зеркало из https://github.com/microsoft/torchgeo.git
148 строки
3.0 KiB
ReStructuredText
148 строки
3.0 KiB
ReStructuredText
torchgeo.models
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=================
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.. module:: torchgeo.models
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Change Star
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^^^^^^^^^^^
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.. autoclass:: ChangeStar
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.. autoclass:: ChangeStarFarSeg
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.. autoclass:: ChangeMixin
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CROMA
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^^^^^
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.. autoclass:: CROMA
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.. autofunction:: croma_base
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.. autofunction:: croma_large
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.. autoclass:: CROMABase_Weights
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.. autoclass:: CROMALarge_Weights
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DOFA
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^^^^
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.. autoclass:: DOFA
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.. autofunction:: dofa_small_patch16_224
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.. autofunction:: dofa_base_patch16_224
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.. autofunction:: dofa_large_patch16_224
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.. autofunction:: dofa_huge_patch16_224
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.. autoclass:: DOFABase16_Weights
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.. autoclass:: DOFALarge16_Weights
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FarSeg
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^^^^^^
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.. autoclass:: FarSeg
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Fully-convolutional Network
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autoclass:: FCN
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FC Siamese Networks
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^^^^^^^^^^^^^^^^^^^
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.. autoclass:: FCSiamConc
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.. autoclass:: FCSiamDiff
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RCF Extractor
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^^^^^^^^^^^^^
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.. autoclass:: RCF
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ResNet
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^^^^^^
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.. autofunction:: resnet18
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.. autofunction:: resnet50
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.. autofunction:: resnet152
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.. autoclass:: ResNet18_Weights
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.. autoclass:: ResNet50_Weights
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.. autoclass:: ResNet152_Weights
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Scale-MAE
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^^^^^^^^^
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.. autofunction:: ScaleMAE
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.. autoclass:: ScaleMAELarge16_Weights
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Swin Transformer
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^^^^^^^^^^^^^^^^^^
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.. autofunction:: swin_v2_t
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.. autofunction:: swin_v2_b
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.. autoclass:: Swin_V2_T_Weights
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.. autoclass:: Swin_V2_B_Weights
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Vision Transformer
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^^^^^^^^^^^^^^^^^^
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.. autofunction:: vit_small_patch16_224
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.. autoclass:: ViTSmall16_Weights
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Utility Functions
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^^^^^^^^^^^^^^^^^
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.. autofunction:: get_model
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.. autofunction:: get_model_weights
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.. autofunction:: get_weight
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.. autofunction:: list_models
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Pretrained Weights
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^^^^^^^^^^^^^^^^^^
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TorchGeo provides a number of pre-trained models and backbones, allowing you to perform transfer learning on small datasets without training a new model from scratch or relying on ImageNet weights. Depending on the satellite/sensor where your data comes from, choose from the following pre-trained weights based on which one has the best performance metrics.
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Sensor-Agnostic
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---------------
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These weights can be used with imagery from any satellite/sensor. In addition to the usual performance metrics, there are also additional columns for dynamic spatial (resolution), temporal (time span), and/or spectral (wavelength) support, either via their training data (implicit) or via their model architecture (explicit).
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.. csv-table::
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:widths: 45 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
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:header-rows: 1
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:align: center
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:file: weights/agnostic.csv
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Landsat
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-------
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.. csv-table::
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:widths: 65 10 10 10 10 10 10 10 10 10
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:header-rows: 1
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:align: center
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:file: weights/landsat.csv
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NAIP
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----
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.. csv-table::
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:widths: 45 10 10 10 10
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:header-rows: 1
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:align: center
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:file: weights/naip.csv
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Sentinel-1
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----------
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.. csv-table::
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:widths: 45 10 10 10 10
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:header-rows: 1
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:align: center
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:file: weights/sentinel1.csv
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Sentinel-2
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----------
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.. csv-table::
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:widths: 45 10 10 10 10 15 10 10 10
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:header-rows: 1
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:align: center
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:file: weights/sentinel2.csv
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