torchvision://resnet50"  # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.
+
+extract:
+  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop). 0 means concat these features and 1 means sum these features.
+  assemble: 0
+
+  # function for assigning output features.
+  extractor:
+    name: "ResSeries"  # name of the extractor.
+    ResSeries:
+      extract_features: ["pool5"]  # name of the output feature map. If it is ["all"], then all available features will be output.
+
+  # function for splitting the output features (e.g. PCB).
+  splitter:
+    name: "Identity"  # name of the function for splitting features.
+
+  # a list of pooling functions.
+  aggregators:
+    names: ["SCDA"]  # names of aggregators.
+
+index:
+  # path of the query set features and gallery set features.
+  query_fea_dir: "/data/features/best_features/cub/query"
+  gallery_fea_dir: "/data/features/best_features/cub/gallery"
+
+  # name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
+  # If there are multiple elements in the list, they will be concatenated on the channel-wise.
+  feature_names: ["pool5_SCDA"]
+
+  # a list of dimension process functions.
+  dim_processors:
+    names: ["L2Normalize", "PCA", "L2Normalize"]  # names of dimension processors.
+    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/cub/gallery"  # path of the features for training PCA.
+      l2: True  # whether do l2-normalization on the training features.
+
+  # function for enhancing the quality of features.
+  feature_enhancer:
+    name: "Identity"  # name of the feature enhancer.
+
+  # function for calculating the distance between query features and gallery features.
+  metric:
+    name: "KNN"  # name of the metric.
+
+  # function for re-ranking the results.
+  re_ranker:
+    name: "Identity"  # name of the re-ranker.
+
+evaluate:
+  # function for evaluating results.
+  evaluator:
+    name: "OverAll"  # name of the evaluator.
+

configs/cub_w_tricks.yaml

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+# retrieval settings
+
+datasets:
+  batch_size: 16
+
+  collate_fn:
+    name: "CollateFn"
+
+  folder:
+    name: "Folder"
+
+  transformers:
+    names: ["ShorterResize", "CenterCrop", "ToTensor", "Normalize"]
+    ShorterResize:
+      size: 256
+    CenterCrop:
+      size: 224
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "resnet50"
+  resnet50:
+    load_checkpoint: "torchvision://resnet50"
+
+extract:
+  assemble: 0
+
+  extractor:
+    name: "ResSeries"
+    ResSeries:
+      extract_features: ["pool5"]
+
+  splitter:
+    name: "Identity"
+
+  aggregators:
+    names: ["SCDA"]
+
+index:
+  query_fea_dir: "/data/features/best_features/cub/query"
+  gallery_fea_dir: "/data/features/best_features/cub/gallery"
+
+  feature_names: ["pool5_SCDA"]
+
+  dim_processors:
+    names: ["L2Normalize", "PCA", "L2Normalize"]
+    PCA:
+      proj_dim: 512
+      whiten: False
+      train_fea_dir: "/data/features/best_features/cub/gallery"
+      l2: True
+
+  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: "OverAll"
+

configs/duke.yaml

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+# retrieval settings
+
+datasets:
+  # number of images in a batch.
+  batch_size: 16
+
+  # function for stacking images in a batch.
+  collate_fn:
+    name: "CollateFn"
+
+  # function for loading images.
+  folder:
+    name: "Folder"
+
+  # a list of data augmentation functions.
+  transformers:
+    names: ["DirectResize", "TwoFlip", "ToTensor", "Normalize"]  # names of transformers.
+    DirectResize:
+      size: (256, 128)  # target size of the output img.
+      interpolation: 3  # nearest interpolation
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "ft_net"  # name of the model.
+  ft_net:
+    load_checkpoint: "/home/songrenjie/projects/reID_baseline/model/ft_ResNet50/res50_duke.pth"  # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.
+
+extract:
+  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop). 0 means concat these features and 1 means sum these features.
+  assemble: 1
+
+  # function for assigning output features.
+  extractor:
+    name: "ReIDSeries"  # name of the extractor.
+    ReIDSeries:
+      extract_features: ["output"]  # name of the output feature map. If it is ["all"], then all available features will be output.
+
+  # function for splitting the output features (e.g. PCB).
+  splitter:
+    name: "Identity"  # name of the function for splitting features.
+
+  # a list of pooling functions.
+  aggregators:
+    names: ["GAP"]  # names of aggregators.
+
+index:
+  # path of the query set features and gallery set features.
+  query_fea_dir: "/data/features/best_features/duke/query"
+  gallery_fea_dir: "/data/features/best_features/duke/gallery"
+
+  # name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
+  # If there are multiple elements in the list, they will be concatenated on the channel-wise.
+  feature_names: ['output']
+
+  # a list of dimension process functions.
+  dim_processors:
+    names: ["L2Normalize"]  # names of dimension processors.
+
+  # function for enhancing the quality of features.
+  feature_enhancer:
+    name: "Identity"  # name of the feature enhancer.
+
+  # function for calculating the distance between query features and gallery features.
+  metric:
+    name: "KNN"  # name of the metric.
+
+  # function for re-ranking the results.
+  re_ranker:
+    name: "Identity"  # name of the re-ranker.
+
+evaluate:
+  # function for evaluating results.
+  evaluator:
+    name: "ReIDOverAll"  # name of the evaluator.
+

