├── .gitignore
├── LICENSE
├── README.md
├── build
└── lib
│ └── mmcls
│ ├── __init__.py
│ ├── apis
│ ├── __init__.py
│ ├── inference.py
│ └── model.py
│ ├── datasets
│ ├── __init__.py
│ ├── base_dataset.py
│ ├── builder.py
│ ├── categories.py
│ ├── cifar.py
│ ├── cub.py
│ ├── custom.py
│ ├── dataset_wrappers.py
│ ├── imagenet.py
│ ├── inshop.py
│ ├── mnist.py
│ ├── multi_label.py
│ ├── multi_task.py
│ ├── samplers
│ │ ├── __init__.py
│ │ └── repeat_aug.py
│ ├── transforms
│ │ ├── __init__.py
│ │ ├── auto_augment.py
│ │ ├── formatting.py
│ │ └── processing.py
│ ├── utils.py
│ └── voc.py
│ ├── engine
│ ├── __init__.py
│ ├── hooks
│ │ ├── __init__.py
│ │ ├── class_num_check_hook.py
│ │ ├── ema_hook.py
│ │ ├── margin_head_hooks.py
│ │ ├── precise_bn_hook.py
│ │ ├── retriever_hooks.py
│ │ ├── switch_recipe_hook.py
│ │ └── visualization_hook.py
│ └── optimizers
│ │ ├── __init__.py
│ │ ├── adan_t.py
│ │ └── lamb.py
│ ├── evaluation
│ ├── __init__.py
│ └── metrics
│ │ ├── __init__.py
│ │ ├── corruption_error.py
│ │ ├── multi_label.py
│ │ ├── multi_task.py
│ │ ├── retrieval.py
│ │ ├── single_label.py
│ │ └── voc_multi_label.py
│ ├── models
│ ├── __init__.py
│ ├── backbones
│ │ ├── __init__.py
│ │ ├── alexnet.py
│ │ ├── base_backbone.py
│ │ ├── beit.py
│ │ ├── conformer.py
│ │ ├── convmixer.py
│ │ ├── convnext.py
│ │ ├── cspnet.py
│ │ ├── davit.py
│ │ ├── deit.py
│ │ ├── deit3.py
│ │ ├── densenet.py
│ │ ├── edgenext.py
│ │ ├── efficientformer.py
│ │ ├── efficientnet.py
│ │ ├── efficientnet_v2.py
│ │ ├── hornet.py
│ │ ├── hrnet.py
│ │ ├── inception_v3.py
│ │ ├── lenet.py
│ │ ├── levit.py
│ │ ├── mixmim.py
│ │ ├── mlp_mixer.py
│ │ ├── mobilenet_v1.py
│ │ ├── mobilenet_v2.py
│ │ ├── mobilenet_v3.py
│ │ ├── mobileone.py
│ │ ├── mobilevit.py
│ │ ├── mvit.py
│ │ ├── poolformer.py
│ │ ├── regnet.py
│ │ ├── replknet.py
│ │ ├── repmlp.py
│ │ ├── repvgg.py
│ │ ├── res2net.py
│ │ ├── resnest.py
│ │ ├── resnet.py
│ │ ├── resnet_cifar.py
│ │ ├── resnext.py
│ │ ├── revvit.py
│ │ ├── seresnet.py
│ │ ├── seresnext.py
│ │ ├── shufflenet_v1.py
│ │ ├── shufflenet_v2.py
│ │ ├── swin_transformer.py
│ │ ├── swin_transformer_v2.py
│ │ ├── t2t_vit.py
│ │ ├── timm_backbone.py
│ │ ├── tinyvit.py
│ │ ├── tnt.py
│ │ ├── twins.py
│ │ ├── van.py
│ │ ├── vgg.py
│ │ ├── vig.py
│ │ ├── vision_transformer.py
│ │ └── xcit.py
│ ├── builder.py
│ ├── classifiers
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── distiller.py
│ │ ├── hugging_face.py
│ │ ├── image.py
│ │ ├── multi_layer_distiller.py
│ │ └── timm.py
│ ├── dis_losses
│ │ ├── __init__.py
│ │ ├── attention.py
│ │ ├── fd.py
│ │ ├── kd.py
│ │ ├── mfd.py
│ │ ├── mgd.py
│ │ ├── old_version_loss.py
│ │ ├── tcd.py
│ │ ├── tfd.py
│ │ └── vitkd.py
│ ├── heads
│ │ ├── __init__.py
│ │ ├── base_head.py
│ │ ├── cls_head.py
│ │ ├── conformer_head.py
│ │ ├── deit_head.py
│ │ ├── efficientformer_head.py
│ │ ├── levit_head.py
│ │ ├── linear_head.py
│ │ ├── margin_head.py
│ │ ├── multi_label_cls_head.py
│ │ ├── multi_label_csra_head.py
│ │ ├── multi_label_linear_head.py
│ │ ├── multi_task_head.py
│ │ ├── stacked_head.py
│ │ ├── vig_head.py
│ │ └── vision_transformer_head.py
│ ├── losses
│ │ ├── __init__.py
│ │ ├── asymmetric_loss.py
│ │ ├── cross_entropy_loss.py
│ │ ├── focal_loss.py
│ │ ├── label_smooth_loss.py
│ │ ├── seesaw_loss.py
│ │ └── utils.py
│ ├── necks
│ │ ├── __init__.py
│ │ ├── gap.py
│ │ ├── gem.py
│ │ ├── hr_fuse.py
│ │ └── reduction.py
│ ├── retrievers
│ │ ├── __init__.py
│ │ ├── base.py
│ │ └── image2image.py
│ ├── tta
│ │ ├── __init__.py
│ │ └── score_tta.py
│ └── utils
│ │ ├── __init__.py
│ │ ├── attention.py
│ │ ├── batch_augments
│ │ ├── __init__.py
│ │ ├── cutmix.py
│ │ ├── mixup.py
│ │ ├── resizemix.py
│ │ └── wrapper.py
│ │ ├── channel_shuffle.py
│ │ ├── data_preprocessor.py
│ │ ├── embed.py
│ │ ├── helpers.py
│ │ ├── inverted_residual.py
│ │ ├── layer_scale.py
│ │ ├── make_divisible.py
│ │ ├── norm.py
│ │ ├── position_encoding.py
│ │ └── se_layer.py
│ ├── registry.py
│ ├── structures
│ ├── __init__.py
│ ├── cls_data_sample.py
│ ├── multi_task_data_sample.py
│ └── utils.py
│ ├── utils
│ ├── __init__.py
│ ├── analyze.py
│ ├── collect_env.py
│ ├── progress.py
│ └── setup_env.py
│ ├── version.py
│ └── visualization
│ ├── __init__.py
│ └── cls_visualizer.py
├── configs
├── _base_
│ ├── datasets
│ │ ├── cifar100_bs16.py
│ │ ├── cifar10_bs16.py
│ │ ├── cub_bs8_384.py
│ │ ├── cub_bs8_448.py
│ │ ├── imagenet21k_bs128.py
│ │ ├── imagenet_bs128_mbv3.py
│ │ ├── imagenet_bs128_mlp_224.py
│ │ ├── imagenet_bs128_poolformer_medium_224.py
│ │ ├── imagenet_bs128_poolformer_small_224.py
│ │ ├── imagenet_bs128_revvit_224.py
│ │ ├── imagenet_bs128_swin_224.py
│ │ ├── imagenet_bs128_vig_224.py
│ │ ├── imagenet_bs16_eva02_336.py
│ │ ├── imagenet_bs16_eva_196.py
│ │ ├── imagenet_bs16_eva_224.py
│ │ ├── imagenet_bs16_eva_336.py
│ │ ├── imagenet_bs16_eva_560.py
│ │ ├── imagenet_bs16_pil_bicubic_384.py
│ │ ├── imagenet_bs256_davit_224.py
│ │ ├── imagenet_bs256_levit_224.py
│ │ ├── imagenet_bs256_resnet_224.py
│ │ ├── imagenet_bs256_rsb_a12.py
│ │ ├── imagenet_bs256_rsb_a3.py
│ │ ├── imagenet_bs32.py
│ │ ├── imagenet_bs32_pil_bicubic.py
│ │ ├── imagenet_bs32_pil_resize.py
│ │ ├── imagenet_bs32_swin_224.py
│ │ ├── imagenet_bs64.py
│ │ ├── imagenet_bs64_autoaug.py
│ │ ├── imagenet_bs64_clip_224.py
│ │ ├── imagenet_bs64_clip_384.py
│ │ ├── imagenet_bs64_clip_448.py
│ │ ├── imagenet_bs64_convmixer_224.py
│ │ ├── imagenet_bs64_deit3_224.py
│ │ ├── imagenet_bs64_deit3_384.py
│ │ ├── imagenet_bs64_edgenext_256.py
│ │ ├── imagenet_bs64_efficient_224.py
│ │ ├── imagenet_bs64_mbv1_224.py
│ │ ├── imagenet_bs64_mixer_224.py
│ │ ├── imagenet_bs64_pil_resize.py
│ │ ├── imagenet_bs64_pil_resize_autoaug.py
│ │ ├── imagenet_bs64_swin_224.py
│ │ ├── imagenet_bs64_swin_256.py
│ │ ├── imagenet_bs64_swin_384.py
│ │ ├── imagenet_bs64_t2t_224.py
│ │ ├── imagenet_bs8_pil_bicubic_320.py
│ │ ├── inshop_bs32_448.py
│ │ ├── pipelines
│ │ │ ├── auto_aug.py
│ │ │ └── rand_aug.py
│ │ └── voc_bs16.py
│ ├── default_runtime.py
│ ├── models
│ │ ├── bit_resnet50.py
│ │ ├── conformer
│ │ │ ├── base-p16.py
│ │ │ ├── small-p16.py
│ │ │ ├── small-p32.py
│ │ │ └── tiny-p16.py
│ │ ├── convmixer
│ │ │ ├── convmixer-1024-20.py
│ │ │ ├── convmixer-1536-20.py
│ │ │ └── convmixer-768-32.py
│ │ ├── convnext
│ │ │ ├── convnext-base.py
│ │ │ ├── convnext-large.py
│ │ │ ├── convnext-small.py
│ │ │ ├── convnext-tiny.py
│ │ │ └── convnext-xlarge.py
│ │ ├── convnext_v2
│ │ │ ├── atto.py
│ │ │ ├── base.py
│ │ │ ├── femto.py
│ │ │ ├── huge.py
│ │ │ ├── large.py
│ │ │ ├── nano.py
│ │ │ ├── pico.py
│ │ │ └── tiny.py
│ │ ├── davit
│ │ │ ├── davit-base.py
│ │ │ ├── davit-small.py
│ │ │ └── davit-tiny.py
│ │ ├── deit3
│ │ │ ├── deit3-base-p16-224.py
│ │ │ ├── deit3-base-p16-384.py
│ │ │ ├── deit3-huge-p14-224.py
│ │ │ ├── deit3-large-p16-224.py
│ │ │ ├── deit3-large-p16-384.py
│ │ │ ├── deit3-medium-p16-224.py
│ │ │ ├── deit3-small-p16-224.py
│ │ │ └── deit3-small-p16-384.py
│ │ ├── densenet
│ │ │ ├── densenet121.py
│ │ │ ├── densenet161.py
│ │ │ ├── densenet169.py
│ │ │ └── densenet201.py
│ │ ├── edgenext
│ │ │ ├── edgenext-base.py
│ │ │ ├── edgenext-small.py
│ │ │ ├── edgenext-xsmall.py
│ │ │ └── edgenext-xxsmall.py
│ │ ├── efficientformer-l1.py
│ │ ├── efficientnet_b0.py
│ │ ├── efficientnet_b1.py
│ │ ├── efficientnet_b2.py
│ │ ├── efficientnet_b3.py
│ │ ├── efficientnet_b4.py
│ │ ├── efficientnet_b5.py
│ │ ├── efficientnet_b6.py
│ │ ├── efficientnet_b7.py
│ │ ├── efficientnet_b8.py
│ │ ├── efficientnet_em.py
│ │ ├── efficientnet_es.py
│ │ ├── efficientnet_l2.py
│ │ ├── efficientnet_v2
│ │ │ ├── efficientnetv2_b0.py
│ │ │ ├── efficientnetv2_b1.py
│ │ │ ├── efficientnetv2_b2.py
│ │ │ ├── efficientnetv2_b3.py
│ │ │ ├── efficientnetv2_l.py
│ │ │ ├── efficientnetv2_m.py
│ │ │ ├── efficientnetv2_s.py
│ │ │ └── efficientnetv2_xl.py
│ │ ├── eva
│ │ │ ├── eva-g.py
│ │ │ ├── eva-l.py
│ │ │ └── vit-b-eva.py
│ │ ├── hornet
│ │ │ ├── hornet-base-gf.py
│ │ │ ├── hornet-base.py
│ │ │ ├── hornet-large-gf.py
│ │ │ ├── hornet-large-gf384.py
│ │ │ ├── hornet-large.py
│ │ │ ├── hornet-small-gf.py
│ │ │ ├── hornet-small.py
│ │ │ ├── hornet-tiny-gf.py
│ │ │ └── hornet-tiny.py
│ │ ├── hrnet
│ │ │ ├── hrnet-w18.py
│ │ │ ├── hrnet-w30.py
│ │ │ ├── hrnet-w32.py
│ │ │ ├── hrnet-w40.py
│ │ │ ├── hrnet-w44.py
│ │ │ ├── hrnet-w48.py
│ │ │ └── hrnet-w64.py
│ │ ├── inception_v3.py
│ │ ├── levit-256-p16.py
│ │ ├── mixmim
│ │ │ └── mixmim_base.py
│ │ ├── mlp_mixer_base_patch14.py
│ │ ├── mlp_mixer_base_patch16.py
│ │ ├── mlp_mixer_large_patch16.py
│ │ ├── mlp_mixer_small_patch16.py
│ │ ├── mobilenet_v1_1x.py
│ │ ├── mobilenet_v2_1x.py
│ │ ├── mobilenet_v3
│ │ │ ├── mobilenet_v3_large_imagenet.py
│ │ │ ├── mobilenet_v3_small_050_imagenet.py
│ │ │ ├── mobilenet_v3_small_075_imagenet.py
│ │ │ ├── mobilenet_v3_small_cifar.py
│ │ │ └── mobilenet_v3_small_imagenet.py
│ │ ├── mobileone
│ │ │ ├── mobileone_s0.py
│ │ │ ├── mobileone_s1.py
│ │ │ ├── mobileone_s2.py
│ │ │ ├── mobileone_s3.py
│ │ │ └── mobileone_s4.py
│ │ ├── mobilevit
│ │ │ ├── mobilevit_s.py
│ │ │ ├── mobilevit_xs.py
│ │ │ └── mobilevit_xxs.py
│ │ ├── mvit
│ │ │ ├── mvitv2-base.py
│ │ │ ├── mvitv2-large.py
│ │ │ ├── mvitv2-small.py
│ │ │ └── mvitv2-tiny.py
│ │ ├── poolformer
│ │ │ ├── poolformer_m36.py
│ │ │ ├── poolformer_m48.py
│ │ │ ├── poolformer_s12.py
│ │ │ ├── poolformer_s24.py
│ │ │ └── poolformer_s36.py
│ │ ├── regnet
│ │ │ ├── regnetx_1.6gf.py
│ │ │ ├── regnetx_12gf.py
│ │ │ ├── regnetx_3.2gf.py
│ │ │ ├── regnetx_4.0gf.py
│ │ │ ├── regnetx_400mf.py
│ │ │ ├── regnetx_6.4gf.py
│ │ │ ├── regnetx_8.0gf.py
│ │ │ └── regnetx_800mf.py
│ │ ├── replknet-31B_in1k.py
│ │ ├── replknet-31L_in1k.py
│ │ ├── replknet-XL_in1k.py
│ │ ├── repmlp-base_224.py
│ │ ├── repvgg-A0_in1k.py
│ │ ├── repvgg-B3_lbs-mixup_in1k.py
│ │ ├── res2net101-w26-s4.py
│ │ ├── res2net50-w14-s8.py
│ │ ├── res2net50-w26-s4.py
│ │ ├── res2net50-w26-s6.py
│ │ ├── res2net50-w26-s8.py
│ │ ├── res2net50-w48-s2.py
│ │ ├── resnest101.py
│ │ ├── resnest200.py
│ │ ├── resnest269.py
│ │ ├── resnest50.py
│ │ ├── resnet101.py
│ │ ├── resnet101_cifar.py
│ │ ├── resnet152.py
│ │ ├── resnet152_cifar.py
│ │ ├── resnet152d_strong_aug.py
│ │ ├── resnet18.py
│ │ ├── resnet18_cifar.py
│ │ ├── resnet34.py
│ │ ├── resnet34_cifar.py
│ │ ├── resnet34_gem.py
│ │ ├── resnet50.py
│ │ ├── resnet50_cifar.py
│ │ ├── resnet50_cifar_cutmix.py
│ │ ├── resnet50_cifar_mixup.py
│ │ ├── resnet50_cutmix.py
│ │ ├── resnet50_label_smooth.py
│ │ ├── resnet50_mixup.py
│ │ ├── resnet50_strong_aug.py
│ │ ├── resnet50d_strong_aug.py
│ │ ├── resnetv1c50.py
│ │ ├── resnetv1d101.py
│ │ ├── resnetv1d152.py
│ │ ├── resnetv1d50.py
│ │ ├── resnext101_32x4d.py
│ │ ├── resnext101_32x8d.py
│ │ ├── resnext152_32x4d.py
│ │ ├── resnext50_32x4d.py
│ │ ├── revvit
│ │ │ ├── revvit-base.py
│ │ │ └── revvit-small.py
│ │ ├── seresnet101.py
│ │ ├── seresnet50.py
│ │ ├── seresnext101_32x4d.py
│ │ ├── seresnext50_32x4d.py
│ │ ├── shufflenet_v1_1x.py
│ │ ├── shufflenet_v2_1x.py
│ │ ├── swin_transformer
│ │ │ ├── base_224.py
│ │ │ ├── base_384.py
│ │ │ ├── large_224.py
│ │ │ ├── large_384.py
│ │ │ ├── small_224.py
│ │ │ └── tiny_224.py
│ │ ├── swin_transformer_v2
│ │ │ ├── base_256.py
│ │ │ ├── base_384.py
│ │ │ ├── large_256.py
│ │ │ ├── large_384.py
│ │ │ ├── small_256.py
│ │ │ └── tiny_256.py
│ │ ├── t2t-vit-t-14.py
│ │ ├── t2t-vit-t-19.py
│ │ ├── t2t-vit-t-24.py
│ │ ├── tinyvit
│ │ │ ├── tinyvit-11m.py
│ │ │ ├── tinyvit-21m.py
│ │ │ └── tinyvit-5m.py
│ │ ├── tnt_s_patch16_224.py
│ │ ├── twins_pcpvt_base.py
│ │ ├── twins_svt_base.py
│ │ ├── van
│ │ │ ├── van_base.py
│ │ │ ├── van_large.py
│ │ │ ├── van_small.py
│ │ │ └── van_tiny.py
│ │ ├── vgg11.py
│ │ ├── vgg11bn.py
│ │ ├── vgg13.py
│ │ ├── vgg13bn.py
│ │ ├── vgg16.py
│ │ ├── vgg16bn.py
│ │ ├── vgg19.py
│ │ ├── vgg19bn.py
│ │ ├── vig
│ │ │ ├── pyramid_vig_base.py
│ │ │ ├── pyramid_vig_medium.py
│ │ │ ├── pyramid_vig_small.py
│ │ │ ├── pyramid_vig_tiny.py
│ │ │ ├── vig_base.py
│ │ │ ├── vig_small.py
│ │ │ └── vig_tiny.py
│ │ ├── vit-base-p16.py
│ │ ├── vit-base-p32.py
│ │ ├── vit-large-p16.py
│ │ ├── vit-large-p32.py
│ │ └── wide-resnet50.py
│ └── schedules
│ │ ├── cifar10_bs128.py
│ │ ├── cub_bs64.py
│ │ ├── imagenet_bs1024_AdamW_mlp.py
│ │ ├── imagenet_bs1024_adamw_conformer.py
│ │ ├── imagenet_bs1024_adamw_eva02.py
│ │ ├── imagenet_bs1024_adamw_resnet.py
│ │ ├── imagenet_bs1024_adamw_revvit.py
│ │ ├── imagenet_bs1024_adamw_swin.py
│ │ ├── imagenet_bs1024_coslr.py
│ │ ├── imagenet_bs1024_linearlr_bn_nowd.py
│ │ ├── imagenet_bs2048.py
│ │ ├── imagenet_bs2048_AdamW.py
│ │ ├── imagenet_bs2048_adamw_levit.py
│ │ ├── imagenet_bs2048_coslr.py
│ │ ├── imagenet_bs2048_coslr_Lamb.py
│ │ ├── imagenet_bs2048_rsb.py
│ │ ├── imagenet_bs256.py
│ │ ├── imagenet_bs256_140e.py
│ │ ├── imagenet_bs256_200e_coslr_warmup.py
│ │ ├── imagenet_bs256_cos_lr_SGD_150e.py
│ │ ├── imagenet_bs256_coslr.py
│ │ ├── imagenet_bs256_coslr_coswd_300e.py
│ │ ├── imagenet_bs256_epochstep.py
│ │ ├── imagenet_bs4096_AdamW.py
│ │ └── imagenet_bs512_coslr.py
├── arcface
│ ├── README.md
│ ├── metafile.yml
│ └── resnet50-arcface_8xb32_inshop.py
├── beit
│ ├── README.md
│ ├── beit-base-p16_8xb64_in1k.py
│ └── metafile.yml
├── beitv2
│ ├── README.md
│ ├── beitv2-base-p16_8xb64_in1k.py
│ └── metafile.yml
├── clip
│ ├── README.md
│ ├── metafile.yml
│ ├── vit-base-p16_pt-64xb64_in1k-384px.py
│ ├── vit-base-p16_pt-64xb64_in1k-448px.py
│ ├── vit-base-p16_pt-64xb64_in1k.py
│ ├── vit-base-p32_pt-64xb64_in1k-384px.py
│ ├── vit-base-p32_pt-64xb64_in1k-448px.py
│ └── vit-base-p32_pt-64xb64_in1k.py
├── conformer
│ ├── README.md
│ ├── conformer-base-p16_8xb128_in1k.py
│ ├── conformer-small-p16_8xb128_in1k.py
│ ├── conformer-small-p32_8xb128_in1k.py
│ ├── conformer-tiny-p16_8xb128_in1k.py
│ └── metafile.yml
├── convmixer
│ ├── README.md
│ ├── convmixer-1024-20_10xb64_in1k.py
│ ├── convmixer-1536-20_10xb64_in1k.py
│ ├── convmixer-768-32_10xb64_in1k.py
│ └── metafile.yml
├── convnext
│ ├── README.md
│ ├── convnext-base_32xb128_in1k-384px.py
│ ├── convnext-base_32xb128_in1k.py
│ ├── convnext-base_32xb128_in21k.py
│ ├── convnext-large_64xb64_in1k-384px.py
│ ├── convnext-large_64xb64_in1k.py
│ ├── convnext-large_64xb64_in21k.py
│ ├── convnext-small_32xb128_in1k-384px.py
│ ├── convnext-small_32xb128_in1k.py
│ ├── convnext-tiny_32xb128_in1k-384px.py
│ ├── convnext-tiny_32xb128_in1k.py
│ ├── convnext-xlarge_64xb64_in1k-384px.py
│ ├── convnext-xlarge_64xb64_in1k.py
│ ├── convnext-xlarge_64xb64_in21k.py
│ └── metafile.yml
├── convnext_v2
│ ├── README.md
│ ├── convnext-v2-atto_32xb32_in1k.py
│ ├── convnext-v2-base_32xb32_in1k-384px.py
│ ├── convnext-v2-base_32xb32_in1k.py
│ ├── convnext-v2-femto_32xb32_in1k.py
│ ├── convnext-v2-huge_32xb32_in1k-384px.py
│ ├── convnext-v2-huge_32xb32_in1k-512px.py
│ ├── convnext-v2-huge_32xb32_in1k.py
│ ├── convnext-v2-large_32xb32_in1k-384px.py
│ ├── convnext-v2-large_32xb32_in1k.py
│ ├── convnext-v2-nano_32xb32_in1k-384px.py
│ ├── convnext-v2-nano_32xb32_in1k.py
│ ├── convnext-v2-pico_32xb32_in1k.py
│ ├── convnext-v2-tiny_32xb32_in1k-384px.py
│ ├── convnext-v2-tiny_32xb32_in1k.py
│ └── metafile.yml
├── cspnet
│ ├── README.md
│ ├── cspdarknet50_8xb32_in1k.py
│ ├── cspresnet50_8xb32_in1k.py
│ ├── cspresnext50_8xb32_in1k.py
│ └── metafile.yml
├── csra
│ ├── README.md
│ ├── metafile.yml
│ └── resnet101-csra_1xb16_voc07-448px.py
├── davit
│ ├── README.md
│ ├── davit-base_4xb256_in1k.py
│ ├── davit-small_4xb256_in1k.py
│ ├── davit-tiny_4xb256_in1k.py
│ └── metafile.yml
├── deit
│ ├── README.md
│ ├── deit-base-distilled_ft-16xb32_in1k-384px.py
│ ├── deit-base-distilled_pt-16xb64_in1k.py
