Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

mmcls.models

The models package contains several sub-packages for addressing the different components of a model.

• Classifier: The top-level module which defines the whole process of a classification model.

• Backbones: Usually a feature extraction network, e.g., ResNet, MobileNet.

• Necks: The component between backbones and heads, e.g., GlobalAveragePooling.

• Heads: The component for specific tasks. In MMClassification, we provides heads for classification.

• Losses: Loss functions.

build_classifier
build_backbone	Build backbone.
build_neck	Build neck.
build_head	Build head.
build_loss	Build loss.

Classifier

BaseClassifier	Base class for classifiers.
ImageClassifier

Backbones

AlexNet	AlexNet backbone.
CSPDarkNet	CSP-Darknet backbone used in YOLOv4.
CSPNet	The abstract CSP Network class.
CSPResNeXt	CSP-ResNeXt backbone.
CSPResNet	CSP-ResNet backbone.
Conformer	Conformer backbone.
ConvMixer	ConvMixer.
ConvNeXt	ConvNeXt.
DenseNet	DenseNet.
DistilledVisionTransformer	Distilled Vision Transformer.
EfficientNet	EfficientNet backbone.
HRNet	HRNet backbone.
LeNet5	LeNet5 backbone.
MlpMixer	Mlp-Mixer backbone.
MobileNetV2	MobileNetV2 backbone.
MobileNetV3	MobileNetV3 backbone.
PCPVT	The backbone of Twins-PCPVT.
PoolFormer	PoolFormer.
RegNet	RegNet backbone.
RepMLPNet	RepMLPNet backbone.
RepVGG	RepVGG backbone.
Res2Net	Res2Net backbone.
ResNeSt	ResNeSt backbone.
ResNeXt	ResNeXt backbone.
ResNet	ResNet backbone.
ResNetV1c	ResNetV1c backbone.
ResNetV1d	ResNetV1d backbone.
ResNet_CIFAR	ResNet backbone for CIFAR.
SEResNeXt	SEResNeXt backbone.
SEResNet	SEResNet backbone.
SVT	The backbone of Twins-SVT.
ShuffleNetV1	ShuffleNetV1 backbone.
ShuffleNetV2	ShuffleNetV2 backbone.
SwinTransformer	Swin Transformer.
T2T_ViT	Tokens-to-Token Vision Transformer (T2T-ViT)
TIMMBackbone	Wrapper to use backbones from timm library.
TNT	Transformer in Transformer.
VAN	Visual Attention Network.
VGG	VGG backbone.
VisionTransformer	Vision Transformer.
EfficientFormer	EfficientFormer.
HorNet	A PyTorch impl of : HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions

Necks

GlobalAveragePooling	Global Average Pooling neck.
GeneralizedMeanPooling	Generalized Mean Pooling neck.
HRFuseScales	Fuse feature map of multiple scales in HRNet.

Heads

ClsHead	classification head.
LinearClsHead	Linear classifier head.
StackedLinearClsHead	Classifier head with several hidden fc layer and a output fc layer.
MultiLabelClsHead	Classification head for multilabel task.
MultiLabelLinearClsHead	Linear classification head for multilabel task.
VisionTransformerClsHead	Vision Transformer classifier head.
DeiTClsHead	Distilled Vision Transformer classifier head.
ConformerHead	Linear classifier head.

Losses

Accuracy
AsymmetricLoss	asymmetric loss.
CrossEntropyLoss	Cross entropy loss.
LabelSmoothLoss	Initializer for the label smoothed cross entropy loss.
FocalLoss	Focal loss.
SeesawLoss	Implementation of seesaw loss.