configs/duke_w_tricks.yaml

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+# retrieval settings
+
+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: "/home/songrenjie/projects/reID_baseline/model/ft_ResNet50/res50_duke.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/duke/query"
+  gallery_fea_dir: "/data/features/best_features/duke/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/duke/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"
+

configs/indoor.yaml

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+# retrieval settings
+
+datasets:
+  # number of images in a batch.
+  batch_size: 16
+
+  # function for stacking images in a batch.
+  collate_fn:
+    name: "CollateFn"
+
+  # function for loading images.
+  folder:
+    name: "Folder"
+
+  # a list of data augmentation functions.
+  transformers:
+    names: ["DirectResize", "ToTensor", "Normalize"]  # names of transformers.
+    DirectResize:
+      size: (224, 224)  # target size of the output img.
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "resnet50"  # name of the model.
+  resnet50:
+    load_checkpoint: "/data/places365_model/res50_places365.pt"  # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.
+
+extract:
+  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop).. 0 means concat these features and 1 means sum these features.
+  assemble: 0
+
+  # function for assigning output features.
+  extractor:
+    name: "ResSeries"  # name of the extractor.
+    ResSeries:
+      extract_features: ["pool5"]  # name of the output feature map. If it is ["all"], then all available features will be output.
+
+  # function for splitting the output features (e.g. PCB).
+  splitter:
+    name: "Identity"  # name of the function for splitting features.
+
+  # a list of pooling functions.
+  aggregators:
+    names: ["Crow"]  # names of aggregators.
+
+index:
+  # path of the query set features and gallery set features.
+  query_fea_dir: "/data/features/best_features/indoor/query"
+  gallery_fea_dir: "/data/features/best_features/indoor/gallery"
+
+  # name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
+  # If there are multiple elements in the list, they will be concatenated on the channel-wise.
+  feature_names: ["pool5_Crow"]
+
+  # a list of dimension process functions.
+  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/indoor/gallery"  # path of the features for training PCA.
+      l2: True  # whether do l2-normalization on the training features.
+
+  # function for enhancing the quality of features.
+  feature_enhancer:
+    name: "Identity"  # name of the feature enhancer.
+
+  # function for calculating the distance between query features and gallery features.
+  metric:
+    name: "KNN"  # name of the metric.
+
+  # function for re-ranking the results.
+  re_ranker:
+    name: "Identity"  # name of the re-ranker.
+
+evaluate:
+  # function for evaluating results.
+  evaluator:
+    name: "OverAll"  # name of the evaluator.
+

configs/indoor_w_tricks.yaml

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+datasets:
+  batch_size: 16
+
+  collate_fn:
+    name: "CollateFn"
+
+  folder:
+    name: "Folder"
+
+  transformers:
+    names: ["DirectResize", "ToTensor", "Normalize"]
+    DirectResize:
+      size: (224, 224) #(448, 448) #(224, 224)
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "resnet50"
+  resnet50:
+    load_checkpoint: "/data/places365_model/res50_places365.pt"
+
+extract:
+  assemble: 0
+
+  extractor:
+    name: "ResSeries"
+    ResSeries:
+      extract_features: ["pool5"]
+
+  splitter:
+    name: "Identity"
+
+  aggregators:
+    names: ["Crow"]
+
+index:
+  query_fea_dir: "/data/features/best_features/indoor/query"
+  gallery_fea_dir: "/data/features/best_features/indoor/gallery"
+
+  feature_names: ["pool5_Crow"]
+
+  dim_processors:
+    names: ["L2Normalize", "PCA", "L2Normalize"]
+    PCA:
+      proj_dim: 512
+      whiten: False
+      train_fea_dir: "/data/features/best_features/indoor/gallery"
+      l2: True
+
+  feature_enhancer:
+    name: "DBA"
+    DBA:
+      enhance_k: 10  # number of the nearest points to be calculated.
+
+  metric:
+    name: "KNN"
+
+  re_ranker:
+    name: "QE"
+    QE:
+      qe_times: 1  # number of query expansion times.
+      qe_k: 10  # number of the neighbors to be combined.
+
+evaluate:
+  evaluator:
+    name: "OverAll"
+