│ ├── deit-base_ft-16xb32_in1k-384px.py
│ ├── deit-base_pt-16xb64_in1k.py
│ ├── deit-base_pt-4xb256_in1k.py
│ ├── deit-base_pt-4xb256_p14_in1k.py
│ ├── deit-small-distilled_pt-4xb256_in1k.py
│ ├── deit-small_pt-4xb256_in1k.py
│ ├── deit-small_pt-4xb256_in1k_with_token.py
│ ├── deit-small_pt-4xb256_p16_in1k.py
│ ├── deit-tiny-distilled_pt-4xb256_in1k.py
│ ├── deit-tiny_pt-4xb256_in1k.py
│ ├── deit-tiny_pt-4xb256_in1k_with_token.py
│ └── metafile.yml
├── deit3
│ ├── README.md
│ ├── deit3-base-p16_64xb32_in1k-384px.py
│ ├── deit3-base-p16_64xb64_in1k.py
│ ├── deit3-huge-p14_64xb32_in1k.py
│ ├── deit3-large-p16_64xb16_in1k-384px.py
│ ├── deit3-large-p16_64xb64_in1k.py
│ ├── deit3-medium-p16_64xb64_in1k.py
│ ├── deit3-small-p16_64xb64_in1k-384px.py
│ ├── deit3-small-p16_64xb64_in1k.py
│ └── metafile.yml
├── densenet
│ ├── README.md
│ ├── densenet121_4xb256_in1k.py
│ ├── densenet161_4xb256_in1k.py
│ ├── densenet169_4xb256_in1k.py
│ ├── densenet201_4xb256_in1k.py
│ └── metafile.yml
├── distillers
│ ├── advanced_training_strategy
│ │ ├── swin-l_distill_deit-b_img_s3_s4.py
│ │ └── swin-l_distill_res50_img_s3_s4.py
│ └── traditional_traning_strategy
│ │ ├── swin-b_distill_res50_img_s3_s4.py
│ │ ├── swin-l_distill_mbv1_img_s3_s4.py
│ │ ├── swin-l_distill_res50_img_s3_s4.py
│ │ └── swin-s_distill_res50_img_s3_s4.py
├── edgenext
│ ├── README.md
│ ├── edgenext-base_8xb256-usi_in1k.py
│ ├── edgenext-base_8xb256_in1k.py
│ ├── edgenext-small_8xb256-usi_in1k.py
│ ├── edgenext-small_8xb256_in1k.py
│ ├── edgenext-xsmall_8xb256_in1k.py
│ ├── edgenext-xxsmall_8xb256_in1k.py
│ └── metafile.yml
├── efficientformer
│ ├── README.md
│ ├── efficientformer-l1_8xb128_in1k.py
│ ├── efficientformer-l3_8xb128_in1k.py
│ ├── efficientformer-l7_8xb128_in1k.py
│ └── metafile.yml
├── efficientnet
│ ├── README.md
│ ├── efficientnet-b0_8xb32-01norm_in1k.py
│ ├── efficientnet-b0_8xb32_in1k.py
│ ├── efficientnet-b1_8xb32-01norm_in1k.py
│ ├── efficientnet-b1_8xb32_in1k.py
│ ├── efficientnet-b2_8xb32-01norm_in1k.py
│ ├── efficientnet-b2_8xb32_in1k.py
│ ├── efficientnet-b3_8xb32-01norm_in1k.py
│ ├── efficientnet-b3_8xb32_in1k.py
│ ├── efficientnet-b4_8xb32-01norm_in1k.py
│ ├── efficientnet-b4_8xb32_in1k.py
│ ├── efficientnet-b5_8xb32-01norm_in1k.py
│ ├── efficientnet-b5_8xb32_in1k.py
│ ├── efficientnet-b6_8xb32-01norm_in1k.py
│ ├── efficientnet-b6_8xb32_in1k.py
│ ├── efficientnet-b7_8xb32-01norm_in1k.py
│ ├── efficientnet-b7_8xb32_in1k.py
│ ├── efficientnet-b8_8xb32-01norm_in1k.py
│ ├── efficientnet-b8_8xb32_in1k.py
│ ├── efficientnet-em_8xb32-01norm_in1k.py
│ ├── efficientnet-es_8xb32-01norm_in1k.py
│ ├── efficientnet-l2_8xb32_in1k-475px.py
│ ├── efficientnet-l2_8xb8_in1k-800px.py
│ └── metafile.yml
├── efficientnet_v2
│ ├── README.md
│ ├── efficientnetv2-b0_8xb32_in1k.py
│ ├── efficientnetv2-b1_8xb32_in1k.py
│ ├── efficientnetv2-b2_8xb32_in1k.py
│ ├── efficientnetv2-b3_8xb32_in1k.py
│ ├── efficientnetv2-l_8xb32_in1k-480px.py
│ ├── efficientnetv2-l_8xb32_in21k.py
│ ├── efficientnetv2-m_8xb32_in1k-480px.py
│ ├── efficientnetv2-m_8xb32_in21k.py
│ ├── efficientnetv2-s_8xb32_in1k-384px.py
│ ├── efficientnetv2-s_8xb32_in21k.py
│ ├── efficientnetv2-xl_8xb32_in1k-512px.py
│ ├── efficientnetv2-xl_8xb32_in21k.py
│ └── metafile.yml
├── eva
│ ├── README.md
│ ├── eva-02-b_in1k-224px.py
│ ├── eva-02-s_in1k-336px.py
│ ├── eva-g-p14_8xb16_in1k-336px.py
│ ├── eva-g-p14_8xb16_in1k-560px.py
│ ├── eva-g-p14_headless.py
│ ├── eva-g-p16_headless.py
│ ├── eva-l-p14_8xb16_in1k-196px.py
│ ├── eva-l-p14_8xb16_in1k-224px.py
│ ├── eva-l-p14_8xb16_in1k-336px.py
│ ├── eva-l-p14_headless.py
│ ├── metafile.yml
│ └── vit-b-eva.py
├── hornet
│ ├── README.md
│ ├── hornet-base-gf_8xb64_in1k.py
│ ├── hornet-base_8xb64_in1k.py
│ ├── hornet-small-gf_8xb64_in1k.py
│ ├── hornet-small_8xb64_in1k.py
│ ├── hornet-tiny-gf_8xb128_in1k.py
│ ├── hornet-tiny_8xb128_in1k.py
│ └── metafile.yml
├── hrnet
│ ├── README.md
│ ├── hrnet-w18_4xb32_in1k.py
│ ├── hrnet-w30_4xb32_in1k.py
│ ├── hrnet-w32_4xb32_in1k.py
│ ├── hrnet-w40_4xb32_in1k.py
│ ├── hrnet-w44_4xb32_in1k.py
│ ├── hrnet-w48_4xb32_in1k.py
│ ├── hrnet-w64_4xb32_in1k.py
│ └── metafile.yml
├── inception_v3
│ ├── README.md
│ ├── inception-v3_8xb32_in1k.py
│ └── metafile.yml
├── lenet
│ ├── README.md
│ └── lenet5_mnist.py
├── levit
│ ├── README.md
│ ├── deploy
│ │ ├── levit-128_8xb256_in1k.py
│ │ ├── levit-128s_8xb256_in1k.py
│ │ ├── levit-192_8xb256_in1k.py
│ │ ├── levit-256_8xb256_in1k.py
│ │ └── levit-384_8xb256_in1k.py
│ ├── levit-128_8xb256_in1k.py
│ ├── levit-128s_8xb256_in1k.py
│ ├── levit-192_8xb256_in1k.py
│ ├── levit-256_8xb256_in1k.py
│ ├── levit-384_8xb256_in1k.py
│ └── metafile.yml
├── mixmim
│ ├── README.md
│ ├── metafile.yml
│ └── mixmim-base_8xb64_in1k.py
├── mlp_mixer
│ ├── README.md
│ ├── metafile.yml
│ ├── mlp-mixer-base-p14_64xb64_in1k.py
│ ├── mlp-mixer-base-p16_64xb64_in1k.py
│ ├── mlp-mixer-large-p16_64xb64_in1k.py
│ └── mlp-mixer-small-p16_64xb64_in1k.py
├── mobilenet_v1
│ ├── mobilenet_v1.py
│ └── mobilenet_v1_refine.py
├── mobilenet_v2
│ ├── README.md
│ ├── metafile.yml
│ └── mobilenet-v2_8xb32_in1k.py
├── mobilenet_v3
│ ├── README.md
│ ├── metafile.yml
│ ├── mobilenet-v3-large_8xb128_in1k.py
│ ├── mobilenet-v3-small-050_8xb128_in1k.py
│ ├── mobilenet-v3-small-075_8xb128_in1k.py
│ ├── mobilenet-v3-small_8xb128_in1k.py
│ └── mobilenet-v3-small_8xb16_cifar10.py
├── mobileone
│ ├── README.md
│ ├── deploy
│ │ ├── mobileone-s0_deploy_8xb32_in1k.py
│ │ ├── mobileone-s1_deploy_8xb32_in1k.py
│ │ ├── mobileone-s2_deploy_8xb32_in1k.py
│ │ ├── mobileone-s3_deploy_8xb32_in1k.py
│ │ └── mobileone-s4_deploy_8xb32_in1k.py
│ ├── metafile.yml
│ ├── mobileone-s0_8xb32_in1k.py
│ ├── mobileone-s1_8xb32_in1k.py
│ ├── mobileone-s2_8xb32_in1k.py
│ ├── mobileone-s3_8xb32_in1k.py
│ └── mobileone-s4_8xb32_in1k.py
├── mobilevit
│ ├── README.md
│ ├── metafile.yml
│ ├── mobilevit-small_8xb128_in1k.py
│ ├── mobilevit-xsmall_8xb128_in1k.py
│ └── mobilevit-xxsmall_8xb128_in1k.py
├── mvit
│ ├── README.md
│ ├── metafile.yml
│ ├── mvitv2-base_8xb256_in1k.py
│ ├── mvitv2-large_8xb256_in1k.py
│ ├── mvitv2-small_8xb256_in1k.py
│ └── mvitv2-tiny_8xb256_in1k.py
├── poolformer
│ ├── README.md
│ ├── metafile.yml
│ ├── poolformer-m36_32xb128_in1k.py
│ ├── poolformer-m48_32xb128_in1k.py
│ ├── poolformer-s12_32xb128_in1k.py
│ ├── poolformer-s24_32xb128_in1k.py
│ └── poolformer-s36_32xb128_in1k.py
├── regnet
│ ├── README.md
│ ├── metafile.yml
│ ├── regnetx-1.6gf_8xb128_in1k.py
│ ├── regnetx-12gf_8xb64_in1k.py
│ ├── regnetx-3.2gf_8xb64_in1k.py
│ ├── regnetx-4.0gf_8xb64_in1k.py
│ ├── regnetx-400mf_8xb128_in1k.py
│ ├── regnetx-6.4gf_8xb64_in1k.py
│ ├── regnetx-8.0gf_8xb64_in1k.py
│ └── regnetx-800mf_8xb128_in1k.py
├── replknet
│ ├── README.md
│ ├── deploy
│ │ ├── replknet-31B-deploy_32xb64_in1k-384px.py
│ │ ├── replknet-31B-deploy_32xb64_in1k.py
│ │ ├── replknet-31L-deploy_32xb64_in1k-384px.py
│ │ └── replknet-XL-deploy_32xb64_in1k-320px.py
│ ├── metafile.yml
│ ├── replknet-31B_32xb64_in1k-384px.py
│ ├── replknet-31B_32xb64_in1k.py
│ ├── replknet-31L_32xb64_in1k-384px.py
│ └── replknet-XL_32xb64_in1k-320px.py
├── repmlp
│ ├── README.md
│ ├── metafile.yml
│ ├── repmlp-base_8xb64_in1k-256px.py
│ ├── repmlp-base_8xb64_in1k.py
│ ├── repmlp-base_delopy_8xb64_in1k.py
│ └── repmlp-base_deploy_8xb64_in1k-256px.py
├── repvgg
│ ├── README.md
│ ├── metafile.yml
│ ├── repvgg-A0_8xb32_in1k.py
│ ├── repvgg-A0_deploy_in1k.py
│ ├── repvgg-A1_8xb32_in1k.py
│ ├── repvgg-A2_8xb32_in1k.py
│ ├── repvgg-B0_8xb32_in1k.py
│ ├── repvgg-B1_8xb32_in1k.py
│ ├── repvgg-B1g2_8xb32_in1k.py
│ ├── repvgg-B1g4_8xb32_in1k.py
│ ├── repvgg-B2_8xb32_in1k.py
│ ├── repvgg-B2g4_8xb32_in1k.py
│ ├── repvgg-B3_8xb32_in1k.py
│ ├── repvgg-B3g4_8xb32_in1k.py
│ └── repvgg-D2se_8xb32_in1k.py
├── res2net
│ ├── README.md
│ ├── metafile.yml
│ ├── res2net101-w26-s4_8xb32_in1k.py
│ ├── res2net50-w14-s8_8xb32_in1k.py
│ └── res2net50-w26-s8_8xb32_in1k.py
├── resnest
│ ├── README.md
│ ├── _randaug_policies.py
│ ├── resnest101_32xb64_in1k.py
│ ├── resnest200_64xb32_in1k.py
│ ├── resnest269_64xb32_in1k.py
│ └── resnest50_32xb64_in1k.py
├── resnet
│ ├── README.md
│ ├── bit_resnet50_8xb32_in1k_strong_aug_coslr_300.py
│ ├── metafile.yml
│ ├── resnet101_8xb16_cifar10.py
│ ├── resnet101_8xb32_in1k.py
│ ├── resnet152_8xb16_cifar10.py
│ ├── resnet152_8xb32_in1k.py
│ ├── resnet152d_8xb32_in1k_strong_aug_coslr_300.py
│ ├── resnet18_8xb16_cifar10.py
│ ├── resnet18_8xb16_cifar100.py
│ ├── resnet18_8xb32_in1k.py
│ ├── resnet18_8xb32_in1k_former_distill.py
│ ├── resnet18_8xb64_in1k_cos_aug.py
│ ├── resnet34_8xb16_cifar10.py
│ ├── resnet34_8xb32_in1k.py
│ ├── resnet50_32xb64-warmup-coslr_in1k.py
│ ├── resnet50_32xb64-warmup-lbs_in1k.py
│ ├── resnet50_32xb64-warmup_in1k.py
│ ├── resnet50_8xb128_coslr-90e_in21k.py
│ ├── resnet50_8xb16-mixup_cifar10.py
│ ├── resnet50_8xb16_cifar10.py
│ ├── resnet50_8xb16_cifar100.py
│ ├── resnet50_8xb256-rsb-a1-600e_in1k.py
│ ├── resnet50_8xb256-rsb-a2-300e_in1k.py
│ ├── resnet50_8xb256-rsb-a3-100e_in1k.py
│ ├── resnet50_8xb32-coslr-preciseBN_in1k.py
│ ├── resnet50_8xb32-coslr_in1k.py
│ ├── resnet50_8xb32-cutmix_in1k.py
│ ├── resnet50_8xb32-fp16-dynamic_in1k.py
│ ├── resnet50_8xb32-fp16_in1k.py
│ ├── resnet50_8xb32-lbs_in1k.py
│ ├── resnet50_8xb32-mixup_in1k.py
│ ├── resnet50_8xb32_in1k.py
│ ├── resnet50_8xb32_in1k_strong_aug_coslr_300.py
│ ├── resnet50_8xb8_cub.py
│ ├── resnet50d_8xb32_in1k_strong_aug_coslr_300.py
│ ├── resnetv1c101_8xb32_in1k.py
│ ├── resnetv1c152_8xb32_in1k.py
│ ├── resnetv1c50_8xb32_in1k.py
│ ├── resnetv1d101_8xb32_in1k.py
│ ├── resnetv1d152_8xb32_in1k.py
│ └── resnetv1d50_8xb32_in1k.py
├── resnext
│ ├── README.md
│ ├── metafile.yml
│ ├── resnext101-32x4d_8xb32_in1k.py
│ ├── resnext101-32x8d_8xb32_in1k.py
│ ├── resnext152-32x4d_8xb32_in1k.py
│ └── resnext50-32x4d_8xb32_in1k.py
├── revvit
│ ├── README.md
│ ├── metafile.yml
│ ├── revvit-base_8xb256_in1k.py
│ └── revvit-small_8xb256_in1k.py
├── seresnet
│ ├── README.md
│ ├── metafile.yml
│ ├── seresnet101_8xb32_in1k.py
│ ├── seresnet50_8xb32_in1k.py
│ ├── seresnext101-32x4d_8xb32_in1k.py
│ └── seresnext50-32x4d_8xb32_in1k.py
├── shufflenet_v1
│ ├── README.md
│ ├── metafile.yml
│ └── shufflenet-v1-1x_16xb64_in1k.py
├── shufflenet_v2
│ ├── README.md
│ ├── metafile.yml
│ └── shufflenet-v2-1x_16xb64_in1k.py
├── swin_transformer
│ ├── README.md
│ ├── metafile.yml
│ ├── swin-base_16xb64_in1k-384px.py
│ ├── swin-base_16xb64_in1k.py
│ ├── swin-large_16xb64_in1k-384px.py
│ ├── swin-large_16xb64_in1k.py
│ ├── swin-large_8xb8_cub_384px.py
│ ├── swin-small_16xb64_in1k.py
│ └── swin-tiny_16xb64_in1k.py
├── swin_transformer_v2
│ ├── README.md
│ ├── metafile.yml
│ ├── swinv2-base-w12_8xb128_in21k-192px.py
│ ├── swinv2-base-w16_16xb64_in1k-256px.py
│ ├── swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py
│ ├── swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py
│ ├── swinv2-base-w8_16xb64_in1k-256px.py
│ ├── swinv2-large-w12_8xb128_in21k-192px.py
│ ├── swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py
│ ├── swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py
│ ├── swinv2-small-w16_16xb64_in1k-256px.py
│ ├── swinv2-small-w8_16xb64_in1k-256px.py
│ ├── swinv2-tiny-w16_16xb64_in1k-256px.py
│ └── swinv2-tiny-w8_16xb64_in1k-256px.py
├── t2t_vit
│ ├── README.md
│ ├── metafile.yml
│ ├── t2t-vit-t-14_8xb64_in1k.py
│ ├── t2t-vit-t-19_8xb64_in1k.py
│ └── t2t-vit-t-24_8xb64_in1k.py
├── tinyvit
│ ├── README.md
│ ├── metafile.yml
│ ├── tinyvit-11m-distill_8xb256_in1k.py
│ ├── tinyvit-11m_8xb256_in1k.py
│ ├── tinyvit-21m-distill_8xb256_in1k-384px.py
│ ├── tinyvit-21m-distill_8xb256_in1k-512px.py
│ ├── tinyvit-21m-distill_8xb256_in1k.py
│ ├── tinyvit-21m_8xb256_in1k.py
│ ├── tinyvit-5m-distill_8xb256_in1k.py
│ └── tinyvit-5m_8xb256_in1k.py
├── tnt
│ ├── README.md
│ ├── metafile.yml
│ └── tnt-s-p16_16xb64_in1k.py
├── twins
│ ├── README.md
│ ├── metafile.yml
│ ├── twins-pcpvt-base_8xb128_in1k.py
│ ├── twins-pcpvt-large_16xb64_in1k.py
│ ├── twins-pcpvt-small_8xb128_in1k.py
│ ├── twins-svt-base_8xb128_in1k.py
│ ├── twins-svt-large_16xb64_in1k.py
│ └── twins-svt-small_8xb128_in1k.py
├── van
│ ├── README.md
│ ├── metafile.yml
│ ├── van-base_8xb128_in1k.py
│ ├── van-large_8xb128_in1k.py
│ ├── van-small_8xb128_in1k.py
│ └── van-tiny_8xb128_in1k.py
├── vgg
│ ├── README.md
│ ├── metafile.yml
│ ├── vgg11_8xb32_in1k.py
│ ├── vgg11bn_8xb32_in1k.py
│ ├── vgg13_8xb32_in1k.py
│ ├── vgg13bn_8xb32_in1k.py
│ ├── vgg16_8xb16_voc.py
│ ├── vgg16_8xb32_in1k.py
│ ├── vgg16bn_8xb32_in1k.py
│ ├── vgg19_8xb32_in1k.py
│ └── vgg19bn_8xb32_in1k.py
├── vig
│ ├── README.md
│ ├── metafile.yml
│ ├── pvig-base_8xb128_in1k.py
│ ├── pvig-medium_8xb128_in1k.py
│ ├── pvig-small_8xb128_in1k.py
│ ├── pvig-tiny_8xb128_in1k.py
│ ├── vig-base_8xb128_in1k.py
│ ├── vig-small_8xb128_in1k.py
│ └── vig-tiny_8xb128_in1k.py
├── vision_transformer
│ ├── README.md
│ ├── metafile.yml
│ ├── vit-base-p16_ft-4xb544-ipu_in1k.py
│ ├── vit-base-p16_ft-64xb64_in1k-384.py
│ ├── vit-base-p16_pt-32xb128-mae_in1k-224.py
│ ├── vit-base-p16_pt-64xb64_in1k-224.py
│ ├── vit-base-p32_ft-64xb64_in1k-384.py
│ ├── vit-base-p32_pt-64xb64_in1k-224.py
│ ├── vit-large-p16_ft-64xb64_in1k-384.py
│ ├── vit-large-p16_pt-64xb64_in1k-224.py
│ ├── vit-large-p32_ft-64xb64_in1k-384.py
│ └── vit-large-p32_pt-64xb64_in1k-224.py
├── wrn
│ ├── README.md
│ ├── metafile.yml
│ ├── wide-resnet101_8xb32_in1k.py
│ ├── wide-resnet50_8xb32_in1k.py
│ └── wide-resnet50_timm_8xb32_in1k.py
└── xcit
│ ├── README.md
│ ├── metafile.yml
│ ├── xcit-large-24-p16_8xb128_in1k-384px.py
│ ├── xcit-large-24-p16_8xb128_in1k.py
│ ├── xcit-large-24-p8_8xb128_in1k-384px.py
│ ├── xcit-large-24-p8_8xb128_in1k.py
│ ├── xcit-medium-24-p16_8xb128_in1k-384px.py
│ ├── xcit-medium-24-p16_8xb128_in1k.py
│ ├── xcit-medium-24-p8_8xb128_in1k-384px.py
│ ├── xcit-medium-24-p8_8xb128_in1k.py
│ ├── xcit-nano-12-p16_8xb128_in1k-384px.py
│ ├── xcit-nano-12-p16_8xb128_in1k.py
│ ├── xcit-nano-12-p8_8xb128_in1k-384px.py
│ ├── xcit-nano-12-p8_8xb128_in1k.py
│ ├── xcit-small-12-p16_8xb128_in1k-384px.py
│ ├── xcit-small-12-p16_8xb128_in1k.py
│ ├── xcit-small-12-p8_8xb128_in1k-384px.py
│ ├── xcit-small-12-p8_8xb128_in1k.py
│ ├── xcit-small-24-p16_8xb128_in1k-384px.py
│ ├── xcit-small-24-p16_8xb128_in1k.py
│ ├── xcit-small-24-p8_8xb128_in1k-384px.py
│ ├── xcit-small-24-p8_8xb128_in1k.py
│ ├── xcit-tiny-12-p16_8xb128_in1k-384px.py
│ ├── xcit-tiny-12-p16_8xb128_in1k.py
│ ├── xcit-tiny-12-p8_8xb128_in1k-384px.py
│ ├── xcit-tiny-12-p8_8xb128_in1k.py
│ ├── xcit-tiny-24-p16_8xb128_in1k-384px.py
│ ├── xcit-tiny-24-p16_8xb128_in1k.py
│ ├── xcit-tiny-24-p8_8xb128_in1k-384px.py
│ └── xcit-tiny-24-p8_8xb128_in1k.py
├── demo
├── bird.JPEG
├── cat-dog.png
├── demo.JPEG
├── dog.jpg
├── image_demo.py
└── ipu_train_example.sh
├── docker
├── Dockerfile
└── serve
│ ├── Dockerfile
│ ├── config.properties
│ └── entrypoint.sh
├── docs
├── en
│ ├── Makefile
│ ├── _static
│ │ ├── css
│ │ │ └── readthedocs.css
│ │ ├── image
│ │ │ ├── confusion-matrix.png
│ │ │ ├── mmcls-logo.png
│ │ │ └── tools
│ │ │ │ └── analysis
│ │ │ │ └── analyze_log.jpg
│ │ └── js
│ │ │ └── custom.js
│ ├── _templates
│ │ ├── 404.html
│ │ ├── autosummary
│ │ │ └── class.rst
│ │ ├── callable.rst
│ │ └── data_transform.rst
│ ├── advanced_guides
│ │ ├── convention.md
│ │ ├── data_flow.md
│ │ ├── datasets.md
│ │ ├── evaluation.md
│ │ ├── modules.md
│ │ ├── pipeline.md
│ │ ├── runtime.md
│ │ └── schedule.md
│ ├── api
│ │ ├── apis.rst
│ │ ├── data_process.rst
│ │ ├── datasets.rst
│ │ ├── engine.rst
│ │ ├── evaluation.rst
│ │ ├── models.rst
│ │ ├── structures.rst
│ │ ├── utils.rst
│ │ └── visualization.rst
