Index A | B | C | D | E | F | G | H | I | L | M | N | P | Q | R | S | T | V | W A Accuracy (class in mmcls.models.losses) accuracy() (in module mmcls.models.losses) adjust_width_group() (mmcls.models.backbones.RegNet method) AlexNet (class in mmcls.models.backbones) asymmetric_loss() (in module mmcls.models.losses) AsymmetricLoss (class in mmcls.models.losses) Augments (class in mmcls.models.utils) AutoAugment (class in mmcls.datasets.pipelines) AutoContrast (class in mmcls.datasets.pipelines) average_performance() (in module mmcls.core.evaluation) average_precision() (in module mmcls.core.evaluation) B BaseClassifier (class in mmcls.models.classifiers) BaseDataset (class in mmcls.datasets) binary_cross_entropy() (in module mmcls.models.losses) Brightness (class in mmcls.datasets.pipelines) build_backbone() (in module mmcls.models) build_dataloader() (in module mmcls.datasets) build_head() (in module mmcls.models) build_loss() (in module mmcls.models) build_neck() (in module mmcls.models) C calculate_confusion_matrix() (in module mmcls.core.evaluation) CenterCrop (class in mmcls.datasets.pipelines) channel_shuffle() (in module mmcls.models.utils) CIFAR10 (class in mmcls.datasets) CIFAR100 (class in mmcls.datasets) class_to_idx (mmcls.datasets.BaseDataset property) ClassBalancedDataset (class in mmcls.datasets) ClsHead (class in mmcls.models.heads) Collect (class in mmcls.datasets.pipelines) collect_env() (in module mmcls.utils) ColorJitter (class in mmcls.datasets.pipelines) ColorTransform (class in mmcls.datasets.pipelines) Compose (class in mmcls.datasets.pipelines) ConcatDataset (class in mmcls.datasets) Contrast (class in mmcls.datasets.pipelines) convert_to_one_hot() (in module mmcls.models.losses) cross_entropy() (in module mmcls.models.losses) CrossEntropyLoss (class in mmcls.models.losses) Cutout (class in mmcls.datasets.pipelines) D DistEvalHook (class in mmcls.core.evaluation) DistributedSampler (class in mmcls.datasets) E Equalize (class in mmcls.datasets.pipelines) EvalHook (class in mmcls.core.evaluation) evaluate() (mmcls.datasets.BaseDataset method) (mmcls.datasets.MultiLabelDataset method) extract_feat() (mmcls.models.classifiers.ImageClassifier method) F f1_score() (in module mmcls.core.evaluation) FashionMNIST (class in mmcls.datasets) FocalLoss (class in mmcls.models.losses) forward() (mmcls.models.backbones.AlexNet method) (mmcls.models.backbones.LeNet5 method) (mmcls.models.backbones.MlpMixer method) (mmcls.models.backbones.MobileNetV2 method) (mmcls.models.backbones.MobileNetV3 method) (mmcls.models.backbones.RegNet method) (mmcls.models.backbones.RepVGG method) (mmcls.models.backbones.ResNet method) (mmcls.models.backbones.ResNet_CIFAR method) (mmcls.models.backbones.ShuffleNetV1 method) (mmcls.models.backbones.ShuffleNetV2 method) (mmcls.models.backbones.SwinTransformer method) (mmcls.models.backbones.T2T_ViT method) (mmcls.models.backbones.TIMMBackbone method) (mmcls.models.backbones.TNT method) (mmcls.models.backbones.VGG method) (mmcls.models.backbones.VisionTransformer method) (mmcls.models.classifiers.BaseClassifier method) (mmcls.models.losses.Accuracy method) (mmcls.models.losses.AsymmetricLoss method) (mmcls.models.losses.CrossEntropyLoss method) (mmcls.models.losses.FocalLoss method) (mmcls.models.losses.LabelSmoothLoss method) (mmcls.models.losses.SeesawLoss method) (mmcls.models.necks.GlobalAveragePooling