configs/market.yaml

@@ -0,0 +1,77 @@
+# retrieval settings
+
+datasets:
+  # number of images in a batch.
+  batch_size: 16
+
+  # function for stacking images in a batch.
+  collate_fn:
+    name: "CollateFn"  # name of the collate_fn.
+
+  # function for loading images.
+  folder:
+    name: "Folder"  # name of the folder.
+
+  # a list of data augmentation functions.
+  transformers:
+    names: ["DirectResize", "TwoFlip", "ToTensor", "Normalize"]  # names of transformers.
+    DirectResize:
+      size: (256, 128)  # target size of the output img.
+      interpolation: 3  # nearest interpolation
+    Normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+model:
+  name: "ft_net"  # name of the model.
+  ft_net:
+    load_checkpoint: "/data/my_model_zoo/res50_market1501.pth"  # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.
+
+extract:
+  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop).. 0 means concat these features and 1 means sum these features.
+  assemble: 1
+
+  # function for assigning output features.
+  extractor:
+    name: "ReIDSeries"  # name of the extractor.
+    ReIDSeries:
+      extract_features: ["output"]  # name of the output feature map. If it is ["all"], then all available features will be output.
+
+  # function for splitting the output features (e.g. PCB).
+  splitter:
+    name: "Identity"  # name of the function for splitting features.
+
+  # a list of pooling functions.
+  aggregators:
+    names: ["GAP"]
+
+index:
+  # path of the query set features and gallery set features.
+  query_fea_dir: "/data/features/best_features/market/query"
+  gallery_fea_dir: "/data/features/best_features/market/gallery"
+
+  # name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
+  # If there are multiple elements in the list, they will be concatenated on the channel-wise.
+  feature_names: ['output']
+
+  # a list of dimension process functions.
+  dim_processors:
+    names: ["L2Normalize"]  # names of dimension processors.
+
+  # function for enhancing the quality of features.
+  feature_enhancer:
+    name: "Identity"  # name of the feature enhancer.
+
+  # function for calculating the distance between query features and gallery features.
+  metric:
+    name: "KNN"  # name of the metric.
+
+  # function for re-ranking the results.
+  re_ranker:
+    name: "Identity"  # name of the re-ranker.
+
+evaluate:
+  # function for evaluating results.
+  evaluator:
+    name: "ReIDOverAll"  # name of the evaluator.
+