│ ├── conf.py
│ ├── device
│ │ └── npu.md
│ ├── docutils.conf
│ ├── get_started.md
│ ├── index.rst
│ ├── migration.md
│ ├── notes
│ │ ├── changelog.md
│ │ ├── faq.md
│ │ └── projects.md
│ ├── stat.py
│ ├── useful_tools
│ │ ├── cam_visualization.md
│ │ ├── complexity_analysis.md
│ │ ├── dataset_visualization.md
│ │ ├── log_result_analysis.md
│ │ ├── model_serving.md
│ │ ├── print_config.md
│ │ ├── scheduler_visualization.md
│ │ └── verify_dataset.md
│ └── user_guides
│ │ ├── config.md
│ │ ├── dataset_prepare.md
│ │ ├── finetune.md
│ │ ├── inference.md
│ │ └── train_test.md
└── zh_CN
│ ├── Makefile
│ ├── _static
│ ├── css
│ │ └── readthedocs.css
│ ├── image
│ │ ├── mmcls-logo.png
│ │ └── tools
│ │ │ └── analysis
│ │ │ └── analyze_log.jpg
│ └── js
│ │ └── custom.js
│ ├── _templates
│ ├── 404.html
│ ├── autosummary
│ │ └── class.rst
│ ├── callable.rst
│ └── data_transform.rst
│ ├── advanced_guides
│ ├── convention.md
│ ├── data_flow.md
│ ├── datasets.md
│ ├── evaluation.md
│ ├── modules.md
│ ├── pipeline.md
│ ├── runtime.md
│ └── schedule.md
│ ├── conf.py
│ ├── device
│ └── npu_zh.md
│ ├── docutils.conf
│ ├── get_started.md
│ ├── index.rst
│ ├── locales
│ └── zh_CN
│ │ └── LC_MESSAGES
│ │ ├── api.po
│ │ └── papers.po
│ ├── migration.md
│ ├── notes
│ ├── contribution_guide.md
│ ├── faq.md
│ └── projects.md
│ ├── stat.py
│ ├── useful_tools
│ ├── cam_visualization.md
│ ├── complexity_analysis.md
│ ├── dataset_visualization.md
│ ├── log_result_analysis.md
│ ├── model_serving.md
│ ├── print_config.md
│ ├── scheduler_visualization.md
│ └── verify_dataset.md
│ └── user_guides
│ ├── config.md
│ ├── dataset_prepare.md
│ ├── finetune.md
│ ├── inference.md
│ └── train_test.md
├── imgs
├── ADE-20K.png
├── IN-1K.png
├── MS-COCO.png
└── teaser.png
├── mmcls.egg-info
├── PKG-INFO
├── SOURCES.txt
├── dependency_links.txt
├── not-zip-safe
├── requires.txt
└── top_level.txt
├── mmcls
├── .mim
│ ├── configs
│ │ ├── _base_
│ │ │ ├── datasets
│ │ │ │ ├── cifar100_bs16.py
│ │ │ │ ├── cifar10_bs16.py
│ │ │ │ ├── cub_bs8_384.py
│ │ │ │ ├── cub_bs8_448.py
│ │ │ │ ├── imagenet21k_bs128.py
│ │ │ │ ├── imagenet_bs128_mbv3.py
│ │ │ │ ├── imagenet_bs128_poolformer_medium_224.py
│ │ │ │ ├── imagenet_bs128_poolformer_small_224.py
│ │ │ │ ├── imagenet_bs128_revvit_224.py
│ │ │ │ ├── imagenet_bs128_vig_224.py
│ │ │ │ ├── imagenet_bs16_eva_196.py
│ │ │ │ ├── imagenet_bs16_eva_336.py
│ │ │ │ ├── imagenet_bs16_eva_560.py
│ │ │ │ ├── imagenet_bs16_pil_bicubic_384.py
│ │ │ │ ├── imagenet_bs256_davit_224.py
│ │ │ │ ├── imagenet_bs256_levit_224.py
│ │ │ │ ├── imagenet_bs256_rsb_a12.py
│ │ │ │ ├── imagenet_bs256_rsb_a3.py
│ │ │ │ ├── imagenet_bs32.py
│ │ │ │ ├── imagenet_bs32_pil_bicubic.py
│ │ │ │ ├── imagenet_bs32_pil_resize.py
│ │ │ │ ├── imagenet_bs32_swin_224.py
│ │ │ │ ├── imagenet_bs64.py
│ │ │ │ ├── imagenet_bs64_autoaug.py
│ │ │ │ ├── imagenet_bs64_clip_224.py
│ │ │ │ ├── imagenet_bs64_clip_384.py
│ │ │ │ ├── imagenet_bs64_clip_448.py
│ │ │ │ ├── imagenet_bs64_convmixer_224.py
│ │ │ │ ├── imagenet_bs64_deit3_224.py
│ │ │ │ ├── imagenet_bs64_deit3_384.py
│ │ │ │ ├── imagenet_bs64_edgenext_256.py
│ │ │ │ ├── imagenet_bs64_mixer_224.py
│ │ │ │ ├── imagenet_bs64_pil_resize.py
│ │ │ │ ├── imagenet_bs64_pil_resize_autoaug.py
│ │ │ │ ├── imagenet_bs64_swin_224.py
│ │ │ │ ├── imagenet_bs64_swin_256.py
│ │ │ │ ├── imagenet_bs64_swin_384.py
│ │ │ │ ├── imagenet_bs64_t2t_224.py
│ │ │ │ ├── imagenet_bs8_pil_bicubic_320.py
│ │ │ │ ├── inshop_bs32_448.py
│ │ │ │ ├── pipelines
│ │ │ │ │ ├── auto_aug.py
│ │ │ │ │ └── rand_aug.py
│ │ │ │ └── voc_bs16.py
│ │ │ ├── default_runtime.py
│ │ │ ├── models
│ │ │ │ ├── conformer
│ │ │ │ │ ├── base-p16.py
│ │ │ │ │ ├── small-p16.py
│ │ │ │ │ ├── small-p32.py
│ │ │ │ │ └── tiny-p16.py
│ │ │ │ ├── convmixer
│ │ │ │ │ ├── convmixer-1024-20.py
│ │ │ │ │ ├── convmixer-1536-20.py
│ │ │ │ │ └── convmixer-768-32.py
│ │ │ │ ├── convnext
│ │ │ │ │ ├── convnext-base.py
│ │ │ │ │ ├── convnext-large.py
│ │ │ │ │ ├── convnext-small.py
│ │ │ │ │ ├── convnext-tiny.py
│ │ │ │ │ └── convnext-xlarge.py
│ │ │ │ ├── convnext_v2
│ │ │ │ │ ├── atto.py
│ │ │ │ │ ├── base.py
│ │ │ │ │ ├── femto.py
│ │ │ │ │ ├── huge.py
│ │ │ │ │ ├── large.py
│ │ │ │ │ ├── nano.py
│ │ │ │ │ ├── pico.py
│ │ │ │ │ └── tiny.py
│ │ │ │ ├── davit
│ │ │ │ │ ├── davit-base.py
│ │ │ │ │ ├── davit-small.py
│ │ │ │ │ └── davit-tiny.py
│ │ │ │ ├── deit3
│ │ │ │ │ ├── deit3-base-p16-224.py
│ │ │ │ │ ├── deit3-base-p16-384.py
│ │ │ │ │ ├── deit3-huge-p14-224.py
│ │ │ │ │ ├── deit3-large-p16-224.py
│ │ │ │ │ ├── deit3-large-p16-384.py
│ │ │ │ │ ├── deit3-medium-p16-224.py
│ │ │ │ │ ├── deit3-small-p16-224.py
│ │ │ │ │ └── deit3-small-p16-384.py
│ │ │ │ ├── densenet
│ │ │ │ │ ├── densenet121.py
│ │ │ │ │ ├── densenet161.py
│ │ │ │ │ ├── densenet169.py
│ │ │ │ │ └── densenet201.py
│ │ │ │ ├── edgenext
│ │ │ │ │ ├── edgenext-base.py
│ │ │ │ │ ├── edgenext-small.py
│ │ │ │ │ ├── edgenext-xsmall.py
│ │ │ │ │ └── edgenext-xxsmall.py
│ │ │ │ ├── efficientformer-l1.py
│ │ │ │ ├── efficientnet_b0.py
│ │ │ │ ├── efficientnet_b1.py
│ │ │ │ ├── efficientnet_b2.py
│ │ │ │ ├── efficientnet_b3.py
│ │ │ │ ├── efficientnet_b4.py
│ │ │ │ ├── efficientnet_b5.py
│ │ │ │ ├── efficientnet_b6.py
│ │ │ │ ├── efficientnet_b7.py
│ │ │ │ ├── efficientnet_b8.py
│ │ │ │ ├── efficientnet_em.py
│ │ │ │ ├── efficientnet_es.py
│ │ │ │ ├── efficientnet_l2.py
│ │ │ │ ├── efficientnet_v2
│ │ │ │ │ ├── efficientnetv2_b0.py
│ │ │ │ │ ├── efficientnetv2_b1.py
│ │ │ │ │ ├── efficientnetv2_b2.py
│ │ │ │ │ ├── efficientnetv2_b3.py
│ │ │ │ │ ├── efficientnetv2_l.py
│ │ │ │ │ ├── efficientnetv2_m.py
│ │ │ │ │ ├── efficientnetv2_s.py
│ │ │ │ │ └── efficientnetv2_xl.py
│ │ │ │ ├── eva
│ │ │ │ │ ├── eva-g.py
│ │ │ │ │ └── eva-l.py
│ │ │ │ ├── hornet
│ │ │ │ │ ├── hornet-base-gf.py
│ │ │ │ │ ├── hornet-base.py
│ │ │ │ │ ├── hornet-large-gf.py
│ │ │ │ │ ├── hornet-large-gf384.py
│ │ │ │ │ ├── hornet-large.py
│ │ │ │ │ ├── hornet-small-gf.py
│ │ │ │ │ ├── hornet-small.py
│ │ │ │ │ ├── hornet-tiny-gf.py
│ │ │ │ │ └── hornet-tiny.py
│ │ │ │ ├── hrnet
│ │ │ │ │ ├── hrnet-w18.py
│ │ │ │ │ ├── hrnet-w30.py
│ │ │ │ │ ├── hrnet-w32.py
│ │ │ │ │ ├── hrnet-w40.py
│ │ │ │ │ ├── hrnet-w44.py
│ │ │ │ │ ├── hrnet-w48.py
│ │ │ │ │ └── hrnet-w64.py
│ │ │ │ ├── inception_v3.py
│ │ │ │ ├── levit-256-p16.py
│ │ │ │ ├── mixmim
│ │ │ │ │ └── mixmim_base.py
│ │ │ │ ├── mlp_mixer_base_patch16.py
│ │ │ │ ├── mlp_mixer_large_patch16.py
│ │ │ │ ├── mobilenet_v1_1x.py
│ │ │ │ ├── mobilenet_v2_1x.py
│ │ │ │ ├── mobilenet_v3
│ │ │ │ │ ├── mobilenet_v3_large_imagenet.py
│ │ │ │ │ ├── mobilenet_v3_small_050_imagenet.py
│ │ │ │ │ ├── mobilenet_v3_small_075_imagenet.py
│ │ │ │ │ ├── mobilenet_v3_small_cifar.py
│ │ │ │ │ └── mobilenet_v3_small_imagenet.py
│ │ │ │ ├── mobileone
│ │ │ │ │ ├── mobileone_s0.py
│ │ │ │ │ ├── mobileone_s1.py
│ │ │ │ │ ├── mobileone_s2.py
│ │ │ │ │ ├── mobileone_s3.py
│ │ │ │ │ └── mobileone_s4.py
│ │ │ │ ├── mobilevit
│ │ │ │ │ ├── mobilevit_s.py
│ │ │ │ │ ├── mobilevit_xs.py
│ │ │ │ │ └── mobilevit_xxs.py
│ │ │ │ ├── mvit
│ │ │ │ │ ├── mvitv2-base.py
│ │ │ │ │ ├── mvitv2-large.py
│ │ │ │ │ ├── mvitv2-small.py
│ │ │ │ │ └── mvitv2-tiny.py
│ │ │ │ ├── poolformer
│ │ │ │ │ ├── poolformer_m36.py
│ │ │ │ │ ├── poolformer_m48.py
│ │ │ │ │ ├── poolformer_s12.py
│ │ │ │ │ ├── poolformer_s24.py
│ │ │ │ │ └── poolformer_s36.py
│ │ │ │ ├── regnet
│ │ │ │ │ ├── regnetx_1.6gf.py
│ │ │ │ │ ├── regnetx_12gf.py
│ │ │ │ │ ├── regnetx_3.2gf.py
│ │ │ │ │ ├── regnetx_4.0gf.py
│ │ │ │ │ ├── regnetx_400mf.py
│ │ │ │ │ ├── regnetx_6.4gf.py
│ │ │ │ │ ├── regnetx_8.0gf.py
│ │ │ │ │ └── regnetx_800mf.py
│ │ │ │ ├── replknet-31B_in1k.py
│ │ │ │ ├── replknet-31L_in1k.py
│ │ │ │ ├── replknet-XL_in1k.py
│ │ │ │ ├── repmlp-base_224.py
│ │ │ │ ├── repvgg-A0_in1k.py
│ │ │ │ ├── repvgg-B3_lbs-mixup_in1k.py
│ │ │ │ ├── res2net101-w26-s4.py
│ │ │ │ ├── res2net50-w14-s8.py
│ │ │ │ ├── res2net50-w26-s4.py
│ │ │ │ ├── res2net50-w26-s6.py
│ │ │ │ ├── res2net50-w26-s8.py
│ │ │ │ ├── res2net50-w48-s2.py
│ │ │ │ ├── resnest101.py
│ │ │ │ ├── resnest200.py
│ │ │ │ ├── resnest269.py
│ │ │ │ ├── resnest50.py
│ │ │ │ ├── resnet101.py
│ │ │ │ ├── resnet101_cifar.py
│ │ │ │ ├── resnet152.py
│ │ │ │ ├── resnet152_cifar.py
│ │ │ │ ├── resnet18.py
│ │ │ │ ├── resnet18_cifar.py
│ │ │ │ ├── resnet34.py
│ │ │ │ ├── resnet34_cifar.py
│ │ │ │ ├── resnet34_gem.py
│ │ │ │ ├── resnet50.py
│ │ │ │ ├── resnet50_cifar.py
│ │ │ │ ├── resnet50_cifar_cutmix.py
│ │ │ │ ├── resnet50_cifar_mixup.py
│ │ │ │ ├── resnet50_cutmix.py
│ │ │ │ ├── resnet50_label_smooth.py
│ │ │ │ ├── resnet50_mixup.py
│ │ │ │ ├── resnetv1c50.py
│ │ │ │ ├── resnetv1d101.py
│ │ │ │ ├── resnetv1d152.py
│ │ │ │ ├── resnetv1d50.py
│ │ │ │ ├── resnext101_32x4d.py
│ │ │ │ ├── resnext101_32x8d.py
│ │ │ │ ├── resnext152_32x4d.py
│ │ │ │ ├── resnext50_32x4d.py
│ │ │ │ ├── revvit
│ │ │ │ │ ├── revvit-base.py
│ │ │ │ │ └── revvit-small.py
│ │ │ │ ├── seresnet101.py
│ │ │ │ ├── seresnet50.py
│ │ │ │ ├── seresnext101_32x4d.py
│ │ │ │ ├── seresnext50_32x4d.py
│ │ │ │ ├── shufflenet_v1_1x.py
│ │ │ │ ├── shufflenet_v2_1x.py
│ │ │ │ ├── swin_transformer
│ │ │ │ │ ├── base_224.py
│ │ │ │ │ ├── base_384.py
│ │ │ │ │ ├── large_224.py
│ │ │ │ │ ├── large_384.py
│ │ │ │ │ ├── small_224.py
│ │ │ │ │ └── tiny_224.py
│ │ │ │ ├── swin_transformer_v2
│ │ │ │ │ ├── base_256.py
│ │ │ │ │ ├── base_384.py
│ │ │ │ │ ├── large_256.py
│ │ │ │ │ ├── large_384.py
│ │ │ │ │ ├── small_256.py
│ │ │ │ │ └── tiny_256.py
│ │ │ │ ├── t2t-vit-t-14.py
│ │ │ │ ├── t2t-vit-t-19.py
│ │ │ │ ├── t2t-vit-t-24.py
│ │ │ │ ├── tinyvit
│ │ │ │ │ ├── tinyvit-11m.py
│ │ │ │ │ ├── tinyvit-21m.py
│ │ │ │ │ └── tinyvit-5m.py
│ │ │ │ ├── tnt_s_patch16_224.py
│ │ │ │ ├── twins_pcpvt_base.py
│ │ │ │ ├── twins_svt_base.py
│ │ │ │ ├── van
│ │ │ │ │ ├── van_base.py
│ │ │ │ │ ├── van_large.py
│ │ │ │ │ ├── van_small.py
│ │ │ │ │ └── van_tiny.py
│ │ │ │ ├── vgg11.py
│ │ │ │ ├── vgg11bn.py
│ │ │ │ ├── vgg13.py
│ │ │ │ ├── vgg13bn.py
│ │ │ │ ├── vgg16.py
│ │ │ │ ├── vgg16bn.py
│ │ │ │ ├── vgg19.py
│ │ │ │ ├── vgg19bn.py
│ │ │ │ ├── vig
│ │ │ │ │ ├── pyramid_vig_base.py
│ │ │ │ │ ├── pyramid_vig_medium.py
│ │ │ │ │ ├── pyramid_vig_small.py
│ │ │ │ │ ├── pyramid_vig_tiny.py
│ │ │ │ │ ├── vig_base.py
│ │ │ │ │ ├── vig_small.py
│ │ │ │ │ └── vig_tiny.py
│ │ │ │ ├── vit-base-p16.py
│ │ │ │ ├── vit-base-p32.py
│ │ │ │ ├── vit-large-p16.py
│ │ │ │ ├── vit-large-p32.py
│ │ │ │ └── wide-resnet50.py
│ │ │ └── schedules
│ │ │ │ ├── cifar10_bs128.py
│ │ │ │ ├── cub_bs64.py
│ │ │ │ ├── imagenet_bs1024_adamw_conformer.py
│ │ │ │ ├── imagenet_bs1024_adamw_resnet.py
│ │ │ │ ├── imagenet_bs1024_adamw_revvit.py
│ │ │ │ ├── imagenet_bs1024_adamw_swin.py
│ │ │ │ ├── imagenet_bs1024_coslr.py
│ │ │ │ ├── imagenet_bs1024_linearlr_bn_nowd.py
│ │ │ │ ├── imagenet_bs2048.py
│ │ │ │ ├── imagenet_bs2048_AdamW.py
│ │ │ │ ├── imagenet_bs2048_adamw_levit.py
│ │ │ │ ├── imagenet_bs2048_coslr.py
│ │ │ │ ├── imagenet_bs2048_rsb.py
│ │ │ │ ├── imagenet_bs256.py
│ │ │ │ ├── imagenet_bs256_140e.py
│ │ │ │ ├── imagenet_bs256_200e_coslr_warmup.py
│ │ │ │ ├── imagenet_bs256_coslr.py
│ │ │ │ ├── imagenet_bs256_coslr_coswd_300e.py
│ │ │ │ ├── imagenet_bs256_epochstep.py
│ │ │ │ └── imagenet_bs4096_AdamW.py
│ │ ├── arcface
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ └── resnet50-arcface_8xb32_inshop.py
│ │ ├── beit
│ │ │ ├── README.md
│ │ │ ├── beit-base-p16_8xb64_in1k.py
│ │ │ └── metafile.yml
│ │ ├── beitv2
│ │ │ ├── README.md
│ │ │ ├── beitv2-base-p16_8xb64_in1k.py
│ │ │ └── metafile.yml
│ │ ├── clip
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── vit-base-p16_pt-64xb64_in1k-384px.py
│ │ │ ├── vit-base-p16_pt-64xb64_in1k-448px.py
│ │ │ ├── vit-base-p16_pt-64xb64_in1k.py
│ │ │ ├── vit-base-p32_pt-64xb64_in1k-384px.py
│ │ │ ├── vit-base-p32_pt-64xb64_in1k-448px.py
│ │ │ └── vit-base-p32_pt-64xb64_in1k.py
│ │ ├── conformer
│ │ │ ├── README.md
│ │ │ ├── conformer-base-p16_8xb128_in1k.py
│ │ │ ├── conformer-small-p16_8xb128_in1k.py
│ │ │ ├── conformer-small-p32_8xb128_in1k.py
│ │ │ ├── conformer-tiny-p16_8xb128_in1k.py
│ │ │ └── metafile.yml
│ │ ├── convmixer
│ │ │ ├── README.md
│ │ │ ├── convmixer-1024-20_10xb64_in1k.py
│ │ │ ├── convmixer-1536-20_10xb64_in1k.py
│ │ │ ├── convmixer-768-32_10xb64_in1k.py
│ │ │ └── metafile.yml
│ │ ├── convnext
│ │ │ ├── README.md
│ │ │ ├── convnext-base_32xb128_in1k-384px.py
│ │ │ ├── convnext-base_32xb128_in1k.py
│ │ │ ├── convnext-base_32xb128_in21k.py
│ │ │ ├── convnext-large_64xb64_in1k-384px.py
│ │ │ ├── convnext-large_64xb64_in1k.py
│ │ │ ├── convnext-large_64xb64_in21k.py
│ │ │ ├── convnext-small_32xb128_in1k-384px.py
│ │ │ ├── convnext-small_32xb128_in1k.py
│ │ │ ├── convnext-tiny_32xb128_in1k-384px.py
│ │ │ ├── convnext-tiny_32xb128_in1k.py
│ │ │ ├── convnext-xlarge_64xb64_in1k-384px.py
│ │ │ ├── convnext-xlarge_64xb64_in1k.py
│ │ │ ├── convnext-xlarge_64xb64_in21k.py
│ │ │ └── metafile.yml
│ │ ├── convnext_v2
│ │ │ ├── README.md
│ │ │ ├── convnext-v2-atto_32xb32_in1k.py
│ │ │ ├── convnext-v2-base_32xb32_in1k-384px.py
│ │ │ ├── convnext-v2-base_32xb32_in1k.py
│ │ │ ├── convnext-v2-femto_32xb32_in1k.py
│ │ │ ├── convnext-v2-huge_32xb32_in1k-384px.py
│ │ │ ├── convnext-v2-huge_32xb32_in1k-512px.py
│ │ │ ├── convnext-v2-huge_32xb32_in1k.py
│ │ │ ├── convnext-v2-large_32xb32_in1k-384px.py
│ │ │ ├── convnext-v2-large_32xb32_in1k.py
│ │ │ ├── convnext-v2-nano_32xb32_in1k-384px.py
│ │ │ ├── convnext-v2-nano_32xb32_in1k.py
│ │ │ ├── convnext-v2-pico_32xb32_in1k.py
│ │ │ ├── convnext-v2-tiny_32xb32_in1k-384px.py
│ │ │ ├── convnext-v2-tiny_32xb32_in1k.py
│ │ │ └── metafile.yml
│ │ ├── cspnet
│ │ │ ├── README.md
│ │ │ ├── cspdarknet50_8xb32_in1k.py
│ │ │ ├── cspresnet50_8xb32_in1k.py
│ │ │ ├── cspresnext50_8xb32_in1k.py
│ │ │ └── metafile.yml
│ │ ├── csra
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ └── resnet101-csra_1xb16_voc07-448px.py
│ │ ├── davit
│ │ │ ├── README.md
│ │ │ ├── davit-base_4xb256_in1k.py
│ │ │ ├── davit-small_4xb256_in1k.py
│ │ │ ├── davit-tiny_4xb256_in1k.py
│ │ │ └── metafile.yml
│ │ ├── deit
│ │ │ ├── README.md
│ │ │ ├── deit-base-distilled_ft-16xb32_in1k-384px.py
│ │ │ ├── deit-base-distilled_pt-16xb64_in1k.py
│ │ │ ├── deit-base_ft-16xb32_in1k-384px.py
│ │ │ ├── deit-base_pt-16xb64_in1k.py
│ │ │ ├── deit-small-distilled_pt-4xb256_in1k.py
│ │ │ ├── deit-small_pt-4xb256_in1k.py
│ │ │ ├── deit-tiny-distilled_pt-4xb256_in1k.py
│ │ │ ├── deit-tiny_pt-4xb256_in1k.py
│ │ │ └── metafile.yml
│ │ ├── deit3
│ │ │ ├── README.md
│ │ │ ├── deit3-base-p16_64xb32_in1k-384px.py
│ │ │ ├── deit3-base-p16_64xb64_in1k.py
│ │ │ ├── deit3-huge-p14_64xb32_in1k.py
│ │ │ ├── deit3-large-p16_64xb16_in1k-384px.py
│ │ │ ├── deit3-large-p16_64xb64_in1k.py
│ │ │ ├── deit3-medium-p16_64xb64_in1k.py
│ │ │ ├── deit3-small-p16_64xb64_in1k-384px.py
│ │ │ ├── deit3-small-p16_64xb64_in1k.py
│ │ │ └── metafile.yml
│ │ ├── densenet
│ │ │ ├── README.md
│ │ │ ├── densenet121_4xb256_in1k.py
│ │ │ ├── densenet161_4xb256_in1k.py
│ │ │ ├── densenet169_4xb256_in1k.py
│ │ │ ├── densenet201_4xb256_in1k.py
│ │ │ └── metafile.yml
│ │ ├── distillers
│ │ │ ├── Former2CNN
│ │ │ │ ├── AttentionFD_swin-s_distill_res18_img.py
│ │ │ │ ├── T2S_deit-s_distill_resnet18_img.py
│ │ │ │ ├── afd_deit-b3_distill_res18_img_w_token.py
│ │ │ │ ├── attention_deit-s_distill_resnet18_img_pe_layer_4_teacher_downsampling_corr.py
│ │ │ │ ├── attention_deit-s_distill_resnet18_img_pos_embed_before_stage4.py
│ │ │ │ ├── attention_deit-s_distill_resnet18_img_pos_embed_before_stage4_fpp.py
│ │ │ │ ├── attention_deit-s_distill_resnet18_img_trainable_pos_embed_selfattent.py
│ │ │ │ ├── baseline_swin-s_distill_res18_img.py