method) (mmcls.models.utils.HybridEmbed method) (mmcls.models.utils.InvertedResidual method) (mmcls.models.utils.MultiheadAttention method) (mmcls.models.utils.PatchEmbed method) (mmcls.models.utils.PatchMerging method) (mmcls.models.utils.SELayer method) (mmcls.models.utils.ShiftWindowMSA method) forward_test() (mmcls.models.classifiers.BaseClassifier method) forward_train() (mmcls.models.classifiers.BaseClassifier method) (mmcls.models.classifiers.ImageClassifier method) G generate_one_hot_like_label() (mmcls.models.losses.LabelSmoothLoss method) generate_regnet() (mmcls.models.backbones.RegNet method) get_cat_ids() (mmcls.datasets.BaseDataset method) (mmcls.datasets.ImageNet21k method) (mmcls.datasets.MultiLabelDataset method) get_classes() (mmcls.datasets.BaseDataset class method) get_gt_labels() (mmcls.datasets.BaseDataset method) get_params() (mmcls.datasets.pipelines.RandomCrop static method) (mmcls.datasets.pipelines.RandomResizedCrop static method) get_params_efficientnet_style() (mmcls.datasets.pipelines.RandomResizedCrop static method) get_stages_from_blocks() (mmcls.models.backbones.RegNet method) GlobalAveragePooling (class in mmcls.models.necks) H HybridEmbed (class in mmcls.models.utils) I ImageClassifier (class in mmcls.models.classifiers) ImageNet (class in mmcls.datasets) ImageNet21k (class in mmcls.datasets) inference_model() (in module mmcls.apis) init_model() (in module mmcls.apis) init_weights() (mmcls.models.backbones.ResNet method) (mmcls.models.backbones.ShuffleNetV1 method) (mmcls.models.backbones.ShuffleNetV2 method) (mmcls.models.backbones.SwinTransformer method) (mmcls.models.backbones.T2T_ViT method) (mmcls.models.backbones.VisionTransformer method) (mmcls.models.heads.StackedLinearClsHead method) (mmcls.models.heads.VisionTransformerClsHead method) Invert (class in mmcls.datasets.pipelines) InvertedResidual (class in mmcls.models.utils) L LabelSmoothLoss (class in mmcls.models.losses) LeNet5 (class in mmcls.models.backbones) Lighting (class in mmcls.datasets.pipelines) LinearClsHead (class in mmcls.models.heads) load_annotations() (mmcls.datasets.ImageNet21k method) (mmcls.datasets.VOC method) load_json_logs() (in module mmcls.utils) LoadImageFromFile (class in mmcls.datasets.pipelines) M make_divisible() (in module mmcls.models.utils) make_layer() (mmcls.models.backbones.MobileNetV2 method) (mmcls.models.backbones.ShuffleNetV1 method) mAP() (in module mmcls.core.evaluation) MlpMixer (class in mmcls.models.backbones) mmcls.apis module mmcls.core.evaluation module mmcls.datasets module mmcls.datasets.pipelines module mmcls.models module mmcls.models.backbones module mmcls.models.classifiers module mmcls.models.heads module mmcls.models.losses module mmcls.models.necks module mmcls.models.utils module mmcls.utils module MNIST (class in mmcls.datasets) MobileNetV2 (class in mmcls.models.backbones) MobileNetV3 (class in mmcls.models.backbones) module mmcls.apis mmcls.core.evaluation mmcls.datasets mmcls.datasets.pipelines mmcls.models mmcls.models.backbones mmcls.models.classifiers mmcls.models.heads mmcls.models.losses mmcls.models.necks mmcls.models.utils mmcls.utils multi_gpu_test() (in module mmcls.apis) MultiheadAttention (class in mmcls.models.utils) MultiLabelClsHead (class in mmcls.models.heads) MultiLabelDataset (class in mmcls.datasets) MultiLabelLinearClsHead (class in