mmclassification/docs/en/api/models.rst

142 lines
2.3 KiB
ReStructuredText

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