│ │ │ │ ├── deit-b3_distill_res18_img.py
│ │ │ │ ├── deit-s3_distill_res18_img.py
│ │ │ │ ├── fd_deit-b3_distill_res18_img_w_token.py
│ │ │ │ ├── mfd_deit-s_distill_res18_img.py
│ │ │ │ └── tcd_deit-b3_distill_res18_img.py
│ │ │ ├── cifar100
│ │ │ │ ├── res18_sd_cifar.py
│ │ │ │ └── res34_distill_res18_cifar.py
│ │ │ └── imagenet
│ │ │ │ ├── FD_multistage_swin-s_distill_res18_img.py
│ │ │ │ ├── deit-b3_distill_deit-s_img.py
│ │ │ │ ├── deit-l3_distill_deit-b_img.py
│ │ │ │ ├── deit-s3_distill_deit-t_img.py
│ │ │ │ ├── deit-s_distill_deit-t_img.py
│ │ │ │ ├── deit-s_distill_resnet18_img.py
│ │ │ │ ├── deit-t_sd_img.py
│ │ │ │ ├── fd_swin_aug_swin-s_distill_res18_img.py
│ │ │ │ ├── mfd_swin-s_distill_res18_img.py
│ │ │ │ ├── mv1_sd_img.py
│ │ │ │ ├── mv2_sd_img.py
│ │ │ │ ├── regnet-1.6gf_sd_img.py
│ │ │ │ ├── res101_sd_img.py
│ │ │ │ ├── res18_sd_img.py
│ │ │ │ ├── res34_distill_res18_img.py
│ │ │ │ ├── res50_distill_mv1_img.py
│ │ │ │ ├── res50_sd_img.py
│ │ │ │ ├── shuffv2_sd_img.py
│ │ │ │ └── swin-t_sd_img.py
│ │ ├── edgenext
│ │ │ ├── README.md
│ │ │ ├── edgenext-base_8xb256-usi_in1k.py
│ │ │ ├── edgenext-base_8xb256_in1k.py
│ │ │ ├── edgenext-small_8xb256-usi_in1k.py
│ │ │ ├── edgenext-small_8xb256_in1k.py
│ │ │ ├── edgenext-xsmall_8xb256_in1k.py
│ │ │ ├── edgenext-xxsmall_8xb256_in1k.py
│ │ │ └── metafile.yml
│ │ ├── efficientformer
│ │ │ ├── README.md
│ │ │ ├── efficientformer-l1_8xb128_in1k.py
│ │ │ ├── efficientformer-l3_8xb128_in1k.py
│ │ │ ├── efficientformer-l7_8xb128_in1k.py
│ │ │ └── metafile.yml
│ │ ├── efficientnet
│ │ │ ├── README.md
│ │ │ ├── efficientnet-b0_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b0_8xb32_in1k.py
│ │ │ ├── efficientnet-b1_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b1_8xb32_in1k.py
│ │ │ ├── efficientnet-b2_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b2_8xb32_in1k.py
│ │ │ ├── efficientnet-b3_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b3_8xb32_in1k.py
│ │ │ ├── efficientnet-b4_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b4_8xb32_in1k.py
│ │ │ ├── efficientnet-b5_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b5_8xb32_in1k.py
│ │ │ ├── efficientnet-b6_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b6_8xb32_in1k.py
│ │ │ ├── efficientnet-b7_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b7_8xb32_in1k.py
│ │ │ ├── efficientnet-b8_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-b8_8xb32_in1k.py
│ │ │ ├── efficientnet-em_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-es_8xb32-01norm_in1k.py
│ │ │ ├── efficientnet-l2_8xb32_in1k-475px.py
│ │ │ ├── efficientnet-l2_8xb8_in1k-800px.py
│ │ │ └── metafile.yml
│ │ ├── efficientnet_v2
│ │ │ ├── README.md
│ │ │ ├── efficientnetv2-b0_8xb32_in1k.py
│ │ │ ├── efficientnetv2-b1_8xb32_in1k.py
│ │ │ ├── efficientnetv2-b2_8xb32_in1k.py
│ │ │ ├── efficientnetv2-b3_8xb32_in1k.py
│ │ │ ├── efficientnetv2-l_8xb32_in1k-480px.py
│ │ │ ├── efficientnetv2-l_8xb32_in21k.py
│ │ │ ├── efficientnetv2-m_8xb32_in1k-480px.py
│ │ │ ├── efficientnetv2-m_8xb32_in21k.py
│ │ │ ├── efficientnetv2-s_8xb32_in1k-384px.py
│ │ │ ├── efficientnetv2-s_8xb32_in21k.py
│ │ │ ├── efficientnetv2-xl_8xb32_in1k-512px.py
│ │ │ ├── efficientnetv2-xl_8xb32_in21k.py
│ │ │ └── metafile.yml
│ │ ├── eva
│ │ │ ├── README.md
│ │ │ ├── eva-g-p14_8xb16_in1k-336px.py
│ │ │ ├── eva-g-p14_8xb16_in1k-560px.py
│ │ │ ├── eva-g-p14_headless.py
│ │ │ ├── eva-g-p16_headless.py
│ │ │ ├── eva-l-p14_8xb16_in1k-196px.py
│ │ │ ├── eva-l-p14_8xb16_in1k-336px.py
│ │ │ ├── eva-l-p14_headless.py
│ │ │ └── metafile.yml
│ │ ├── hornet
│ │ │ ├── README.md
│ │ │ ├── hornet-base-gf_8xb64_in1k.py
│ │ │ ├── hornet-base_8xb64_in1k.py
│ │ │ ├── hornet-small-gf_8xb64_in1k.py
│ │ │ ├── hornet-small_8xb64_in1k.py
│ │ │ ├── hornet-tiny-gf_8xb128_in1k.py
│ │ │ ├── hornet-tiny_8xb128_in1k.py
│ │ │ └── metafile.yml
│ │ ├── hrnet
│ │ │ ├── README.md
│ │ │ ├── hrnet-w18_4xb32_in1k.py
│ │ │ ├── hrnet-w30_4xb32_in1k.py
│ │ │ ├── hrnet-w32_4xb32_in1k.py
│ │ │ ├── hrnet-w40_4xb32_in1k.py
│ │ │ ├── hrnet-w44_4xb32_in1k.py
│ │ │ ├── hrnet-w48_4xb32_in1k.py
│ │ │ ├── hrnet-w64_4xb32_in1k.py
│ │ │ └── metafile.yml
│ │ ├── inception_v3
│ │ │ ├── README.md
│ │ │ ├── inception-v3_8xb32_in1k.py
│ │ │ └── metafile.yml
│ │ ├── lenet
│ │ │ ├── README.md
│ │ │ └── lenet5_mnist.py
│ │ ├── levit
│ │ │ ├── README.md
│ │ │ ├── deploy
│ │ │ │ ├── levit-128_8xb256_in1k.py
│ │ │ │ ├── levit-128s_8xb256_in1k.py
│ │ │ │ ├── levit-192_8xb256_in1k.py
│ │ │ │ ├── levit-256_8xb256_in1k.py
│ │ │ │ └── levit-384_8xb256_in1k.py
│ │ │ ├── levit-128_8xb256_in1k.py
│ │ │ ├── levit-128s_8xb256_in1k.py
│ │ │ ├── levit-192_8xb256_in1k.py
│ │ │ ├── levit-256_8xb256_in1k.py
│ │ │ ├── levit-384_8xb256_in1k.py
│ │ │ └── metafile.yml
│ │ ├── mixmim
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ └── mixmim-base_8xb64_in1k.py
│ │ ├── mlp_mixer
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── mlp-mixer-base-p16_64xb64_in1k.py
│ │ │ └── mlp-mixer-large-p16_64xb64_in1k.py
│ │ ├── mobilenet_v1
│ │ │ └── mobilenet_v1.py
│ │ ├── mobilenet_v2
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ └── mobilenet-v2_8xb32_in1k.py
│ │ ├── mobilenet_v3
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── mobilenet-v3-large_8xb128_in1k.py
│ │ │ ├── mobilenet-v3-small-050_8xb128_in1k.py
│ │ │ ├── mobilenet-v3-small-075_8xb128_in1k.py
│ │ │ ├── mobilenet-v3-small_8xb128_in1k.py
│ │ │ └── mobilenet-v3-small_8xb16_cifar10.py
│ │ ├── mobileone
│ │ │ ├── README.md
│ │ │ ├── deploy
│ │ │ │ ├── mobileone-s0_deploy_8xb32_in1k.py
│ │ │ │ ├── mobileone-s1_deploy_8xb32_in1k.py
│ │ │ │ ├── mobileone-s2_deploy_8xb32_in1k.py
│ │ │ │ ├── mobileone-s3_deploy_8xb32_in1k.py
│ │ │ │ └── mobileone-s4_deploy_8xb32_in1k.py
│ │ │ ├── metafile.yml
│ │ │ ├── mobileone-s0_8xb32_in1k.py
│ │ │ ├── mobileone-s1_8xb32_in1k.py
│ │ │ ├── mobileone-s2_8xb32_in1k.py
│ │ │ ├── mobileone-s3_8xb32_in1k.py
│ │ │ └── mobileone-s4_8xb32_in1k.py
│ │ ├── mobilevit
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── mobilevit-small_8xb128_in1k.py
│ │ │ ├── mobilevit-xsmall_8xb128_in1k.py
│ │ │ └── mobilevit-xxsmall_8xb128_in1k.py
│ │ ├── mvit
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── mvitv2-base_8xb256_in1k.py
│ │ │ ├── mvitv2-large_8xb256_in1k.py
│ │ │ ├── mvitv2-small_8xb256_in1k.py
│ │ │ └── mvitv2-tiny_8xb256_in1k.py
│ │ ├── poolformer
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── poolformer-m36_32xb128_in1k.py
│ │ │ ├── poolformer-m48_32xb128_in1k.py
│ │ │ ├── poolformer-s12_32xb128_in1k.py
│ │ │ ├── poolformer-s24_32xb128_in1k.py
│ │ │ └── poolformer-s36_32xb128_in1k.py
│ │ ├── regnet
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── regnetx-1.6gf_8xb128_in1k.py
│ │ │ ├── regnetx-12gf_8xb64_in1k.py
│ │ │ ├── regnetx-3.2gf_8xb64_in1k.py
│ │ │ ├── regnetx-4.0gf_8xb64_in1k.py
│ │ │ ├── regnetx-400mf_8xb128_in1k.py
│ │ │ ├── regnetx-6.4gf_8xb64_in1k.py
│ │ │ ├── regnetx-8.0gf_8xb64_in1k.py
│ │ │ └── regnetx-800mf_8xb128_in1k.py
│ │ ├── replknet
│ │ │ ├── README.md
│ │ │ ├── deploy
│ │ │ │ ├── replknet-31B-deploy_32xb64_in1k-384px.py
│ │ │ │ ├── replknet-31B-deploy_32xb64_in1k.py
│ │ │ │ ├── replknet-31L-deploy_32xb64_in1k-384px.py
│ │ │ │ └── replknet-XL-deploy_32xb64_in1k-320px.py
│ │ │ ├── metafile.yml
│ │ │ ├── replknet-31B_32xb64_in1k-384px.py
│ │ │ ├── replknet-31B_32xb64_in1k.py
│ │ │ ├── replknet-31L_32xb64_in1k-384px.py
│ │ │ └── replknet-XL_32xb64_in1k-320px.py
│ │ ├── repmlp
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── repmlp-base_8xb64_in1k-256px.py
│ │ │ ├── repmlp-base_8xb64_in1k.py
│ │ │ ├── repmlp-base_delopy_8xb64_in1k.py
│ │ │ └── repmlp-base_deploy_8xb64_in1k-256px.py
│ │ ├── repvgg
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── repvgg-A0_8xb32_in1k.py
│ │ │ ├── repvgg-A0_deploy_in1k.py
│ │ │ ├── repvgg-A1_8xb32_in1k.py
│ │ │ ├── repvgg-A2_8xb32_in1k.py
│ │ │ ├── repvgg-B0_8xb32_in1k.py
│ │ │ ├── repvgg-B1_8xb32_in1k.py
│ │ │ ├── repvgg-B1g2_8xb32_in1k.py
│ │ │ ├── repvgg-B1g4_8xb32_in1k.py
│ │ │ ├── repvgg-B2_8xb32_in1k.py
│ │ │ ├── repvgg-B2g4_8xb32_in1k.py
│ │ │ ├── repvgg-B3_8xb32_in1k.py
│ │ │ ├── repvgg-B3g4_8xb32_in1k.py
│ │ │ └── repvgg-D2se_8xb32_in1k.py
│ │ ├── res2net
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── res2net101-w26-s4_8xb32_in1k.py
│ │ │ ├── res2net50-w14-s8_8xb32_in1k.py
│ │ │ └── res2net50-w26-s8_8xb32_in1k.py
│ │ ├── resnest
│ │ │ ├── README.md
│ │ │ ├── _randaug_policies.py
│ │ │ ├── resnest101_32xb64_in1k.py
│ │ │ ├── resnest200_64xb32_in1k.py
│ │ │ ├── resnest269_64xb32_in1k.py
│ │ │ └── resnest50_32xb64_in1k.py
│ │ ├── resnet
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── resnet101_8xb16_cifar10.py
│ │ │ ├── resnet101_8xb32_in1k.py
│ │ │ ├── resnet152_8xb16_cifar10.py
│ │ │ ├── resnet152_8xb32_in1k.py
│ │ │ ├── resnet18_8xb16_cifar10.py
│ │ │ ├── resnet18_8xb16_cifar100.py
│ │ │ ├── resnet18_8xb32_in1k.py
│ │ │ ├── resnet18_8xb32_in1k_former_distill.py
│ │ │ ├── resnet18_8xb64_in1k_cos_aug.py
│ │ │ ├── resnet34_8xb16_cifar10.py
│ │ │ ├── resnet34_8xb32_in1k.py
│ │ │ ├── resnet50_32xb64-warmup-coslr_in1k.py
│ │ │ ├── resnet50_32xb64-warmup-lbs_in1k.py
│ │ │ ├── resnet50_32xb64-warmup_in1k.py
│ │ │ ├── resnet50_8xb128_coslr-90e_in21k.py
│ │ │ ├── resnet50_8xb16-mixup_cifar10.py
│ │ │ ├── resnet50_8xb16_cifar10.py
│ │ │ ├── resnet50_8xb16_cifar100.py
│ │ │ ├── resnet50_8xb256-rsb-a1-600e_in1k.py
│ │ │ ├── resnet50_8xb256-rsb-a2-300e_in1k.py
│ │ │ ├── resnet50_8xb256-rsb-a3-100e_in1k.py
│ │ │ ├── resnet50_8xb32-coslr-preciseBN_in1k.py
│ │ │ ├── resnet50_8xb32-coslr_in1k.py
│ │ │ ├── resnet50_8xb32-cutmix_in1k.py
│ │ │ ├── resnet50_8xb32-fp16-dynamic_in1k.py
│ │ │ ├── resnet50_8xb32-fp16_in1k.py
│ │ │ ├── resnet50_8xb32-lbs_in1k.py
│ │ │ ├── resnet50_8xb32-mixup_in1k.py
│ │ │ ├── resnet50_8xb32_in1k.py
│ │ │ ├── resnet50_8xb8_cub.py
│ │ │ ├── resnetv1c101_8xb32_in1k.py
│ │ │ ├── resnetv1c152_8xb32_in1k.py
│ │ │ ├── resnetv1c50_8xb32_in1k.py
│ │ │ ├── resnetv1d101_8xb32_in1k.py
│ │ │ ├── resnetv1d152_8xb32_in1k.py
│ │ │ └── resnetv1d50_8xb32_in1k.py
│ │ ├── resnext
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── resnext101-32x4d_8xb32_in1k.py
│ │ │ ├── resnext101-32x8d_8xb32_in1k.py
│ │ │ ├── resnext152-32x4d_8xb32_in1k.py
│ │ │ └── resnext50-32x4d_8xb32_in1k.py
│ │ ├── revvit
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── revvit-base_8xb256_in1k.py
│ │ │ └── revvit-small_8xb256_in1k.py
│ │ ├── seresnet
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── seresnet101_8xb32_in1k.py
│ │ │ ├── seresnet50_8xb32_in1k.py
│ │ │ ├── seresnext101-32x4d_8xb32_in1k.py
│ │ │ └── seresnext50-32x4d_8xb32_in1k.py
│ │ ├── shufflenet_v1
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ └── shufflenet-v1-1x_16xb64_in1k.py
│ │ ├── shufflenet_v2
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ └── shufflenet-v2-1x_16xb64_in1k.py
│ │ ├── swin_transformer
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── swin-base_16xb64_in1k-384px.py
│ │ │ ├── swin-base_16xb64_in1k.py
│ │ │ ├── swin-large_16xb64_in1k-384px.py
│ │ │ ├── swin-large_16xb64_in1k.py
│ │ │ ├── swin-large_8xb8_cub_384px.py
│ │ │ ├── swin-small_16xb64_in1k.py
│ │ │ └── swin-tiny_16xb64_in1k.py
│ │ ├── swin_transformer_v2
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── swinv2-base-w12_8xb128_in21k-192px.py
│ │ │ ├── swinv2-base-w16_16xb64_in1k-256px.py
│ │ │ ├── swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py
│ │ │ ├── swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py
│ │ │ ├── swinv2-base-w8_16xb64_in1k-256px.py
│ │ │ ├── swinv2-large-w12_8xb128_in21k-192px.py
│ │ │ ├── swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py
│ │ │ ├── swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py
│ │ │ ├── swinv2-small-w16_16xb64_in1k-256px.py
│ │ │ ├── swinv2-small-w8_16xb64_in1k-256px.py
│ │ │ ├── swinv2-tiny-w16_16xb64_in1k-256px.py
│ │ │ └── swinv2-tiny-w8_16xb64_in1k-256px.py
│ │ ├── t2t_vit
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── t2t-vit-t-14_8xb64_in1k.py
│ │ │ ├── t2t-vit-t-19_8xb64_in1k.py
│ │ │ └── t2t-vit-t-24_8xb64_in1k.py
│ │ ├── tinyvit
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── tinyvit-11m-distill_8xb256_in1k.py
│ │ │ ├── tinyvit-11m_8xb256_in1k.py
│ │ │ ├── tinyvit-21m-distill_8xb256_in1k-384px.py
│ │ │ ├── tinyvit-21m-distill_8xb256_in1k-512px.py
│ │ │ ├── tinyvit-21m-distill_8xb256_in1k.py
│ │ │ ├── tinyvit-21m_8xb256_in1k.py
│ │ │ ├── tinyvit-5m-distill_8xb256_in1k.py
│ │ │ └── tinyvit-5m_8xb256_in1k.py
│ │ ├── tnt
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ └── tnt-s-p16_16xb64_in1k.py
│ │ ├── twins
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── twins-pcpvt-base_8xb128_in1k.py
│ │ │ ├── twins-pcpvt-large_16xb64_in1k.py
│ │ │ ├── twins-pcpvt-small_8xb128_in1k.py
│ │ │ ├── twins-svt-base_8xb128_in1k.py
│ │ │ ├── twins-svt-large_16xb64_in1k.py
│ │ │ └── twins-svt-small_8xb128_in1k.py
│ │ ├── van
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── van-base_8xb128_in1k.py
│ │ │ ├── van-large_8xb128_in1k.py
│ │ │ ├── van-small_8xb128_in1k.py
│ │ │ └── van-tiny_8xb128_in1k.py
│ │ ├── vgg
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── vgg11_8xb32_in1k.py
│ │ │ ├── vgg11bn_8xb32_in1k.py
│ │ │ ├── vgg13_8xb32_in1k.py
│ │ │ ├── vgg13bn_8xb32_in1k.py
│ │ │ ├── vgg16_8xb16_voc.py
│ │ │ ├── vgg16_8xb32_in1k.py
│ │ │ ├── vgg16bn_8xb32_in1k.py
│ │ │ ├── vgg19_8xb32_in1k.py
│ │ │ └── vgg19bn_8xb32_in1k.py
│ │ ├── vig
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── pvig-base_8xb128_in1k.py
│ │ │ ├── pvig-medium_8xb128_in1k.py
│ │ │ ├── pvig-small_8xb128_in1k.py
│ │ │ ├── pvig-tiny_8xb128_in1k.py
│ │ │ ├── vig-base_8xb128_in1k.py
│ │ │ ├── vig-small_8xb128_in1k.py
│ │ │ └── vig-tiny_8xb128_in1k.py
│ │ ├── vision_transformer
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── vit-base-p16_ft-4xb544-ipu_in1k.py
│ │ │ ├── vit-base-p16_ft-64xb64_in1k-384.py
│ │ │ ├── vit-base-p16_pt-32xb128-mae_in1k-224.py
│ │ │ ├── vit-base-p16_pt-64xb64_in1k-224.py
│ │ │ ├── vit-base-p32_ft-64xb64_in1k-384.py
│ │ │ ├── vit-base-p32_pt-64xb64_in1k-224.py
│ │ │ ├── vit-large-p16_ft-64xb64_in1k-384.py
│ │ │ ├── vit-large-p16_pt-64xb64_in1k-224.py
│ │ │ ├── vit-large-p32_ft-64xb64_in1k-384.py
│ │ │ └── vit-large-p32_pt-64xb64_in1k-224.py
│ │ ├── wrn
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── wide-resnet101_8xb32_in1k.py
│ │ │ ├── wide-resnet50_8xb32_in1k.py
│ │ │ └── wide-resnet50_timm_8xb32_in1k.py
│ │ └── xcit
│ │ │ ├── README.md
│ │ │ ├── metafile.yml
│ │ │ ├── xcit-large-24-p16_8xb128_in1k-384px.py
│ │ │ ├── xcit-large-24-p16_8xb128_in1k.py
│ │ │ ├── xcit-large-24-p8_8xb128_in1k-384px.py
│ │ │ ├── xcit-large-24-p8_8xb128_in1k.py
│ │ │ ├── xcit-medium-24-p16_8xb128_in1k-384px.py
│ │ │ ├── xcit-medium-24-p16_8xb128_in1k.py
│ │ │ ├── xcit-medium-24-p8_8xb128_in1k-384px.py
│ │ │ ├── xcit-medium-24-p8_8xb128_in1k.py
│ │ │ ├── xcit-nano-12-p16_8xb128_in1k-384px.py
│ │ │ ├── xcit-nano-12-p16_8xb128_in1k.py
│ │ │ ├── xcit-nano-12-p8_8xb128_in1k-384px.py
│ │ │ ├── xcit-nano-12-p8_8xb128_in1k.py
│ │ │ ├── xcit-small-12-p16_8xb128_in1k-384px.py
│ │ │ ├── xcit-small-12-p16_8xb128_in1k.py
│ │ │ ├── xcit-small-12-p8_8xb128_in1k-384px.py
│ │ │ ├── xcit-small-12-p8_8xb128_in1k.py
│ │ │ ├── xcit-small-24-p16_8xb128_in1k-384px.py
│ │ │ ├── xcit-small-24-p16_8xb128_in1k.py
│ │ │ ├── xcit-small-24-p8_8xb128_in1k-384px.py
│ │ │ ├── xcit-small-24-p8_8xb128_in1k.py
│ │ │ ├── xcit-tiny-12-p16_8xb128_in1k-384px.py
│ │ │ ├── xcit-tiny-12-p16_8xb128_in1k.py
│ │ │ ├── xcit-tiny-12-p8_8xb128_in1k-384px.py
