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.

All modules for which code is available

• mmcls.apis.inference
• mmcls.apis.test
• mmcls.apis.train
• mmcls.core.evaluation.eval_metrics
• mmcls.core.evaluation.mean_ap
• mmcls.core.evaluation.multilabel_eval_metrics
• mmcls.core.hook.class_num_check_hook
• mmcls.core.hook.lr_updater
• mmcls.core.hook.precise_bn_hook
• mmcls.core.hook.wandblogger_hook
• mmcls.core.optimizers.lamb
• mmcls.datasets.base_dataset
• mmcls.datasets.cifar
• mmcls.datasets.custom
• mmcls.datasets.dataset_wrappers
• mmcls.datasets.imagenet
• mmcls.datasets.imagenet21k
• mmcls.datasets.mnist
• mmcls.datasets.multi_label
• mmcls.datasets.pipelines.auto_augment
• mmcls.datasets.pipelines.formatting
• mmcls.datasets.pipelines.loading
• mmcls.datasets.pipelines.transforms
• mmcls.datasets.stanford_cars
• mmcls.datasets.voc
• mmcls.models.backbones.alexnet
• mmcls.models.backbones.conformer
• mmcls.models.backbones.convmixer
• mmcls.models.backbones.convnext
• mmcls.models.backbones.cspnet
• mmcls.models.backbones.deit
• mmcls.models.backbones.densenet
• mmcls.models.backbones.efficientformer
• mmcls.models.backbones.efficientnet
• mmcls.models.backbones.hornet
• mmcls.models.backbones.hrnet
• mmcls.models.backbones.lenet
• mmcls.models.backbones.mlp_mixer
• mmcls.models.backbones.mobilenet_v2
• mmcls.models.backbones.mobilenet_v3
• mmcls.models.backbones.poolformer
• mmcls.models.backbones.regnet
• mmcls.models.backbones.repmlp
• mmcls.models.backbones.repvgg
• mmcls.models.backbones.res2net
• mmcls.models.backbones.resnest
• mmcls.models.backbones.resnet
• mmcls.models.backbones.resnet_cifar
• mmcls.models.backbones.resnext
• mmcls.models.backbones.seresnet
• mmcls.models.backbones.seresnext
• mmcls.models.backbones.shufflenet_v1
• mmcls.models.backbones.shufflenet_v2
• mmcls.models.backbones.swin_transformer
• mmcls.models.backbones.t2t_vit
• mmcls.models.backbones.timm_backbone
• mmcls.models.backbones.tnt
• mmcls.models.backbones.twins
• mmcls.models.backbones.van
• mmcls.models.backbones.vgg
• mmcls.models.backbones.vision_transformer
• mmcls.models.builder
• mmcls.models.classifiers.base
• mmcls.models.classifiers.image
• mmcls.models.heads.cls_head
• mmcls.models.heads.conformer_head
• mmcls.models.heads.deit_head
• mmcls.models.heads.linear_head
• mmcls.models.heads.multi_label_head
• mmcls.models.heads.multi_label_linear_head
• mmcls.models.heads.stacked_head
• mmcls.models.heads.vision_transformer_head
• mmcls.models.losses.accuracy
• mmcls.models.losses.asymmetric_loss
• mmcls.models.losses.cross_entropy_loss
• mmcls.models.losses.focal_loss
• mmcls.models.losses.label_smooth_loss
• mmcls.models.losses.seesaw_loss
• mmcls.models.necks.gap
• mmcls.models.necks.gem
• mmcls.models.necks.hr_fuse
• mmcls.models.utils.attention
• mmcls.models.utils.augment.cutmix
• mmcls.models.utils.augment.mixup
• mmcls.models.utils.augment.resizemix
• mmcls.models.utils.channel_shuffle
• mmcls.models.utils.helpers
• mmcls.models.utils.inverted_residual
• mmcls.models.utils.make_divisible
• mmcls.models.utils.position_encoding
• mmcls.models.utils.se_layer
• mmcls.utils.collect_env
• mmcls.utils.logger
• mmcls.utils.setup_env