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miunangel
2025-08-16 20:46:31 +08:00
commit 3dc26db3b9
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7
Dassl.ProGrad.pytorch/configs/datasets/da/cifar_stl.yaml Normal file
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INPUT:
SIZE: (32, 32)
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
DATASET:
NAME: "CIFARSTL"

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Dassl.ProGrad.pytorch/configs/datasets/da/digit5.yaml Normal file
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INPUT:
SIZE: (32, 32)
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
TRANSFORMS: ["normalize"]
DATASET:
NAME: "Digit5"
MODEL:
BACKBONE:
NAME: "cnn_digit5_m3sda"

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Dassl.ProGrad.pytorch/configs/datasets/da/domainnet.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "DomainNet"
MODEL:
BACKBONE:
NAME: "resnet101"

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Dassl.ProGrad.pytorch/configs/datasets/da/mini_domainnet.yaml Normal file
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INPUT:
SIZE: (96, 96)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "miniDomainNet"
MODEL:
BACKBONE:
NAME: "resnet18"

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Dassl.ProGrad.pytorch/configs/datasets/da/office31.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "Office31"
MODEL:
BACKBONE:
NAME: "resnet50"
HEAD:
NAME: "mlp"
HIDDEN_LAYERS: [256]
DROPOUT: 0.

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Dassl.ProGrad.pytorch/configs/datasets/da/office_home.yaml Normal file
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INPUT:
SIZE: (224, 224)
DATASET:
NAME: "OfficeHome"

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Dassl.ProGrad.pytorch/configs/datasets/da/visda17.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "center_crop", "normalize"]
DATASET:
NAME: "VisDA17"
MODEL:
BACKBONE:
NAME: "resnet101"
TEST:
PER_CLASS_RESULT: True

14
Dassl.ProGrad.pytorch/configs/datasets/dg/cifar100_c.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["random_flip", "random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
DATASET:
NAME: "CIFAR100C"
CIFAR_C_TYPE: "fog"
CIFAR_C_LEVEL: 5
MODEL:
BACKBONE:
NAME: "wide_resnet_16_4"

14
Dassl.ProGrad.pytorch/configs/datasets/dg/cifar10_c.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["random_flip", "random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
DATASET:
NAME: "CIFAR10C"
CIFAR_C_TYPE: "fog"
CIFAR_C_LEVEL: 5
MODEL:
BACKBONE:
NAME: "wide_resnet_16_4"

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Dassl.ProGrad.pytorch/configs/datasets/dg/digit_single.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
DATASET:
NAME: "DigitSingle"
MODEL:
BACKBONE:
NAME: "cnn_digitsingle"

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Dassl.ProGrad.pytorch/configs/datasets/dg/digits_dg.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
DATASET:
NAME: "DigitsDG"
MODEL:
BACKBONE:
NAME: "cnn_digitsdg"

11
Dassl.ProGrad.pytorch/configs/datasets/dg/office_home_dg.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "OfficeHomeDG"
MODEL:
BACKBONE:
NAME: "resnet18"
PRETRAINED: True

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Dassl.ProGrad.pytorch/configs/datasets/dg/pacs.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "PACS"
MODEL:
BACKBONE:
NAME: "resnet18"
PRETRAINED: True

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Dassl.ProGrad.pytorch/configs/datasets/dg/vlcs.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "VLCS"
MODEL:
BACKBONE:
NAME: "resnet18"
PRETRAINED: True

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Dassl.ProGrad.pytorch/configs/datasets/ssl/cifar10.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["random_flip", "random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
DATASET:
NAME: "CIFAR10"
NUM_LABELED: 4000
VAL_PERCENT: 0.
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"

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Dassl.ProGrad.pytorch/configs/datasets/ssl/cifar100.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["random_flip", "random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
CROP_PADDING: 4
DATASET:
NAME: "CIFAR100"
NUM_LABELED: 10000
VAL_PERCENT: 0.
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"

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Dassl.ProGrad.pytorch/configs/datasets/ssl/stl10.yaml Normal file
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INPUT:
SIZE: (96, 96)
TRANSFORMS: ["random_flip", "random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
CROP_PADDING: 4
DATASET:
NAME: "STL10"
STL10_FOLD: 0
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"

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Dassl.ProGrad.pytorch/configs/datasets/ssl/svhn.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
CROP_PADDING: 4
DATASET:
NAME: "SVHN"
NUM_LABELED: 1000
VAL_PERCENT: 0.
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"