│ │ │ ├── xcit-tiny-12-p8_8xb128_in1k.py
│ │ │ ├── xcit-tiny-24-p16_8xb128_in1k-384px.py
│ │ │ ├── xcit-tiny-24-p16_8xb128_in1k.py
│ │ │ ├── xcit-tiny-24-p8_8xb128_in1k-384px.py
│ │ │ └── xcit-tiny-24-p8_8xb128_in1k.py
│ └── tools
│ │ ├── analysis_tools
│ │ ├── analyze_logs.py
│ │ ├── analyze_results.py
│ │ ├── confusion_matrix.py
│ │ ├── eval_metric.py
│ │ └── get_flops.py
│ │ ├── dist_test.sh
│ │ ├── dist_train.sh
│ │ ├── kfold-cross-valid.py
│ │ ├── misc
│ │ ├── print_config.py
│ │ └── verify_dataset.py
│ │ ├── model_converters
│ │ ├── clip_to_mmcls.py
│ │ ├── convnext_to_mmcls.py
│ │ ├── davit_to_mmcls.py
│ │ ├── deit3_to_mmcls.py
│ │ ├── edgenext_to_mmcls.py
│ │ ├── efficientnet_to_mmcls.py
│ │ ├── efficientnetv2_to_mmcls.py
│ │ ├── eva_to_mmcls.py
│ │ ├── hornet2mmcls.py
│ │ ├── levit2mmcls.py
│ │ ├── mixmimx_to_mmcls.py
│ │ ├── mlpmixer_to_mmcls.py
│ │ ├── mobilenetv2_to_mmcls.py
│ │ ├── publish_model.py
│ │ ├── reparameterize_model.py
│ │ ├── replknet_to_mmcls.py
│ │ ├── repvgg_to_mmcls.py
│ │ ├── revvit_to_mmcls.py
│ │ ├── shufflenetv2_to_mmcls.py
│ │ ├── tinyvit_to_mmcls.py
│ │ ├── torchvision_to_mmcls.py
│ │ ├── twins2mmcls.py
│ │ ├── van2mmcls.py
│ │ ├── vgg_to_mmcls.py
│ │ └── vig_to_mmcls.py
│ │ ├── slurm_test.sh
│ │ ├── slurm_train.sh
│ │ ├── test.py
│ │ ├── torchserve
│ │ ├── mmcls2torchserve.py
│ │ ├── mmcls_handler.py
│ │ └── test_torchserver.py
│ │ ├── train.py
│ │ └── visualizations
│ │ ├── browse_dataset.py
│ │ ├── vis_cam.py
│ │ └── vis_scheduler.py
├── __init__.py
├── apis
│ ├── __init__.py
│ ├── inference.py
│ └── model.py
├── datasets
│ ├── __init__.py
│ ├── base_dataset.py
│ ├── builder.py
│ ├── categories.py
│ ├── cifar.py
│ ├── cub.py
│ ├── custom.py
│ ├── dataset_wrappers.py
│ ├── imagenet.py
│ ├── inshop.py
│ ├── mnist.py
│ ├── multi_label.py
│ ├── multi_task.py
│ ├── samplers
│ │ ├── __init__.py
│ │ └── repeat_aug.py
│ ├── transforms
│ │ ├── __init__.py
│ │ ├── auto_augment.py
│ │ ├── formatting.py
│ │ └── processing.py
│ ├── utils.py
│ └── voc.py
├── engine
│ ├── __init__.py
│ ├── hooks
│ │ ├── __init__.py
│ │ ├── class_num_check_hook.py
│ │ ├── ema_hook.py
│ │ ├── margin_head_hooks.py
│ │ ├── precise_bn_hook.py
│ │ ├── retriever_hooks.py
│ │ ├── switch_recipe_hook.py
│ │ └── visualization_hook.py
│ └── optimizers
│ │ ├── __init__.py
│ │ ├── adan_t.py
│ │ └── lamb.py
├── evaluation
│ ├── __init__.py
│ └── metrics
│ │ ├── __init__.py
│ │ ├── corruption_error.py
│ │ ├── multi_label.py
│ │ ├── multi_task.py
│ │ ├── retrieval.py
│ │ ├── single_label.py
│ │ └── voc_multi_label.py
├── models
│ ├── __init__.py
│ ├── backbones
│ │ ├── __init__.py
│ │ ├── alexnet.py
│ │ ├── base_backbone.py
│ │ ├── beit.py
│ │ ├── bit_resnet.py
│ │ ├── conformer.py
│ │ ├── convmixer.py
│ │ ├── convnext.py
│ │ ├── cspnet.py
│ │ ├── davit.py
│ │ ├── deit.py
│ │ ├── deit3.py
│ │ ├── densenet.py
│ │ ├── edgenext.py
│ │ ├── efficientformer.py
│ │ ├── efficientnet.py
│ │ ├── efficientnet_v2.py
│ │ ├── gmlp.py
│ │ ├── hornet.py
│ │ ├── hrnet.py
│ │ ├── inception_v3.py
│ │ ├── lenet.py
│ │ ├── levit.py
│ │ ├── mbv2.old
│ │ ├── mixmim.py
│ │ ├── mlp_mixer.py
│ │ ├── mobilenet_v1.py
│ │ ├── mobilenet_v2.py
│ │ ├── mobilenet_v3.py
│ │ ├── mobileone.py
│ │ ├── mobilevit.py
│ │ ├── mvit.py
│ │ ├── poolformer.py
│ │ ├── regnet.py
│ │ ├── replknet.py
│ │ ├── repmlp.py
│ │ ├── repvgg.py
│ │ ├── res2net.py
│ │ ├── resnest.py
│ │ ├── resnet.py
│ │ ├── resnet_cifar.py
│ │ ├── resnext.py
│ │ ├── revvit.py
│ │ ├── seresnet.py
│ │ ├── seresnext.py
│ │ ├── shufflenet_v1.py
│ │ ├── shufflenet_v2.py
│ │ ├── swin_transformer.py
│ │ ├── swin_transformer_v2.py
│ │ ├── t2t_vit.py
│ │ ├── timm_backbone.py
│ │ ├── tinyvit.py
│ │ ├── tnt.py
│ │ ├── twins.py
│ │ ├── van.py
│ │ ├── vgg.py
│ │ ├── vig.py
│ │ ├── vision_transformer.py
│ │ ├── vit.py
│ │ ├── vit_eva02.py
│ │ └── xcit.py
│ ├── builder.py
│ ├── classifiers
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── distiller.py
│ │ ├── hugging_face.py
│ │ ├── image.py
│ │ └── timm.py
│ ├── dis_losses
│ │ ├── __init__.py
│ │ ├── attention.py
│ │ ├── fd.py
│ │ ├── fmd.py
│ │ ├── fmd_convnext.py
│ │ ├── fmdv2.py
│ │ └── kd.py
│ ├── heads
│ │ ├── __init__.py
│ │ ├── base_head.py
│ │ ├── cls_head.py
│ │ ├── conformer_head.py
│ │ ├── deit_head.py
│ │ ├── efficientformer_head.py
│ │ ├── levit_head.py
│ │ ├── linear_head.py
│ │ ├── margin_head.py
│ │ ├── multi_label_cls_head.py
│ │ ├── multi_label_csra_head.py
│ │ ├── multi_label_linear_head.py
│ │ ├── multi_task_head.py
│ │ ├── stacked_head.py
│ │ ├── vig_head.py
│ │ └── vision_transformer_head.py
│ ├── losses
│ │ ├── __init__.py
│ │ ├── asymmetric_loss.py
│ │ ├── cross_entropy_loss.py
│ │ ├── focal_loss.py
│ │ ├── label_smooth_loss.py
│ │ ├── seesaw_loss.py
│ │ └── utils.py
│ ├── necks
│ │ ├── __init__.py
│ │ ├── gap.py
│ │ ├── gem.py
│ │ ├── hr_fuse.py
│ │ └── reduction.py
│ ├── retrievers
│ │ ├── __init__.py
│ │ ├── base.py
│ │ └── image2image.py
│ ├── tta
│ │ ├── __init__.py
│ │ └── score_tta.py
│ └── utils
│ │ ├── __init__.py
│ │ ├── attention.py
│ │ ├── batch_augments
│ │ ├── __init__.py
│ │ ├── cutmix.py
│ │ ├── mixup.py
│ │ ├── resizemix.py
│ │ └── wrapper.py
│ │ ├── channel_shuffle.py
│ │ ├── data_preprocessor.py
│ │ ├── embed.py
│ │ ├── helpers.py
│ │ ├── inverted_residual.py
│ │ ├── layer_scale.py
│ │ ├── make_divisible.py
│ │ ├── norm.py
│ │ ├── position_encoding.py
│ │ ├── se_layer.py
│ │ └── swiglu_ffn.py
├── registry.py
├── structures
│ ├── __init__.py
│ ├── cls_data_sample.py
│ ├── multi_task_data_sample.py
│ └── utils.py
├── utils
│ ├── __init__.py
│ ├── analyze.py
│ ├── collect_env.py
│ ├── progress.py
│ └── setup_env.py
├── version.py
└── visualization
│ ├── __init__.py
│ └── cls_visualizer.py
├── projects
├── README.md
├── example_project
│ ├── README.md
│ ├── configs
│ │ └── examplenet_8xb32_in1k.py
│ └── models
│ │ ├── __init__.py
│ │ └── example_net.py
├── fgia_accv2022_1st
│ └── README.md
└── ood_eval
│ ├── README.md
│ ├── config
│ ├── vit_ood-eval_toy-example.py
│ └── vit_ood-eval_toy-example_imagenet-c.py
│ └── generate_imagenet_variant_annotation.py
├── pth_transfer.py
├── requirements.txt
├── requirements
├── docs.txt
├── mminstall.txt
├── optional.txt
├── readthedocs.txt
├── runtime.txt
└── tests.txt
├── resources
└── mmcls-logo.png
├── run.sh
├── setup.cfg
├── setup.py
├── tests
├── __init__.py
├── data
│ ├── color.jpg
│ ├── dataset
│ │ ├── a
│ │ │ └── 1.JPG
│ │ ├── ann.json
│ │ ├── ann.txt
│ │ ├── b
│ │ │ ├── 2.jpeg
│ │ │ └── subb
│ │ │ │ └── 3.jpg
│ │ ├── classes.txt
│ │ ├── multi-task.json
│ │ └── multi_label_ann.json
│ ├── gray.jpg
│ ├── meta.yml
│ ├── retinanet.py
│ └── test.logjson
├── test_apis
│ ├── test_inference.py
│ └── test_model.py
├── test_datasets
│ ├── test_dataset_utils.py
│ ├── test_datasets.py
│ ├── test_samplers
│ │ └── test_repeat_aug.py
│ └── test_transforms
│ │ ├── test_auto_augment.py
│ │ ├── test_formatting.py
│ │ └── test_processing.py
├── test_engine
│ └── test_hooks
│ │ ├── test_arcface_hooks.py
│ │ ├── test_class_num_check_hook.py
│ │ ├── test_ema_hook.py
│ │ ├── test_precise_bn_hook.py
│ │ ├── test_retrievers_hooks.py
│ │ ├── test_switch_recipe_hook.py
│ │ └── test_visualization_hook.py
├── test_evaluation
│ └── test_metrics
│ │ ├── test_corruption_error.py
│ │ ├── test_metric_utils.py
│ │ ├── test_multi_label.py
│ │ ├── test_multi_task_metrics.py
│ │ ├── test_retrieval.py
│ │ ├── test_single_label.py
│ │ └── test_voc_metrics.py
├── test_models
│ ├── test_backbones
│ │ ├── __init__.py
│ │ ├── test_beit.py
│ │ ├── test_conformer.py
│ │ ├── test_convmixer.py
│ │ ├── test_convnext.py
│ │ ├── test_cspnet.py
│ │ ├── test_davit.py
│ │ ├── test_deit.py
│ │ ├── test_deit3.py
│ │ ├── test_densenet.py
│ │ ├── test_edgenext.py
│ │ ├── test_efficientformer.py
│ │ ├── test_efficientnet.py
│ │ ├── test_efficientnet_v2.py
│ │ ├── test_hornet.py
│ │ ├── test_hrnet.py
│ │ ├── test_inception_v3.py
│ │ ├── test_levit.py
│ │ ├── test_mixmim.py
│ │ ├── test_mlp_mixer.py
│ │ ├── test_mobilenet_v2.py
│ │ ├── test_mobilenet_v3.py
│ │ ├── test_mobileone.py
│ │ ├── test_mobilevit.py
│ │ ├── test_mvit.py
│ │ ├── test_poolformer.py
│ │ ├── test_regnet.py
│ │ ├── test_replknet.py
│ │ ├── test_repmlp.py
│ │ ├── test_repvgg.py
│ │ ├── test_res2net.py
│ │ ├── test_resnest.py
│ │ ├── test_resnet.py
│ │ ├── test_resnet_cifar.py
│ │ ├── test_resnext.py
│ │ ├── test_revvit.py
│ │ ├── test_seresnet.py
│ │ ├── test_seresnext.py
│ │ ├── test_shufflenet_v1.py
│ │ ├── test_shufflenet_v2.py
│ │ ├── test_swin_transformer.py
│ │ ├── test_swin_transformer_v2.py
│ │ ├── test_t2t_vit.py
│ │ ├── test_timm_backbone.py
│ │ ├── test_tinyvit.py
│ │ ├── test_tnt.py
│ │ ├── test_twins.py
│ │ ├── test_van.py
│ │ ├── test_vgg.py
│ │ ├── test_vision_transformer.py
│ │ ├── test_xcit.py
│ │ └── utils.py
│ ├── test_classifiers.py
│ ├── test_heads.py
│ ├── test_losses.py
│ ├── test_models.py
│ ├── test_necks.py
│ ├── test_retrievers.py
│ ├── test_tta.py
│ └── test_utils
│ │ ├── test_attention.py
│ │ ├── test_batch_augments.py
│ │ ├── test_data_preprocessor.py
│ │ ├── test_embed.py
│ │ ├── test_inverted_residual.py
│ │ ├── test_layer_scale.py
│ │ ├── test_misc.py
│ │ ├── test_norm.py
│ │ ├── test_position_encoding.py
│ │ └── test_se.py
├── test_structures
│ ├── test_datasample.py
│ └── test_utils.py
├── test_utils
│ ├── test_analyze.py
│ ├── test_setup_env.py
│ └── test_version_utils.py
└── test_visualizations
│ └── test_visualizer.py
└── tools
├── analysis_tools
├── analyze_logs.py
├── analyze_results.py
├── confusion_matrix.py
├── eval_metric.py
└── get_flops.py
├── dist_test.sh
├── dist_train.sh
├── kfold-cross-valid.py
├── misc
├── print_config.py
└── verify_dataset.py
├── model_converters
├── clip_to_mmcls.py
├── convnext_to_mmcls.py
├── davit_to_mmcls.py
├── deit3_to_mmcls.py
├── edgenext_to_mmcls.py
├── efficientnet_to_mmcls.py
├── efficientnetv2_to_mmcls.py
├── eva_to_mmcls.py
├── hornet2mmcls.py
├── levit2mmcls.py
├── mixmimx_to_mmcls.py
├── mlpmixer_to_mmcls.py
├── mobilenetv2_to_mmcls.py
├── publish_model.py
├── reparameterize_model.py
├── replknet_to_mmcls.py
├── repvgg_to_mmcls.py
├── revvit_to_mmcls.py
├── shufflenetv2_to_mmcls.py
├── tinyvit_to_mmcls.py
├── torchvision_to_mmcls.py
├── twins2mmcls.py
├── van2mmcls.py
├── vgg_to_mmcls.py
└── vig_to_mmcls.py
├── slurm_test.sh
├── slurm_train.sh
├── test.py
├── torchserve
├── mmcls2torchserve.py
├── mmcls_handler.py
└── test_torchserver.py
├── train.py
└── visualizations
├── browse_dataset.py
├── vis_cam.py
└── vis_scheduler.py
/.gitignore:
--------------------------------------------------------------------------------
1 | /ckpt
2 | /data
3 | /work_dirs
4 | *.pyc
5 | *.pkl
6 | reference.md
7 | /configs/distillers/next_week
--------------------------------------------------------------------------------
/build/lib/mmcls/apis/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .inference import ImageClassificationInferencer, inference_model
3 | from .model import ModelHub, get_model, init_model, list_models
4 |
5 | __all__ = [
6 | 'init_model', 'inference_model', 'list_models', 'get_model', 'ModelHub',
7 | 'ImageClassificationInferencer'
8 | ]
9 |
--------------------------------------------------------------------------------
/build/lib/mmcls/datasets/samplers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .repeat_aug import RepeatAugSampler
3 |
4 | __all__ = ('RepeatAugSampler', )
5 |
--------------------------------------------------------------------------------
/build/lib/mmcls/engine/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .hooks import * # noqa: F401, F403
3 | from .optimizers import * # noqa: F401, F403
4 |
--------------------------------------------------------------------------------
/build/lib/mmcls/engine/optimizers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .adan_t import Adan
3 | from .lamb import Lamb
4 |
5 | __all__ = ['Lamb', 'Adan']
6 |
--------------------------------------------------------------------------------
/build/lib/mmcls/evaluation/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .metrics import * # noqa: F401,F403
3 |
--------------------------------------------------------------------------------
/build/lib/mmcls/models/dis_losses/__init__.py:
--------------------------------------------------------------------------------
1 | from .kd import KDLoss
2 | from .fd import FDLoss
3 | from .tfd import TFDLoss
4 | from .mfd import MultiStageFDLoss
5 |
6 |
7 | __all__ = [
8 | 'KDLoss', 'FDLoss' , 'TFDLoss', 'MultiStageFDLoss',
9 | ]
10 |
--------------------------------------------------------------------------------
/build/lib/mmcls/models/necks/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .gap import GlobalAveragePooling
3 | from .gem import GeneralizedMeanPooling
4 | from .hr_fuse import HRFuseScales
5 | from .reduction import LinearReduction
6 |
7 | __all__ = [
8 | 'GlobalAveragePooling', 'GeneralizedMeanPooling', 'HRFuseScales',
9 | 'LinearReduction'
10 | ]
11 |
--------------------------------------------------------------------------------
/build/lib/mmcls/models/retrievers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .base import BaseRetriever
3 | from .image2image import ImageToImageRetriever
4 |
5 | __all__ = ['BaseRetriever', 'ImageToImageRetriever']
6 |
--------------------------------------------------------------------------------
/build/lib/mmcls/models/tta/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .score_tta import AverageClsScoreTTA
3 |
4 | __all__ = ['AverageClsScoreTTA']
5 |
--------------------------------------------------------------------------------
/build/lib/mmcls/models/utils/batch_augments/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .cutmix import CutMix
3 | from .mixup import Mixup
4 | from .resizemix import ResizeMix
5 | from .wrapper import RandomBatchAugment
6 |
7 | __all__ = ('RandomBatchAugment', 'CutMix', 'Mixup', 'ResizeMix')
8 |
--------------------------------------------------------------------------------
/build/lib/mmcls/structures/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .cls_data_sample import ClsDataSample
3 | from .multi_task_data_sample import MultiTaskDataSample
4 | from .utils import (batch_label_to_onehot, cat_batch_labels,
5 | stack_batch_scores, tensor_split)
6 |
7 | __all__ = [
8 | 'ClsDataSample', 'batch_label_to_onehot', 'cat_batch_labels',
9 | 'stack_batch_scores', 'tensor_split', 'MultiTaskDataSample'
10 | ]
11 |
--------------------------------------------------------------------------------
/build/lib/mmcls/structures/multi_task_data_sample.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
3 | from mmengine.structures import BaseDataElement
4 |
5 |
6 | class MultiTaskDataSample(BaseDataElement):
7 |
8 | @property
9 | def tasks(self):
10 | return self._data_fields
11 |
--------------------------------------------------------------------------------
/build/lib/mmcls/utils/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .analyze import load_json_log
3 | from .collect_env import collect_env
4 | from .progress import track_on_main_process
5 | from .setup_env import register_all_modules
6 |
7 | __all__ = [
8 | 'collect_env', 'register_all_modules', 'track_on_main_process',
9 | 'load_json_log'
10 | ]
11 |
--------------------------------------------------------------------------------
/build/lib/mmcls/utils/collect_env.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from mmengine.utils import get_git_hash
3 | from mmengine.utils.dl_utils import collect_env as collect_base_env
4 |
5 | import mmcls
6 |
7 |
8 | def collect_env():
9 | """Collect the information of the running environments."""
