mirror of
https://github.com/open-mmlab/mmclassification.git
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138 lines
2.3 KiB
ReStructuredText
138 lines
2.3 KiB
ReStructuredText
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.. role:: hidden
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:class: hidden-section
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mmcls.models
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===================================
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The ``models`` package contains several sub-packages for addressing the different components of a model.
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- :ref:`classifiers`: The top-level module which defines the whole process of a classification model.
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- :ref:`backbones`: Usually a feature extraction network, e.g., ResNet, MobileNet.
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- :ref:`necks`: The component between backbones and heads, e.g., GlobalAveragePooling.
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- :ref:`heads`: The component for specific tasks. In MMClassification, we provides heads for classification.
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- :ref:`losses`: Loss functions.
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.. currentmodule:: mmcls.models
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.. autosummary::
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:toctree: generated
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:nosignatures:
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build_classifier
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build_backbone
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build_neck
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build_head
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build_loss
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.. _classifiers:
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Classifier
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------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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:template: classtemplate.rst
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BaseClassifier
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ImageClassifier
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.. _backbones:
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Backbones
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------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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:template: classtemplate.rst
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AlexNet
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CSPDarkNet
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CSPNet
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CSPResNeXt
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CSPResNet
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Conformer
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ConvMixer
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ConvNeXt
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DistilledVisionTransformer
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EfficientNet
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HRNet
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LeNet5
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MlpMixer
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MobileNetV2
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MobileNetV3
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PCPVT
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PoolFormer
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RegNet
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RepMLPNet
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RepVGG
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Res2Net
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ResNeSt
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ResNeXt
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ResNet
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ResNetV1c
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ResNetV1d
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ResNet_CIFAR
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SEResNeXt
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SEResNet
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SVT
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ShuffleNetV1
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ShuffleNetV2
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SwinTransformer
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T2T_ViT
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TIMMBackbone
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TNT
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VGG
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VisionTransformer
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.. _necks:
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Necks
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------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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:template: classtemplate.rst
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GlobalAveragePooling
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GeneralizedMeanPooling
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HRFuseScales
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.. _heads:
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Heads
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------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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:template: classtemplate.rst
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ClsHead
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LinearClsHead
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StackedLinearClsHead
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MultiLabelClsHead
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MultiLabelLinearClsHead
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VisionTransformerClsHead
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DeiTClsHead
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ConformerHead
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.. _losses:
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Losses
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------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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:template: classtemplate.rst
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Accuracy
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AsymmetricLoss
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CrossEntropyLoss
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LabelSmoothLoss
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FocalLoss
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SeesawLoss
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