configs/market_w_tricks.yaml

@@ -0,0 +1,68 @@
+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"
+

configs/oxford.yaml

@@ -0,0 +1,83 @@
+# retrieval settings
+
+datasets:
+  # number of images in a batch.
+  batch_size: 16
+
+  # function for stacking images in a batch.
+  collate_fn:
+    name: "CollateFn"  # name of the collate_fn.
+
+  # function for loading images.
+  folder:
+    name: "Folder"  # name of the folder.
+
+  # a list of data augmentation functions.
+  transformers:
+    names: ["ShorterResize", "CenterCrop", "ToCaffeTensor", "Normalize"]  # names of transformers.
+    ShorterResize:
+      size: 256  # target size of the shorter edge.
+    CenterCrop:
+      size: 224  # target size of the crop img.
+    Normalize:
+      mean: [104, 116, 124]
+      std: [1.0, 1.0, 1.0]
+
+model:
+  name: "vgg16"  # name of the model.
+  vgg16:
+    load_checkpoint: "/data/places365_model/vgg16_hybrid1365.pt"  # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.
+
+extract:
+  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop). 0 means concat these features and 1 means sum these features.
+  assemble: 0
+
+  # function for assigning output features.
+  extractor:
+    name: "VggSeries"  # name of the extractor.
+    VggSeries:
+      extract_features: ["pool5"]  # name of the output feature map. If it is ["all"], then all available features will be output.
+
+  # function for splitting the output features (e.g. PCB).
+  splitter:
+    name: "Identity"  # name of the function for splitting features.
+
+  # a list of pooling functions.
+  aggregators:
+    names: ["GAP"]  # names of aggregators.
+
+index:
+  # path of the query set features and gallery set features.
+  query_fea_dir: "/data/features/best_features/oxford/query"
+  gallery_fea_dir: "/data/features/best_features/oxford/gallery"
+
+  # name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
+  # If there are multiple elements in the list, they will be concatenated on the channel-wise.
+  feature_names: ["pool5_GAP"]
+
+  # a list of dimension process functions.
+  dim_processors:
+    names: ["L2Normalize", "SVD", "L2Normalize"]
+    SVD:
+      proj_dim: 511  # the dimension after reduction. If it is 0, then no reduction will be done.
+      whiten: True  # whether do whiten when using SVD.
+      train_fea_dir: "/data/features/best_features/paris"  # path of the features for training SVD.
+      l2: True  # whether do l2-normalization on the training features.
+
+  # function for enhancing the quality of features.
+  feature_enhancer:
+    name: "Identity"  # name of the feature enhancer.
+
+  # function for calculating the distance between query features and gallery features.
+  metric:
+    name: "KNN"  # name of the metric.
+
+  # function for re-ranking the results.
+  re_ranker:
+    name: "Identity"  # name of the re-ranker.
+
+evaluate:
+  # function for evaluating results.
+  evaluator:
+    name: "OxfordOverAll"  # name of the evaluator.
+

configs/oxford_w_tricks.yaml

@@ -0,0 +1,69 @@
+datasets:
+  batch_size: 16
+
+  collate_fn:
+    name: "CollateFn"
+
+  folder:
+    name: "Folder"
+
+  transformers:
+    names: ["ShorterResize", "CenterCrop", "ToCaffeTensor", "Normalize"]
+    ShorterResize:
+      size: 256
+    CenterCrop:
+      size: 224
+    Normalize:
+      mean: [104, 116, 124]
+      std: [1.0, 1.0, 1.0]
+
+model:
+  name: "vgg16"
+  vgg16:
+    load_checkpoint: "/data/places365_model/vgg16_hybrid1365.pt"
+
+extract:
+  assemble: 0
+
+  extractor:
+    name: "VggSeries"
+    VggSeries:
+      extract_features: ["pool5"]
+
+  splitter:
+    name: "Identity"
+
+  aggregators:
+    names: ["GAP"]
+
+index:
+  query_fea_dir: "/data/features/best_features/oxford/query"
+  gallery_fea_dir: "/data/features/best_features/oxford/gallery"
+
+  feature_names: ["pool5_GAP"]
+
+  dim_processors:
+    names: ["L2Normalize", "SVD", "L2Normalize"]
+    SVD:
+      proj_dim: 511
+      whiten: True
+      train_fea_dir: "/data/features/best_features/paris"
+      l2: True
+
+  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: "OxfordOverAll"
+

configs/paris.yaml

@@ -0,0 +1,48 @@
+# retrieval settings
+
+datasets:
+  # number of images in a batch.
+  batch_size: 16
+
+  # function for stacking images in a batch.
+  collate_fn:
+    name: "CollateFn"  # name of the collate_fn.
+
+  # function for loading images.
+  folder:
+    name: "Folder"  # name of the folder.
+
+  # a list of data augmentation functions.
+  transformers:
+    names: ["ShorterResize", "CenterCrop", "ToCaffeTensor", "Normalize"]  # names of transformers.
+    ShorterResize:
+      size: 256  # target size of the shorter edge.
+    CenterCrop:
+      size: 224  # target size of the crop img.
+    Normalize:
+      mean: [104, 116, 124]
+      std: [1.0, 1.0, 1.0]
+
+model:
+  name: "vgg16"  # name of the model.
+  vgg16:
+    load_checkpoint: "/data/places365_model/vgg16_hybrid1365.pt"  # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.
+
+extract:
+  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop). 0 means concat these features and 1 means sum these features.
+  assemble: 0
+
+  # function for assigning output features.
+  extractor:
+    name: "VggSeries"  # name of the extractor.
+    VggSeries:
+      extract_features: ["pool5"]  # name of the output feature map. If it is ["all"], then all available features will be output.
+
+  # function for splitting the output features (e.g. PCB).
+  splitter:
+    name: "Identity"  # name of the function for splitting features.
+
+  # a list of pooling functions.
+  aggregators:
+    names: ["GAP"]  # names of aggregators.
+