10 | env_info = collect_base_env()
11 | env_info['MMClassification'] = mmcls.__version__ + '+' + get_git_hash()[:7]
12 | return env_info
13 |
--------------------------------------------------------------------------------
/build/lib/mmcls/utils/progress.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | import mmengine.dist as dist
3 | import rich.progress as progress
4 |
5 |
6 | def track_on_main_process(sequence, *args, **kwargs):
7 | if not dist.is_main_process():
8 | yield from sequence
9 | else:
10 | yield from progress.track(sequence, *args, **kwargs)
11 |
--------------------------------------------------------------------------------
/build/lib/mmcls/visualization/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .cls_visualizer import ClsVisualizer
3 |
4 | __all__ = ['ClsVisualizer']
5 |
--------------------------------------------------------------------------------
/configs/_base_/models/convmixer/convmixer-1024-20.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ConvMixer', arch='1024/20'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/convmixer/convmixer-1536-20.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ConvMixer', arch='1536/20'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1536,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/convmixer/convmixer-768-32.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ConvMixer', arch='768/32', act_cfg=dict(type='ReLU')),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=768,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/densenet/densenet121.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='121'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/densenet/densenet161.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='161'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2208,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/densenet/densenet169.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='169'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1664,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/densenet/densenet201.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='201'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1920,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_b1.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b1'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_b2.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b2'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1408,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_b4.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b4'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1792,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_b5.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b5'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2048,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_b6.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b6'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2304,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_b7.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b7'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2560,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_b8.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b8'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2816,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_l2.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='l2'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=5504,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_b0.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b0'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_b1.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b1'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_b2.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b2'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1408,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_b3.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b3'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1536,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_l.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='l'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_m.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='m'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_s.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='s'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/efficientnet_v2/efficientnetv2_xl.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='xl'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/inception_v3.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='InceptionV3', num_classes=1000, aux_logits=False),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5)),
10 | )
11 |
--------------------------------------------------------------------------------
/configs/_base_/models/mobilenet_v1_1x.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileNetV1'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/mobilevit/mobilevit_s.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileViT', arch='small'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=640,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/mobilevit/mobilevit_xs.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileViT', arch='x_small'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=384,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/mobilevit/mobilevit_xxs.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileViT', arch='xx_small'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=320,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_1.6gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_1.6gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=912,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_12gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_12gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2240,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_3.2gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_3.2gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1008,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_4.0gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_4.0gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1360,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_400mf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_400mf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=384,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_6.4gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_6.4gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1624,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_8.0gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_8.0gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1920,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/regnet/regnetx_800mf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_800mf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=672,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/replknet-31L_in1k.py:
--------------------------------------------------------------------------------
1 | model = dict(
2 | type='ImageClassifier',
3 | backbone=dict(
4 | type='RepLKNet',
5 | arch='31L',
6 | out_indices=(3, ),
7 | ),
8 | neck=dict(type='GlobalAveragePooling'),
9 | head=dict(
10 | type='LinearClsHead',
11 | num_classes=1000,
12 | in_channels=1536,
13 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
14 | topk=(1, 5),
15 | ))
16 |
--------------------------------------------------------------------------------
/configs/_base_/models/replknet-XL_in1k.py:
--------------------------------------------------------------------------------
1 | model = dict(
2 | type='ImageClassifier',
3 | backbone=dict(
4 | type='RepLKNet',
5 | arch='XL',
6 | out_indices=(3, ),
7 | ),
8 | neck=dict(type='GlobalAveragePooling'),
9 | head=dict(
10 | type='LinearClsHead',
11 | num_classes=1000,
12 | in_channels=2048,
13 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
14 | topk=(1, 5),
15 | ))
16 |
--------------------------------------------------------------------------------
/configs/_base_/models/repvgg-A0_in1k.py:
--------------------------------------------------------------------------------
1 | model = dict(
2 | type='ImageClassifier',
3 | backbone=dict(
4 | type='RepVGG',
5 | arch='A0',
6 | out_indices=(3, ),
7 | ),
8 | neck=dict(type='GlobalAveragePooling'),
9 | head=dict(
10 | type='LinearClsHead',
11 | num_classes=1000,
12 | in_channels=1280,
13 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
14 | topk=(1, 5),
15 | ))
16 |
--------------------------------------------------------------------------------
/configs/_base_/models/shufflenet_v1_1x.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ShuffleNetV1', groups=3),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=960,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/shufflenet_v2_1x.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ShuffleNetV2', widen_factor=1.0),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg11.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=11, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg11bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=11, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg13.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=13, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg13bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=13, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg16.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=16, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg16bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=16, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg19.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=19, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/configs/_base_/models/vgg19bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=19, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/configs/clip/vit-base-p16_pt-64xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_clip_384.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/configs/clip/vit-base-p16_pt-64xb64_in1k-448px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_clip_448.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/configs/clip/vit-base-p16_pt-64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_clip_224.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/configs/clip/vit-base-p32_pt-64xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p32.py',
3 | '../_base_/datasets/imagenet_bs64_clip_384.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/configs/clip/vit-base-p32_pt-64xb64_in1k-448px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p32.py',
3 | '../_base_/datasets/imagenet_bs64_clip_448.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/configs/clip/vit-base-p32_pt-64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p32.py',
3 | '../_base_/datasets/imagenet_bs64_clip_224.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/configs/conformer/conformer-base-p16_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/configs/conformer/conformer-small-p16_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/small-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/configs/conformer/conformer-small-p32_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/small-p32.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/configs/conformer/conformer-tiny-p16_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/tiny-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/configs/davit/davit-base_4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/davit/davit-base.py',
3 | '../_base_/datasets/imagenet_bs256_davit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # data settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/configs/davit/davit-small_4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/davit/davit-small.py',
3 | '../_base_/datasets/imagenet_bs256_davit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # data settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/configs/davit/davit-tiny_4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/davit/davit-tiny.py',
3 | '../_base_/datasets/imagenet_bs256_davit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # data settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-base_ft-16xb32_in1k-384px.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer'),
6 | head=dict(type='DeiTClsHead'),
7 | # Change to the path of the pretrained model
8 | # init_cfg=dict(type='Pretrained', checkpoint=''),
9 | )
10 |
--------------------------------------------------------------------------------
/configs/deit/deit-base-distilled_pt-16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-base'),
6 | head=dict(type='DeiTClsHead', in_channels=768),
7 | )
8 |
9 | # dataset settings
10 | train_dataloader = dict(batch_size=64)
11 |
--------------------------------------------------------------------------------
/configs/deit/deit-base_pt-16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(
6 | type='VisionTransformer', arch='deit-base', drop_path_rate=0.1),
7 | head=dict(type='VisionTransformerClsHead', in_channels=768),
8 | )
9 |
10 | # dataset settings
11 | train_dataloader = dict(batch_size=64)
12 |
13 | # runtime settings
14 | custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
15 |
--------------------------------------------------------------------------------
/configs/deit/deit-small-distilled_pt-4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-small'),
6 | head=dict(type='DeiTClsHead', in_channels=384),
7 | )
8 |
--------------------------------------------------------------------------------
/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-tiny'),
6 | head=dict(type='DeiTClsHead', in_channels=192),
7 | )
8 |
--------------------------------------------------------------------------------
/configs/efficientformer/efficientformer-l1_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/efficientformer-l1.py',
3 | '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/efficientformer/efficientformer-l3_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './efficientformer-l1_8xb128_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='l3'), head=dict(in_channels=512))
4 |
--------------------------------------------------------------------------------
/configs/efficientformer/efficientformer-l7_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './efficientformer-l1_8xb128_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='l7'), head=dict(in_channels=768))
4 |
--------------------------------------------------------------------------------
/configs/efficientnet_v2/efficientnetv2-l_8xb32_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./efficientnetv2-s_8xb32_in21k.py']
2 |
3 | # model setting
4 | model = dict(backbone=dict(arch='l'), )
5 |
--------------------------------------------------------------------------------
/configs/efficientnet_v2/efficientnetv2-m_8xb32_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./efficientnetv2-s_8xb32_in21k.py']
2 |
3 | # model setting
4 | model = dict(backbone=dict(arch='m'), )
5 |
--------------------------------------------------------------------------------
/configs/efficientnet_v2/efficientnetv2-xl_8xb32_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./efficientnetv2-s_8xb32_in21k.py']
2 |
3 | # model setting
4 | model = dict(backbone=dict(arch='xl'), )
5 |
--------------------------------------------------------------------------------
/configs/eva/eva-g-p14_8xb16_in1k-336px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-g.py',
3 | '../_base_/datasets/imagenet_bs16_eva_336.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=336))
10 |
--------------------------------------------------------------------------------
/configs/eva/eva-g-p14_8xb16_in1k-560px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-g.py',
3 | '../_base_/datasets/imagenet_bs16_eva_560.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=560))
10 |
--------------------------------------------------------------------------------
/configs/eva/eva-l-p14_8xb16_in1k-196px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-l.py',
3 | '../_base_/datasets/imagenet_bs16_eva_196.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=196))
10 |
--------------------------------------------------------------------------------
/configs/eva/eva-l-p14_8xb16_in1k-224px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-l.py',
3 | '../_base_/datasets/imagenet_bs16_eva_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=224))
10 |
--------------------------------------------------------------------------------
/configs/eva/eva-l-p14_8xb16_in1k-336px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-l.py',
3 | '../_base_/datasets/imagenet_bs16_eva_336.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=336))
10 |
--------------------------------------------------------------------------------
/configs/hornet/hornet-base-gf_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hornet/hornet-base-gf.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | data = dict(samples_per_gpu=64)
9 |
10 | optim_wrapper = dict(optimizer=dict(lr=4e-3), clip_grad=dict(max_norm=1.0))
11 |
12 | custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
13 |
--------------------------------------------------------------------------------
/configs/hornet/hornet-base_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hornet/hornet-base.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | data = dict(samples_per_gpu=64)
9 |
10 | optim_wrapper = dict(optimizer=dict(lr=4e-3), clip_grad=dict(max_norm=5.0))
11 |
12 | custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
13 |
--------------------------------------------------------------------------------
/configs/hornet/hornet-small-gf_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hornet/hornet-small-gf.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | data = dict(samples_per_gpu=64)
9 |
10 | optim_wrapper = dict(optimizer=dict(lr=4e-3), clip_grad=dict(max_norm=1.0))
11 |
12 | custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
13 |
--------------------------------------------------------------------------------
/configs/hornet/hornet-small_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hornet/hornet-small.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | data = dict(samples_per_gpu=64)
9 |
10 | optim_wrapper = dict(optimizer=dict(lr=4e-3), clip_grad=dict(max_norm=5.0))
11 |
12 | custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
13 |
--------------------------------------------------------------------------------
/configs/hornet/hornet-tiny-gf_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hornet/hornet-tiny-gf.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | data = dict(samples_per_gpu=128)
9 |
10 | optim_wrapper = dict(optimizer=dict(lr=4e-3), clip_grad=dict(max_norm=1.0))
11 |
12 | custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
13 |
--------------------------------------------------------------------------------
/configs/hornet/hornet-tiny_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hornet/hornet-tiny.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | data = dict(samples_per_gpu=128)
9 |
10 | optim_wrapper = dict(optimizer=dict(lr=4e-3), clip_grad=dict(max_norm=100.0))
11 |
12 | custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
13 |
--------------------------------------------------------------------------------
/configs/hrnet/hrnet-w18_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w18.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/configs/hrnet/hrnet-w30_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w30.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/configs/hrnet/hrnet-w32_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w32.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/configs/hrnet/hrnet-w40_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w40.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/configs/hrnet/hrnet-w44_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w44.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/configs/hrnet/hrnet-w48_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w48.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/configs/hrnet/hrnet-w64_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w64.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/configs/levit/deploy/levit-128_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-128_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/levit/deploy/levit-128s_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-128s_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/levit/deploy/levit-192_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-192_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/levit/deploy/levit-256_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-256_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/levit/deploy/levit-384_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-384_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/levit/levit-128_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(arch='128'), head=dict(in_channels=384))
10 |
11 | # dataset settings
12 | train_dataloader = dict(batch_size=256)
13 |
--------------------------------------------------------------------------------
/configs/levit/levit-128s_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(arch='128s'), head=dict(in_channels=384))
10 |
11 | # dataset settings
12 | train_dataloader = dict(batch_size=256)
13 |
--------------------------------------------------------------------------------
/configs/levit/levit-192_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(arch='192'), head=dict(in_channels=384))
10 |
11 | # dataset settings
12 | train_dataloader = dict(batch_size=256)
13 |
--------------------------------------------------------------------------------
/configs/levit/levit-256_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # dataset settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/configs/levit/levit-384_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # model settings
9 | model = dict(
10 | backbone=dict(arch='384', drop_path_rate=0.1),
11 | head=dict(in_channels=768),
12 | )
13 |
14 | # dataset settings
15 | train_dataloader = dict(batch_size=256)
16 |
--------------------------------------------------------------------------------
/configs/mixmim/mixmim-base_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mixmim/mixmim_base.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mlp_mixer_base_patch16.py',
3 | '../_base_/datasets/imagenet_bs128_mlp_224.py',
4 | '../_base_/schedules/imagenet_bs1024_AdamW_mlp.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # configs/_base_/models/mlp_mixer_base_patch14.py
9 |
10 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
11 |
--------------------------------------------------------------------------------
/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mlp_mixer_large_patch16.py',
3 | '../_base_/datasets/imagenet_bs128_mlp_224.py',
4 | '../_base_/schedules/imagenet_bs1024_AdamW_mlp.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
9 |
--------------------------------------------------------------------------------
/configs/mobilenet_v1/mobilenet_v1.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mobilenet_v1_1x.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 |
9 |
--------------------------------------------------------------------------------
/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mobilenet_v2_1x.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_cos_lr_SGD_150e.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(
9 | backbone=dict(
10 | out_indices=(2,3,4,7))
11 | )
--------------------------------------------------------------------------------
/configs/mobileone/deploy/mobileone-s0_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s0_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/mobileone/deploy/mobileone-s1_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s1_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/mobileone/deploy/mobileone-s2_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s2_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/mobileone/deploy/mobileone-s3_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s3_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/mobileone/deploy/mobileone-s4_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s4_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/regnet/regnetx-1.6gf_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./regnetx-400mf_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='RegNet', arch='regnetx_1.6gf'),
6 | head=dict(in_channels=912, ))
7 |
--------------------------------------------------------------------------------
/configs/regnet/regnetx-800mf_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./regnetx-400mf_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='RegNet', arch='regnetx_800mf'),
6 | head=dict(in_channels=672, ))
7 |
--------------------------------------------------------------------------------
/configs/replknet/deploy/replknet-31B-deploy_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-31B_32xb64_in1k-384px.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/configs/replknet/deploy/replknet-31B-deploy_32xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-31B_32xb64_in1k.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/configs/replknet/deploy/replknet-31L-deploy_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-31L_32xb64_in1k-384px.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/configs/replknet/deploy/replknet-XL-deploy_32xb64_in1k-320px.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-XL_32xb64_in1k-320px.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/configs/replknet/replknet-31B_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-31B_in1k.py',
3 | '../_base_/datasets/imagenet_bs16_pil_bicubic_384.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/configs/replknet/replknet-31B_32xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-31B_in1k.py',
3 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/configs/replknet/replknet-31L_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-31L_in1k.py',
3 | '../_base_/datasets/imagenet_bs16_pil_bicubic_384.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/configs/replknet/replknet-XL_32xb64_in1k-320px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-XL_in1k.py',
3 | '../_base_/datasets/imagenet_bs8_pil_bicubic_320.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./repmlp-base_8xb64_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/repmlp/repmlp-base_deploy_8xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./repmlp-base_8xb64_in1k-256px.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-A0_deploy_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-A1_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='A1'))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-A2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='A2'), head=dict(in_channels=1408))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-B0_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B0'), head=dict(in_channels=1280))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-B1_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B1'), head=dict(in_channels=2048))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-B1g2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B1g2'), head=dict(in_channels=2048))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-B1g4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B1g4'), head=dict(in_channels=2048))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-B2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B2'), head=dict(in_channels=2560))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-B2g4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-B3_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B2g4'), head=dict(in_channels=2560))
4 |
--------------------------------------------------------------------------------
/configs/repvgg/repvgg-B3g4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-B3_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B3g4'))
4 |
--------------------------------------------------------------------------------
/configs/res2net/res2net101-w26-s4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/res2net101-w26-s4.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/res2net/res2net50-w14-s8_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/res2net50-w14-s8.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/res2net/res2net50-w26-s8_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/res2net50-w26-s8.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/bit_resnet50_8xb32_in1k_strong_aug_coslr_300.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/bit_resnet50.py', '../_base_/datasets/imagenet_bs256_resnet_224.py',
3 | '../_base_/schedules/imagenet_bs2048_coslr_Lamb.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet101_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet101_cifar.py',
3 | '../_base_/datasets/cifar10_bs16.py',
4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet101.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
6 | model = dict(
7 | backbone=dict(
8 | out_indices=(2,3))
9 | )
--------------------------------------------------------------------------------
/configs/resnet/resnet152_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet152_cifar.py',
3 | '../_base_/datasets/cifar10_bs16.py',
4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet152_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet152.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet18_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet18.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
6 | model = dict(
7 | backbone=dict(
8 | out_indices=(2,3))
9 | )
--------------------------------------------------------------------------------
/configs/resnet/resnet18_8xb32_in1k_former_distill.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet18.py', '../_base_/datasets/imagenet_bs32_swin_224.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 | model = dict(
6 | backbone=dict(
7 | out_indices=(2,3))
8 | )
--------------------------------------------------------------------------------
/configs/resnet/resnet34_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet34_cifar.py', '../_base_/datasets/cifar10_bs16.py',
3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet34_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet34.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_32xb64-warmup-coslr_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py',
3 | '../_base_/schedules/imagenet_bs2048_coslr.py',
4 | '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_32xb64-warmup-lbs_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./resnet50_32xb64-warmup_in1k.py']
2 | model = dict(
3 | head=dict(
4 | type='LinearClsHead',
5 | num_classes=1000,
6 | in_channels=2048,
7 | loss=dict(
8 | type='LabelSmoothLoss',
9 | loss_weight=1.0,
10 | label_smooth_val=0.1,
11 | num_classes=1000),
12 | ))
13 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_32xb64-warmup_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py',
3 | '../_base_/schedules/imagenet_bs2048.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb128_coslr-90e_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet21k_bs128.py',
3 | '../_base_/schedules/imagenet_bs1024_coslr.py',
4 | '../_base_/default_runtime.py'
5 | ]
6 |
7 | # model settings
8 | model = dict(head=dict(num_classes=21843))
9 |
10 | # runtime settings
11 | train_cfg = dict(by_epoch=True, max_epochs=90)
12 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb16-mixup_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_cifar_mixup.py',
3 | '../_base_/datasets/cifar10_bs16.py',
4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_cifar.py', '../_base_/datasets/cifar10_bs16.py',
3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb32-coslr_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256_coslr.py',
4 | '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb32-cutmix_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_cutmix.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./resnet50_8xb32_in1k.py']
2 |
3 | # schedule settings
4 | optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb32-fp16_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./resnet50_8xb32_in1k.py']
2 |
3 | # schedule settings
4 | optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=512.)