mmcls.models.heads) N Normalize (class in mmcls.datasets.pipelines) P Pad (class in mmcls.datasets.pipelines) PatchEmbed (class in mmcls.models.utils) PatchMerging (class in mmcls.models.utils) Posterize (class in mmcls.datasets.pipelines) precision() (in module mmcls.core.evaluation) precision_recall_f1() (in module mmcls.core.evaluation) Q quantize_float() (mmcls.models.backbones.RegNet static method) R RandAugment (class in mmcls.datasets.pipelines) RandomCrop (class in mmcls.datasets.pipelines) RandomErasing (class in mmcls.datasets.pipelines) RandomFlip (class in mmcls.datasets.pipelines) RandomGrayscale (class in mmcls.datasets.pipelines) RandomResizedCrop (class in mmcls.datasets.pipelines) recall() (in module mmcls.core.evaluation) reduce_loss() (in module mmcls.models.losses) RegNet (class in mmcls.models.backbones) RepeatDataset (class in mmcls.datasets) RepVGG (class in mmcls.models.backbones) Res2Net (class in mmcls.models.backbones) Resize (class in mmcls.datasets.pipelines) resize_pos_embed() (mmcls.models.backbones.VisionTransformer static method) ResNeSt (class in mmcls.models.backbones) ResNet (class in mmcls.models.backbones) ResNet_CIFAR (class in mmcls.models.backbones) ResNetV1d (class in mmcls.models.backbones) ResNeXt (class in mmcls.models.backbones) Rotate (class in mmcls.datasets.pipelines) S SeesawLoss (class in mmcls.models.losses) SELayer (class in mmcls.models.utils) SEResNet (class in mmcls.models.backbones) SEResNeXt (class in mmcls.models.backbones) set_random_seed() (in module mmcls.apis) Sharpness (class in mmcls.datasets.pipelines) Shear (class in mmcls.datasets.pipelines) ShiftWindowMSA (class in mmcls.models.utils) show_result() (mmcls.models.classifiers.BaseClassifier method) show_result_pyplot() (in module mmcls.apis) ShuffleNetV1 (class in mmcls.models.backbones) ShuffleNetV2 (class in mmcls.models.backbones) sigmoid_focal_loss() (in module mmcls.models.losses) simple_test() (mmcls.models.classifiers.ImageClassifier method) (mmcls.models.heads.ClsHead method) (mmcls.models.heads.LinearClsHead method) (mmcls.models.heads.MultiLabelLinearClsHead method) (mmcls.models.heads.StackedLinearClsHead method) (mmcls.models.heads.VisionTransformerClsHead method) Solarize (class in mmcls.datasets.pipelines) SolarizeAdd (class in mmcls.datasets.pipelines) StackedLinearClsHead (class in mmcls.models.heads) support() (in module mmcls.core.evaluation) SwinTransformer (class in mmcls.models.backbones) T T2T_ViT (class in mmcls.models.backbones) TIMMBackbone (class in mmcls.models.backbones) TNT (class in mmcls.models.backbones) to_tensor() (in module mmcls.datasets.pipelines) train() (mmcls.models.backbones.MobileNetV2 method) (mmcls.models.backbones.MobileNetV3 method) (mmcls.models.backbones.RepVGG method) (mmcls.models.backbones.ResNet method) (mmcls.models.backbones.ShuffleNetV1 method) (mmcls.models.backbones.ShuffleNetV2 method) (mmcls.models.backbones.VGG method) train_step() (mmcls.models.classifiers.BaseClassifier method) Translate (class in mmcls.datasets.pipelines) V val_step() (mmcls.models.classifiers.BaseClassifier method) VGG (class in mmcls.models.backbones) VisionTransformer (class in mmcls.models.backbones) VisionTransformerClsHead (class in mmcls.models.heads) VOC (class in mmcls.datasets) W weight_reduce_loss() (in module mmcls.models.losses) weighted_loss() (in module mmcls.models.losses)