5 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb32-lbs_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_label_smooth.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb32-mixup_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_mixup.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnet50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py',
4 | '../_base_/default_runtime.py'
5 | ]
6 |
7 | model = dict(
8 | backbone=dict(
9 | out_indices=(2,3))
10 | )
11 |
--------------------------------------------------------------------------------
/configs/resnet/resnetv1c101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1c50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
7 | model = dict(backbone=dict(depth=101))
8 |
--------------------------------------------------------------------------------
/configs/resnet/resnetv1c152_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1c50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
7 | model = dict(backbone=dict(depth=152))
8 |
--------------------------------------------------------------------------------
/configs/resnet/resnetv1c50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1c50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnetv1d101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1d101.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnetv1d152_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1d152.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnet/resnetv1d50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1d50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnext/resnext101-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext101_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnext/resnext101-32x8d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext101_32x8d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnext/resnext152-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext152_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/resnext/resnext50-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext50_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/revvit/revvit-base_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/revvit/revvit-base.py',
3 | '../_base_/datasets/imagenet_bs128_revvit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_revvit.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/revvit/revvit-small_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/revvit/revvit-small.py',
3 | '../_base_/datasets/imagenet_bs128_revvit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_revvit.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/seresnet/seresnet101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnet101.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/seresnet/seresnet50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_140e.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/seresnet/seresnext101-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnext101_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/seresnet/seresnext50-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnext50_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/shufflenet_v1_1x.py',
3 | '../_base_/datasets/imagenet_bs64_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/shufflenet_v2_1x.py',
3 | '../_base_/datasets/imagenet_bs64_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(
9 | backbone=dict(
10 | out_indices=(2,3))
11 | )
--------------------------------------------------------------------------------
/configs/swin_transformer/swin-base_16xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/base_384.py',
3 | '../_base_/datasets/imagenet_bs64_swin_384.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/configs/swin_transformer/swin-base_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/base_224.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/configs/swin_transformer/swin-large_16xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/large_384.py',
3 | '../_base_/datasets/imagenet_bs64_swin_384.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/configs/swin_transformer/swin-large_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/large_224.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/configs/swin_transformer/swin-small_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/small_224.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/base_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(backbone=dict(window_size=[16, 16, 16, 8]))
9 |
--------------------------------------------------------------------------------
/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/base_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/small_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(backbone=dict(window_size=[16, 16, 16, 8]))
9 |
--------------------------------------------------------------------------------
/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/small_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/tiny_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(backbone=dict(window_size=[16, 16, 16, 8]))
9 |
--------------------------------------------------------------------------------
/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/tiny_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/tinyvit/tinyvit-11m-distill_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './tinyvit-11m_8xb256_in1k.py',
3 | ]
4 |
--------------------------------------------------------------------------------
/configs/tinyvit/tinyvit-11m_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
3 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
4 | '../_base_/default_runtime.py',
5 | '../_base_/models/tinyvit/tinyvit-11m.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/tinyvit/tinyvit-21m-distill_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './tinyvit-21m_8xb256_in1k.py',
3 | ]
4 |
--------------------------------------------------------------------------------
/configs/tinyvit/tinyvit-21m_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
3 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
4 | '../_base_/default_runtime.py',
5 | '../_base_/models/tinyvit/tinyvit-21m.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/tinyvit/tinyvit-5m-distill_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './tinyvit-5m_8xb256_in1k.py',
3 | ]
4 |
--------------------------------------------------------------------------------
/configs/tinyvit/tinyvit-5m_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
3 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
4 | '../_base_/default_runtime.py',
5 | '../_base_/models/tinyvit/tinyvit-5m.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/twins/twins-pcpvt-large_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-pcpvt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='large'), head=dict(in_channels=512))
5 |
6 | # dataset settings
7 | train_dataloader = dict(batch_size=64)
8 |
--------------------------------------------------------------------------------
/configs/twins/twins-pcpvt-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-pcpvt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='small'), head=dict(in_channels=512))
5 |
--------------------------------------------------------------------------------
/configs/twins/twins-svt-large_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-svt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='large'), head=dict(in_channels=1024))
5 |
6 | # dataset settings
7 | train_dataloader = dict(batch_size=64)
8 |
--------------------------------------------------------------------------------
/configs/twins/twins-svt-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-svt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='small'), head=dict(in_channels=512))
5 |
--------------------------------------------------------------------------------
/configs/vgg/vgg11_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg11.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/configs/vgg/vgg11bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg11bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vgg/vgg13_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg13.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/configs/vgg/vgg13bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg13bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vgg/vgg16_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg16.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/configs/vgg/vgg16bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg16bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vgg/vgg19_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg19.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/configs/vgg/vgg19bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg19bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vig/pvig-medium_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/pyramid_vig_medium.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vig/pvig-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/pyramid_vig_small.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vig/pvig-tiny_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/pyramid_vig_tiny.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vig/vig-base_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/vig_base.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vig/vig-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/vig_small.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/vig/vig-tiny_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/vig_tiny.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/configs/wrn/wide-resnet101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/wide-resnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
7 | model = dict(backbone=dict(depth=101))
8 |
--------------------------------------------------------------------------------
/configs/wrn/wide-resnet50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/wide-resnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/configs/wrn/wide-resnet50_timm_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/wide-resnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/demo/bird.JPEG:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/demo/bird.JPEG
--------------------------------------------------------------------------------
/demo/cat-dog.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/demo/cat-dog.png
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/demo/demo.JPEG:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/demo/demo.JPEG
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/demo/dog.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/demo/dog.jpg
--------------------------------------------------------------------------------
/docker/serve/config.properties:
--------------------------------------------------------------------------------
1 | inference_address=http://0.0.0.0:8080
2 | management_address=http://0.0.0.0:8081
3 | metrics_address=http://0.0.0.0:8082
4 | model_store=/home/model-server/model-store
5 | load_models=all
6 |
--------------------------------------------------------------------------------
/docker/serve/entrypoint.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 | set -e
3 |
4 | if [[ "$1" = "serve" ]]; then
5 | shift 1
6 | torchserve --start --ts-config /home/model-server/config.properties
7 | else
8 | eval "$@"
9 | fi
10 |
11 | # prevent docker exit
12 | tail -f /dev/null
13 |
--------------------------------------------------------------------------------
/docs/en/_static/image/confusion-matrix.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/docs/en/_static/image/confusion-matrix.png
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/docs/en/_static/image/mmcls-logo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/docs/en/_static/image/mmcls-logo.png
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/docs/en/_static/image/tools/analysis/analyze_log.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/docs/en/_static/image/tools/analysis/analyze_log.jpg
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/docs/en/_static/js/custom.js:
--------------------------------------------------------------------------------
1 | var collapsedSections = ['Useful Tools', 'Advanced Guids', 'Model zoo', 'Notes'];
2 |
3 | $(document).ready(function () {
4 | $('.model-summary').DataTable({
5 | "stateSave": false,
6 | "lengthChange": false,
7 | "pageLength": 20,
8 | "order": []
9 | });
10 | });
11 |
--------------------------------------------------------------------------------
/docs/en/_templates/autosummary/class.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 | .. currentmodule:: {{ module }}
4 |
5 |
6 | {{ name | underline}}
7 |
8 | .. autoclass:: {{ name }}
9 | :members:
10 |
11 | ..
12 | autogenerated from _templates/autosummary/class.rst
13 | note it does not have :inherited-members:
14 |
--------------------------------------------------------------------------------
/docs/en/_templates/callable.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 | .. currentmodule:: {{ module }}
4 |
5 |
6 | {{ name | underline}}
7 |
8 | .. autoclass:: {{ name }}
9 | :members:
10 | :special-members: __call__
11 |
12 | ..
13 | autogenerated from _templates/callable.rst
14 | note it does not have :inherited-members:
15 |
--------------------------------------------------------------------------------
/docs/en/_templates/data_transform.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 | .. currentmodule:: {{ module }}
4 |
5 |
6 | {{ name | underline}}
7 |
8 | .. autoclass:: {{ name }}
9 | :members: transform
10 |
11 | ..
12 | autogenerated from _templates/callable.rst
13 | note it does not have :inherited-members:
14 |
--------------------------------------------------------------------------------
/docs/en/advanced_guides/data_flow.md:
--------------------------------------------------------------------------------
1 | # Data Flow (TODO)
2 |
--------------------------------------------------------------------------------
/docs/en/api/structures.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 |
4 | .. module:: mmcls.structures
5 |
6 | mmcls.structures
7 | ===================================
8 |
9 | This package includes basic data structures for classification tasks.
10 |
11 | ClsDataSample
12 | -------------
13 | .. autoclass:: ClsDataSample
14 |
--------------------------------------------------------------------------------
/docs/en/api/utils.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 |
4 | .. module:: mmcls.utils
5 |
6 | mmcls.utils
7 | ===================================
8 |
9 | This package includes some useful helper functions for developing.
10 |
11 | .. autosummary::
12 | :toctree: generated
13 | :nosignatures:
14 |
15 | collect_env
16 | register_all_modules
17 |
--------------------------------------------------------------------------------
/docs/en/api/visualization.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 |
4 | .. module:: mmcls.visualization
5 |
6 | mmcls.visualization
7 | ===================================
8 |
9 | This package includes visualizer components for classification tasks.
10 |
11 | ClsVisualizer
12 | -------------
13 | .. autoclass:: ClsVisualizer
14 |
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/docs/en/docutils.conf:
--------------------------------------------------------------------------------
1 | [html writers]
2 | table_style: colwidths-auto
3 |
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/docs/zh_CN/_static/image/mmcls-logo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/docs/zh_CN/_static/image/mmcls-logo.png
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/docs/zh_CN/_static/image/tools/analysis/analyze_log.jpg:
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https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/docs/zh_CN/_static/image/tools/analysis/analyze_log.jpg
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/docs/zh_CN/_templates/404.html:
--------------------------------------------------------------------------------
1 | {% extends "layout.html" %}
2 |
3 | {% block body %}
4 |
5 | 未找到页面
6 |
7 | 未找到你要打开的页面。
8 |
9 |
10 | 如果你是从旧版本文档跳转至此，可能是对应的页面被移动了。请从左侧的目录中寻找新版本文档，或者跳转至首页。
11 |
12 |
13 | 如果你找不到希望打开的文档，欢迎在 Issue 中告诉我们！
14 |
15 |
16 | {% endblock %}
17 |
--------------------------------------------------------------------------------
/docs/zh_CN/_templates/autosummary/class.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 | .. currentmodule:: {{ module }}
4 |
5 |
6 | {{ name | underline}}
7 |
8 | .. autoclass:: {{ name }}
9 | :members:
10 |
11 | ..
12 | autogenerated from _templates/autosummary/class.rst
13 | note it does not have :inherited-members:
14 |
--------------------------------------------------------------------------------
/docs/zh_CN/_templates/callable.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 | .. currentmodule:: {{ module }}
4 |
5 |
6 | {{ name | underline}}
7 |
8 | .. autoclass:: {{ name }}
9 | :members:
10 | :special-members: __call__
11 |
12 | ..
13 | autogenerated from _templates/callable.rst
14 | note it does not have :inherited-members:
15 |
--------------------------------------------------------------------------------
/docs/zh_CN/_templates/data_transform.rst:
--------------------------------------------------------------------------------
1 | .. role:: hidden
2 | :class: hidden-section
3 | .. currentmodule:: {{ module }}
4 |
5 |
6 | {{ name | underline}}
7 |
8 | .. autoclass:: {{ name }}
9 | :members: transform
10 |
11 | ..
12 | autogenerated from _templates/callable.rst
13 | note it does not have :inherited-members:
14 |
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/docs/zh_CN/advanced_guides/data_flow.md:
--------------------------------------------------------------------------------
1 | # 数据流（待更新）
2 |
--------------------------------------------------------------------------------
/docs/zh_CN/advanced_guides/evaluation.md:
--------------------------------------------------------------------------------
1 | # 自定义评估指标（待更新）
2 |
3 | 请参见[英文文档](https://mmclassification.readthedocs.io/en/dev-1.x/advanced_guides/evaluation.html)，如果你有兴
4 | 趣参与中文文档的翻译，欢迎在 [讨论区](https://github.com/open-mmlab/mmclassification/discussions/1027)进行报名。
5 |
--------------------------------------------------------------------------------
/docs/zh_CN/advanced_guides/pipeline.md:
--------------------------------------------------------------------------------
1 | # 自定义数据处理流程（待更新）
2 |
3 | 请参见[英文文档](https://mmclassification.readthedocs.io/en/dev-1.x/advanced_guides/pipeline.html)，如果你有兴
4 | 趣参与中文文档的翻译，欢迎在 [讨论区](https://github.com/open-mmlab/mmclassification/discussions/1027)进行报名。
5 |
--------------------------------------------------------------------------------
/docs/zh_CN/docutils.conf:
--------------------------------------------------------------------------------
1 | [html writers]
2 | table_style: colwidths-auto
3 |
--------------------------------------------------------------------------------
/docs/zh_CN/notes/projects.md:
--------------------------------------------------------------------------------
1 | # 基于 MMClassification 的项目列表（待更新）
2 |
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/imgs/ADE-20K.png:
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https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/imgs/ADE-20K.png
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/imgs/IN-1K.png:
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https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/imgs/IN-1K.png
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/imgs/MS-COCO.png:
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https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/imgs/MS-COCO.png
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/imgs/teaser.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/imgs/teaser.png
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/mmcls.egg-info/dependency_links.txt:
--------------------------------------------------------------------------------
1 |
2 |
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/mmcls.egg-info/not-zip-safe:
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1 |
2 |
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/mmcls.egg-info/top_level.txt:
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1 | mmcls
2 |
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/mmcls/.mim/configs/_base_/models/convmixer/convmixer-1024-20.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ConvMixer', arch='1024/20'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/convmixer/convmixer-1536-20.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ConvMixer', arch='1536/20'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1536,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/convmixer/convmixer-768-32.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ConvMixer', arch='768/32', act_cfg=dict(type='ReLU')),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=768,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/densenet/densenet121.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='121'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/densenet/densenet161.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='161'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2208,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/densenet/densenet169.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='169'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1664,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/densenet/densenet201.py:
--------------------------------------------------------------------------------
1 | # Model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='DenseNet', arch='201'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1920,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b0.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b0'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b1.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b1'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b2.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b2'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1408,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b3.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b3'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1536,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b4.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b4'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1792,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b5.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b5'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2048,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b6.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b6'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2304,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b7.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b7'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2560,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_b8.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='b8'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2816,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_l2.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNet', arch='l2'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=5504,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_b0.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b0'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_b1.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b1'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_b2.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b2'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1408,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_b3.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='b3'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1536,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_l.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='l'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_m.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='m'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_s.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='s'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/efficientnet_v2/efficientnetv2_xl.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='EfficientNetV2', arch='xl'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/inception_v3.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='InceptionV3', num_classes=1000, aux_logits=False),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5)),
10 | )
11 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/mobilenet_v1_1x.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileNetV1'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/mobilenet_v2_1x.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileNetV2', widen_factor=1.0),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1280,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/mobilevit/mobilevit_s.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileViT', arch='small'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=640,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/mobilevit/mobilevit_xs.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileViT', arch='x_small'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=384,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/mobilevit/mobilevit_xxs.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='MobileViT', arch='xx_small'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=320,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_1.6gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_1.6gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=912,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_12gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_12gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=2240,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_3.2gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_3.2gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1008,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_4.0gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_4.0gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1360,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_400mf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_400mf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=384,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_6.4gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_6.4gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1624,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_8.0gf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_8.0gf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1920,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/regnet/regnetx_800mf.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='RegNet', arch='regnetx_800mf'),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=672,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/replknet-XL_in1k.py:
--------------------------------------------------------------------------------
1 | model = dict(
2 | type='ImageClassifier',
3 | backbone=dict(
4 | type='RepLKNet',
5 | arch='XL',
6 | out_indices=(3, ),
7 | ),
8 | neck=dict(type='GlobalAveragePooling'),
9 | head=dict(
10 | type='LinearClsHead',
11 | num_classes=1000,
12 | in_channels=2048,
13 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
14 | topk=(1, 5),
15 | ))
16 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/repvgg-A0_in1k.py:
--------------------------------------------------------------------------------
1 | model = dict(
2 | type='ImageClassifier',
3 | backbone=dict(
4 | type='RepVGG',
5 | arch='A0',
6 | out_indices=(3, ),
7 | ),
8 | neck=dict(type='GlobalAveragePooling'),
9 | head=dict(
10 | type='LinearClsHead',
11 | num_classes=1000,
12 | in_channels=1280,
13 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
14 | topk=(1, 5),
15 | ))
16 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/shufflenet_v1_1x.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ShuffleNetV1', groups=3),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=960,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/shufflenet_v2_1x.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='ShuffleNetV2', widen_factor=1.0),
5 | neck=dict(type='GlobalAveragePooling'),
6 | head=dict(
7 | type='LinearClsHead',
8 | num_classes=1000,
9 | in_channels=1024,
10 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
11 | topk=(1, 5),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg11.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=11, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg11bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=11, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg13.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=13, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg13bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=13, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg16.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=16, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg16bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=16, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg19.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(type='VGG', depth=19, num_classes=1000),
5 | neck=None,
6 | head=dict(
7 | type='ClsHead',
8 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
9 | topk=(1, 5),
10 | ))
11 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/_base_/models/vgg19bn.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | model = dict(
3 | type='ImageClassifier',
4 | backbone=dict(
5 | type='VGG', depth=19, norm_cfg=dict(type='BN'), num_classes=1000),
6 | neck=None,
7 | head=dict(
8 | type='ClsHead',
9 | loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
10 | topk=(1, 5),
11 | ))
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/clip/vit-base-p16_pt-64xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_clip_384.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/clip/vit-base-p16_pt-64xb64_in1k-448px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_clip_448.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/clip/vit-base-p16_pt-64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_clip_224.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/clip/vit-base-p32_pt-64xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p32.py',
3 | '../_base_/datasets/imagenet_bs64_clip_384.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/clip/vit-base-p32_pt-64xb64_in1k-448px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p32.py',
3 | '../_base_/datasets/imagenet_bs64_clip_448.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/clip/vit-base-p32_pt-64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vit-base-p32.py',
3 | '../_base_/datasets/imagenet_bs64_clip_224.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model setting/mnt/lustre/lirongjie/tmp/clip_ckpt/trans_ckpt
9 | model = dict(backbone=dict(pre_norm=True, ), )
10 |
11 | # schedule setting
12 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/conformer/conformer-base-p16_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/base-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/conformer/conformer-small-p16_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/small-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/conformer/conformer-small-p32_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/small-p32.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/conformer/conformer-tiny-p16_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/conformer/tiny-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_conformer.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | train_dataloader = dict(batch_size=128)
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/davit/davit-base_4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/davit/davit-base.py',
3 | '../_base_/datasets/imagenet_bs256_davit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # data settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/davit/davit-small_4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/davit/davit-small.py',
3 | '../_base_/datasets/imagenet_bs256_davit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # data settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/davit/davit-tiny_4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/davit/davit-tiny.py',
3 | '../_base_/datasets/imagenet_bs256_davit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # data settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-base_ft-16xb32_in1k-384px.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer'),
6 | head=dict(type='DeiTClsHead'),
7 | # Change to the path of the pretrained model
8 | # init_cfg=dict(type='Pretrained', checkpoint=''),
9 | )
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/deit/deit-base-distilled_pt-16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-base'),
6 | head=dict(type='DeiTClsHead', in_channels=768),
7 | )
8 |
9 | # dataset settings
10 | train_dataloader = dict(batch_size=64)
11 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/deit/deit-small-distilled_pt-4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-small'),
6 | head=dict(type='DeiTClsHead', in_channels=384),
7 | )
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-tiny'),
6 | head=dict(type='DeiTClsHead', in_channels=192),
7 | )
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/deit/deit-tiny_pt-4xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './deit-small_pt-4xb256_in1k.py'
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='VisionTransformer', arch='deit-tiny'),
6 | head=dict(type='VisionTransformerClsHead', in_channels=192),
7 | )
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/efficientformer/efficientformer-l1_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/efficientformer-l1.py',
3 | '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/efficientformer/efficientformer-l3_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './efficientformer-l1_8xb128_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='l3'), head=dict(in_channels=512))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/efficientformer/efficientformer-l7_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './efficientformer-l1_8xb128_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='l7'), head=dict(in_channels=768))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/efficientnet_v2/efficientnetv2-l_8xb32_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./efficientnetv2-s_8xb32_in21k.py']
2 |
3 | # model setting
4 | model = dict(backbone=dict(arch='l'), )
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/efficientnet_v2/efficientnetv2-m_8xb32_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./efficientnetv2-s_8xb32_in21k.py']
2 |
3 | # model setting
4 | model = dict(backbone=dict(arch='m'), )
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/efficientnet_v2/efficientnetv2-xl_8xb32_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./efficientnetv2-s_8xb32_in21k.py']
2 |
3 | # model setting
4 | model = dict(backbone=dict(arch='xl'), )
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/eva/eva-g-p14_8xb16_in1k-336px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-g.py',
3 | '../_base_/datasets/imagenet_bs16_eva_336.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=336))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/eva/eva-g-p14_8xb16_in1k-560px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-g.py',
3 | '../_base_/datasets/imagenet_bs16_eva_560.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=560))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/eva/eva-l-p14_8xb16_in1k-196px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-l.py',
3 | '../_base_/datasets/imagenet_bs16_eva_196.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=196))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/eva/eva-l-p14_8xb16_in1k-336px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/eva/eva-l.py',
3 | '../_base_/datasets/imagenet_bs16_eva_336.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(img_size=336))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/hrnet/hrnet-w18_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w18.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/hrnet/hrnet-w30_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w30.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/hrnet/hrnet-w32_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w32.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/hrnet/hrnet-w40_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w40.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/hrnet/hrnet-w44_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w44.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/hrnet/hrnet-w48_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w48.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/hrnet/hrnet-w64_4xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/hrnet/hrnet-w64.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # NOTE: `auto_scale_lr` is for automatically scaling LR
9 | # based on the actual training batch size.
10 | # base_batch_size = (4 GPUs) x (32 samples per GPU)
11 | auto_scale_lr = dict(base_batch_size=128)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/deploy/levit-128_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-128_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/deploy/levit-128s_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-128s_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/deploy/levit-192_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-192_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/deploy/levit-256_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-256_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/deploy/levit-384_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../levit-384_8xb256_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True), head=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/levit-128_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(arch='128'), head=dict(in_channels=384))
10 |
11 | # dataset settings
12 | train_dataloader = dict(batch_size=256)
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/levit-128s_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(arch='128s'), head=dict(in_channels=384))
10 |
11 | # dataset settings
12 | train_dataloader = dict(batch_size=256)
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/levit-192_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # model settings
9 | model = dict(backbone=dict(arch='192'), head=dict(in_channels=384))
10 |
11 | # dataset settings
12 | train_dataloader = dict(batch_size=256)
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/levit/levit-256_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/levit-256-p16.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs2048_adamw_levit.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # dataset settings
9 | train_dataloader = dict(batch_size=256)
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mixmim/mixmim-base_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mixmim/mixmim_base.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mlp_mixer_base_patch16.py',
3 | '../_base_/datasets/imagenet_bs64_mixer_224.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mlp_mixer_large_patch16.py',
3 | '../_base_/datasets/imagenet_bs64_mixer_224.py',
4 | '../_base_/schedules/imagenet_bs4096_AdamW.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mobilenet_v1/mobilenet_v1.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mobilenet_v1_1x.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/mobilenet_v2_1x.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_epochstep.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(
9 | backbone=dict(
10 | out_indices=(4,5,6,7))
11 | )
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mobileone/deploy/mobileone-s0_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s0_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mobileone/deploy/mobileone-s1_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s1_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mobileone/deploy/mobileone-s2_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s2_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mobileone/deploy/mobileone-s3_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s3_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/mobileone/deploy/mobileone-s4_deploy_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['../mobileone-s4_8xb32_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/regnet/regnetx-1.6gf_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./regnetx-400mf_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='RegNet', arch='regnetx_1.6gf'),
6 | head=dict(in_channels=912, ))
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/regnet/regnetx-800mf_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./regnetx-400mf_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(
5 | backbone=dict(type='RegNet', arch='regnetx_800mf'),
6 | head=dict(in_channels=672, ))
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/deploy/replknet-31B-deploy_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-31B_32xb64_in1k-384px.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/deploy/replknet-31B-deploy_32xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-31B_32xb64_in1k.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/deploy/replknet-31L-deploy_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-31L_32xb64_in1k-384px.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/deploy/replknet-XL-deploy_32xb64_in1k-320px.py:
--------------------------------------------------------------------------------
1 | _base_ = '../replknet-XL_32xb64_in1k-320px.py'
2 |
3 | model = dict(backbone=dict(small_kernel_merged=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/replknet-31B_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-31B_in1k.py',
3 | '../_base_/datasets/imagenet_bs16_pil_bicubic_384.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/replknet-31B_32xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-31B_in1k.py',
3 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/replknet-31L_32xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-31L_in1k.py',
3 | '../_base_/datasets/imagenet_bs16_pil_bicubic_384.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/replknet/replknet-XL_32xb64_in1k-320px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/replknet-XL_in1k.py',
3 | '../_base_/datasets/imagenet_bs8_pil_bicubic_320.py',
4 | '../_base_/schedules/imagenet_bs256_coslr.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | param_scheduler = dict(
10 | type='CosineAnnealingLR', T_max=300, by_epoch=True, begin=0, end=300)
11 |
12 | train_cfg = dict(by_epoch=True, max_epochs=300)
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./repmlp-base_8xb64_in1k.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repmlp/repmlp-base_deploy_8xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./repmlp-base_8xb64_in1k-256px.py']
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-A0_deploy_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(deploy=True))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-A1_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='A1'))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-A2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='A2'), head=dict(in_channels=1408))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-B0_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B0'), head=dict(in_channels=1280))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-B1_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B1'), head=dict(in_channels=2048))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-B1g2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B1g2'), head=dict(in_channels=2048))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-B1g4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B1g4'), head=dict(in_channels=2048))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-B2_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-A0_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B2'), head=dict(in_channels=2560))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-B2g4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-B3_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B2g4'), head=dict(in_channels=2560))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/repvgg/repvgg-B3g4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = './repvgg-B3_8xb32_in1k.py'
2 |
3 | model = dict(backbone=dict(arch='B3g4'))
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/res2net/res2net101-w26-s4_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/res2net101-w26-s4.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/res2net/res2net50-w14-s8_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/res2net50-w14-s8.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/res2net/res2net50-w26-s8_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/res2net50-w26-s8.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet101_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet101_cifar.py',
3 | '../_base_/datasets/cifar10_bs16.py',
4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet101.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
6 | model = dict(
7 | backbone=dict(
8 | out_indices=(2,3))
9 | )
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet152_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet152_cifar.py',
3 | '../_base_/datasets/cifar10_bs16.py',
4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet152_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet152.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet18_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet18_cifar.py', '../_base_/datasets/cifar10_bs16.py',
3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet18_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet18.py', '../_base_/datasets/imagenet_bs32_swin_224.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 | model = dict(
6 | backbone=dict(
7 | out_indices=(2,3))
8 | )
9 | # imagenet_bs64_swin_224
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet18_8xb32_in1k_former_distill.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet18.py', '../_base_/datasets/imagenet_bs32_swin_224.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 | model = dict(
6 | backbone=dict(
7 | out_indices=(2,3))
8 | )
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet34_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet34_cifar.py', '../_base_/datasets/cifar10_bs16.py',
3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet34_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet34.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_32xb64-warmup-coslr_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py',
3 | '../_base_/schedules/imagenet_bs2048_coslr.py',
4 | '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_32xb64-warmup-lbs_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./resnet50_32xb64-warmup_in1k.py']
2 | model = dict(
3 | head=dict(
4 | type='LinearClsHead',
5 | num_classes=1000,
6 | in_channels=2048,
7 | loss=dict(
8 | type='LabelSmoothLoss',
9 | loss_weight=1.0,
10 | label_smooth_val=0.1,
11 | num_classes=1000),
12 | ))
13 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_32xb64-warmup_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py',
3 | '../_base_/schedules/imagenet_bs2048.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb128_coslr-90e_in21k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet21k_bs128.py',
3 | '../_base_/schedules/imagenet_bs1024_coslr.py',
4 | '../_base_/default_runtime.py'
5 | ]
6 |
7 | # model settings
8 | model = dict(head=dict(num_classes=21843))
9 |
10 | # runtime settings
11 | train_cfg = dict(by_epoch=True, max_epochs=90)
12 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb16-mixup_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_cifar_mixup.py',
3 | '../_base_/datasets/cifar10_bs16.py',
4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb16_cifar10.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_cifar.py', '../_base_/datasets/cifar10_bs16.py',
3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
4 | ]
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb32-coslr_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256_coslr.py',
4 | '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb32-cutmix_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_cutmix.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./resnet50_8xb32_in1k.py']
2 |
3 | # schedule settings
4 | optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb32-fp16_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./resnet50_8xb32_in1k.py']
2 |
3 | # schedule settings
4 | optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=512.)
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb32-lbs_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_label_smooth.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb32-mixup_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50_mixup.py',
3 | '../_base_/datasets/imagenet_bs32.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnet50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
4 | ]
5 |
6 | model = dict(
7 | backbone=dict(
8 | out_indices=(2,3))
9 | )
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnetv1c101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1c50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
7 | model = dict(backbone=dict(depth=101))
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnetv1c152_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1c50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
7 | model = dict(backbone=dict(depth=152))
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnetv1c50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1c50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnetv1d101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1d101.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnetv1d152_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1d152.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnet/resnetv1d50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnetv1d50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnext/resnext101-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext101_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnext/resnext101-32x8d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext101_32x8d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnext/resnext152-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext152_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/resnext/resnext50-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/resnext50_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/revvit/revvit-base_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/revvit/revvit-base.py',
3 | '../_base_/datasets/imagenet_bs128_revvit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_revvit.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/revvit/revvit-small_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/revvit/revvit-small.py',
3 | '../_base_/datasets/imagenet_bs128_revvit_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_revvit.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/seresnet/seresnet101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnet101.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/seresnet/seresnet50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256_140e.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/seresnet/seresnext101-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnext101_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/seresnet/seresnext50-32x4d_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/seresnext50_32x4d.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/shufflenet_v1_1x.py',
3 | '../_base_/datasets/imagenet_bs64_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/shufflenet_v2_1x.py',
3 | '../_base_/datasets/imagenet_bs64_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(
9 | backbone=dict(
10 | out_indices=(2,3))
11 | )
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer/swin-base_16xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/base_384.py',
3 | '../_base_/datasets/imagenet_bs64_swin_384.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer/swin-base_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/base_224.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer/swin-large_16xb64_in1k-384px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/large_384.py',
3 | '../_base_/datasets/imagenet_bs64_swin_384.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer/swin-large_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/large_224.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer/swin-small_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/small_224.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer/swin-tiny_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer/tiny_224.py',
3 | '../_base_/datasets/imagenet_bs64_swin_224.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(
9 | backbone=dict(
10 | out_indices=(0,1,2,3))
11 | )
12 |
13 | # schedule settings
14 | optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
15 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/base_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(backbone=dict(window_size=[16, 16, 16, 8]))
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/base_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/small_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(backbone=dict(window_size=[16, 16, 16, 8]))
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/small_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/tiny_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
8 | model = dict(backbone=dict(window_size=[16, 16, 16, 8]))
9 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/swin_transformer_v2/tiny_256.py',
3 | '../_base_/datasets/imagenet_bs64_swin_256.py',
4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
5 | '../_base_/default_runtime.py'
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/tinyvit/tinyvit-11m-distill_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './tinyvit-11m_8xb256_in1k.py',
3 | ]
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/tinyvit/tinyvit-11m_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
3 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
4 | '../_base_/default_runtime.py',
5 | '../_base_/models/tinyvit/tinyvit-11m.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/tinyvit/tinyvit-21m-distill_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './tinyvit-21m_8xb256_in1k.py',
3 | ]
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/tinyvit/tinyvit-21m_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
3 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
4 | '../_base_/default_runtime.py',
5 | '../_base_/models/tinyvit/tinyvit-21m.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/tinyvit/tinyvit-5m-distill_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './tinyvit-5m_8xb256_in1k.py',
3 | ]
4 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/tinyvit/tinyvit-5m_8xb256_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
3 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
4 | '../_base_/default_runtime.py',
5 | '../_base_/models/tinyvit/tinyvit-5m.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/twins/twins-pcpvt-large_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-pcpvt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='large'), head=dict(in_channels=512))
5 |
6 | # dataset settings
7 | train_dataloader = dict(batch_size=64)
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/twins/twins-pcpvt-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-pcpvt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='small'), head=dict(in_channels=512))
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/twins/twins-svt-large_16xb64_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-svt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='large'), head=dict(in_channels=1024))
5 |
6 | # dataset settings
7 | train_dataloader = dict(batch_size=64)
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/twins/twins-svt-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = ['twins-svt-base_8xb128_in1k.py']
2 |
3 | # model settings
4 | model = dict(backbone=dict(arch='small'), head=dict(in_channels=512))
5 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg11_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg11.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg11bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg11bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg13_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg13.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg13bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg13bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg16_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg16.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg16bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg16bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg19_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg19.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
8 | # schedule settings
9 | optim_wrapper = dict(optimizer=dict(lr=0.01))
10 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vgg/vgg19bn_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vgg19bn.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vig/pvig-medium_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/pyramid_vig_medium.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vig/pvig-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/pyramid_vig_small.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
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/mmcls/.mim/configs/vig/pvig-tiny_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/pyramid_vig_tiny.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vig/vig-base_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/vig_base.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vig/vig-small_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/vig_small.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/vig/vig-tiny_8xb128_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/vig/vig_tiny.py',
3 | '../_base_/datasets/imagenet_bs128_vig_224.py',
4 | '../_base_/schedules/imagenet_bs256.py',
5 | '../_base_/default_runtime.py',
6 | ]
7 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/wrn/wide-resnet101_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/wide-resnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
7 | model = dict(backbone=dict(depth=101))
8 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/wrn/wide-resnet50_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/wide-resnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_resize.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/.mim/configs/wrn/wide-resnet50_timm_8xb32_in1k.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/wide-resnet50.py',
3 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py',
4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
5 | ]
6 |
--------------------------------------------------------------------------------
/mmcls/apis/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .inference import ImageClassificationInferencer, inference_model
3 | from .model import ModelHub, get_model, init_model, list_models
4 |
5 | __all__ = [
6 | 'init_model', 'inference_model', 'list_models', 'get_model', 'ModelHub',
7 | 'ImageClassificationInferencer'
8 | ]
9 |
--------------------------------------------------------------------------------
/mmcls/datasets/samplers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .repeat_aug import RepeatAugSampler
3 |
4 | __all__ = ('RepeatAugSampler', )
5 |
--------------------------------------------------------------------------------
/mmcls/engine/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .hooks import * # noqa: F401, F403
3 | from .optimizers import * # noqa: F401, F403
4 |
--------------------------------------------------------------------------------
/mmcls/engine/optimizers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .adan_t import Adan
3 | from .lamb import Lamb
4 |
5 | __all__ = ['Lamb', 'Adan']
6 |
--------------------------------------------------------------------------------
/mmcls/evaluation/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .metrics import * # noqa: F401,F403
3 |
--------------------------------------------------------------------------------
/mmcls/models/classifiers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .base import BaseClassifier
3 | from .hugging_face import HuggingFaceClassifier
4 | from .image import ImageClassifier
5 | from .timm import TimmClassifier
6 | from .distiller import ClassificationDistiller
7 |
8 | __all__ = [
9 | 'BaseClassifier', 'ImageClassifier', 'TimmClassifier',
10 | 'HuggingFaceClassifier', 'ClassificationDistiller',
11 | ]
12 |
--------------------------------------------------------------------------------
/mmcls/models/dis_losses/__init__.py:
--------------------------------------------------------------------------------
1 | from .kd import KDLoss
2 | from .fd import FDLoss
3 | from .fmd import FreqMaskingDistillLoss
4 | from .fmdv2 import FreqMaskingDistillLossv2
5 | from .fmd_convnext import FreqMaskingDistillLossConvNext
6 |
7 |
8 | __all__ = [
9 | 'KDLoss', 'FDLoss' , 'FreqMaskingDistillLoss', 'FreqMaskingDistillLossv2', 'FreqMaskingDistillLossConvNext'
10 | ]
11 |
--------------------------------------------------------------------------------
/mmcls/models/necks/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .gap import GlobalAveragePooling
3 | from .gem import GeneralizedMeanPooling
4 | from .hr_fuse import HRFuseScales
5 | from .reduction import LinearReduction
6 |
7 | __all__ = [
8 | 'GlobalAveragePooling', 'GeneralizedMeanPooling', 'HRFuseScales',
9 | 'LinearReduction'
10 | ]
11 |
--------------------------------------------------------------------------------
/mmcls/models/retrievers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .base import BaseRetriever
3 | from .image2image import ImageToImageRetriever
4 |
5 | __all__ = ['BaseRetriever', 'ImageToImageRetriever']
6 |
--------------------------------------------------------------------------------
/mmcls/models/tta/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .score_tta import AverageClsScoreTTA
3 |
4 | __all__ = ['AverageClsScoreTTA']
5 |
--------------------------------------------------------------------------------
/mmcls/models/utils/batch_augments/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .cutmix import CutMix
3 | from .mixup import Mixup
4 | from .resizemix import ResizeMix
5 | from .wrapper import RandomBatchAugment
6 |
7 | __all__ = ('RandomBatchAugment', 'CutMix', 'Mixup', 'ResizeMix')
8 |
--------------------------------------------------------------------------------
/mmcls/structures/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .cls_data_sample import ClsDataSample
3 | from .multi_task_data_sample import MultiTaskDataSample
4 | from .utils import (batch_label_to_onehot, cat_batch_labels,
5 | stack_batch_scores, tensor_split)
6 |
7 | __all__ = [
8 | 'ClsDataSample', 'batch_label_to_onehot', 'cat_batch_labels',
9 | 'stack_batch_scores', 'tensor_split', 'MultiTaskDataSample'
10 | ]
11 |
--------------------------------------------------------------------------------
/mmcls/structures/multi_task_data_sample.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
3 | from mmengine.structures import BaseDataElement
4 |
5 |
6 | class MultiTaskDataSample(BaseDataElement):
7 |
8 | @property
9 | def tasks(self):
10 | return self._data_fields
11 |
--------------------------------------------------------------------------------
/mmcls/utils/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .analyze import load_json_log
3 | from .collect_env import collect_env
4 | from .progress import track_on_main_process
5 | from .setup_env import register_all_modules
6 |
7 | __all__ = [
8 | 'collect_env', 'register_all_modules', 'track_on_main_process',
9 | 'load_json_log'
10 | ]
11 |
--------------------------------------------------------------------------------
/mmcls/utils/collect_env.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from mmengine.utils import get_git_hash
3 | from mmengine.utils.dl_utils import collect_env as collect_base_env
4 |
5 | import mmcls
6 |
7 |
8 | def collect_env():
9 | """Collect the information of the running environments."""
10 | env_info = collect_base_env()
11 | env_info['MMClassification'] = mmcls.__version__ + '+' + get_git_hash()[:7]
12 | return env_info
13 |
--------------------------------------------------------------------------------
/mmcls/utils/progress.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | import mmengine.dist as dist
3 | import rich.progress as progress
4 |
5 |
6 | def track_on_main_process(sequence, *args, **kwargs):
7 | if not dist.is_main_process():
8 | yield from sequence
9 | else:
10 | yield from progress.track(sequence, *args, **kwargs)
11 |
--------------------------------------------------------------------------------
/mmcls/visualization/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .cls_visualizer import ClsVisualizer
3 |
4 | __all__ = ['ClsVisualizer']
5 |
--------------------------------------------------------------------------------
/projects/example_project/models/__init__.py:
--------------------------------------------------------------------------------
1 | from .example_net import ExampleNet
2 |
3 | __all__ = ['ExampleNet']
4 |
--------------------------------------------------------------------------------
/projects/ood_eval/config/vit_ood-eval_toy-example.py:
--------------------------------------------------------------------------------
1 | _base_ = 'mmcls::resnet/resnetv1c50_8xb32_in1k.py' # can be your own config
2 |
3 | # You can replace imagenet-r with imagenet-a or imagenet-s
4 | test_dataloader = dict(dataset=dict(data_root='data/imagenet-r'))
5 | test_evaluator = dict(ann_file='data/imagenet-r/meta/val.txt')
6 |
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/projects/ood_eval/config/vit_ood-eval_toy-example_imagenet-c.py:
--------------------------------------------------------------------------------
1 | _base_ = 'mmcls::resnet/resnetv1c50_8xb32_in1k.py' # can be your own config
2 |
3 | test_dataloader = dict(dataset=dict(data_root='data/imagenet-c'))
4 | test_evaluator = dict(type='CorruptionError')
5 |
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/requirements.txt:
--------------------------------------------------------------------------------
1 | -r requirements/optional.txt
2 | -r requirements/runtime.txt
3 | -r requirements/tests.txt
4 | -r requirements/mminstall.txt
--------------------------------------------------------------------------------
/requirements/docs.txt:
--------------------------------------------------------------------------------
1 | docutils==0.17.1
2 | modelindex
3 | myst-parser
4 | git+https://github.com/mzr1996/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
5 | sphinx==4.5.0
6 | sphinx-copybutton
7 | sphinx-notfound-page
8 | sphinx-tabs
9 | tabulate
10 |
--------------------------------------------------------------------------------
/requirements/mminstall.txt:
--------------------------------------------------------------------------------
1 | mmcv>=2.0.0rc4,<=2.1.0
2 | mmengine>=0.7.1,<1.0.0
3 |
--------------------------------------------------------------------------------
/requirements/optional.txt:
--------------------------------------------------------------------------------
1 | albumentations>=0.3.2 --no-binary qudida,albumentations
2 | colorama
3 | requests
4 |
--------------------------------------------------------------------------------
/requirements/readthedocs.txt:
--------------------------------------------------------------------------------
1 | mmcv>=2.0.0rc1
2 | mmengine
--------------------------------------------------------------------------------
/requirements/runtime.txt:
--------------------------------------------------------------------------------
1 | matplotlib
2 | modelindex
3 | numpy
4 | packaging
5 | rich
6 |
--------------------------------------------------------------------------------
/requirements/tests.txt:
--------------------------------------------------------------------------------
1 | codecov
2 | flake8
3 | interrogate
4 | isort==4.3.21
5 | mmdet>=3.0.0rc0
6 | pytest
7 | scikit-learn
8 | xdoctest >= 0.10.0
9 | yapf
10 |
--------------------------------------------------------------------------------
/resources/mmcls-logo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/resources/mmcls-logo.png
--------------------------------------------------------------------------------
/run.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 |
3 | CONFIG=$1
4 | GPU=$2
5 | MLP_ROLE_INDEX=$3
6 |
7 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
8 | python -m torch.distributed.launch \
9 | --nproc_per_node=$GPU \
10 | --master_addr=$MLP_WORKER_0_HOST \
11 | --node_rank=$MLP_ROLE_INDEX \
12 | --master_port=$MLP_WORKER_0_PORT \
13 | --nnodes=4\
14 | tools/train.py \
15 | $CONFIG \
16 | --launcher pytorch ${@:3}\
17 |
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/tests/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/data/color.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/tests/data/color.jpg
--------------------------------------------------------------------------------
/tests/data/dataset/a/1.JPG:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/tests/data/dataset/a/1.JPG
--------------------------------------------------------------------------------
/tests/data/dataset/ann.txt:
--------------------------------------------------------------------------------
1 | a/1.JPG 0
2 | b/2.jpeg 1
3 | b/subb/3.jpg 1
4 |
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/tests/data/dataset/b/2.jpeg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/tests/data/dataset/b/2.jpeg
--------------------------------------------------------------------------------
/tests/data/dataset/b/subb/3.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/tests/data/dataset/b/subb/3.jpg
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/tests/data/dataset/classes.txt:
--------------------------------------------------------------------------------
1 | bus
2 | car
3 |
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/tests/data/gray.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/deep-optimization/ScaleKD/478a7dbb19caa7dd6ec225cd824b0e0a74a6cdc4/tests/data/gray.jpg
--------------------------------------------------------------------------------
/tests/data/meta.yml:
--------------------------------------------------------------------------------
1 | Models:
2 | - Name: test_model
3 | Metadata:
4 | FLOPs: 319000000
5 | Parameters: 3500000
6 | Results:
7 | - Dataset: ImageNet-1k
8 | Metrics:
9 | Top 1 Accuracy: 71.86
10 | Top 5 Accuracy: 90.42
11 | Task: Image Classification
12 | Weights: test_weight.pth
13 | Config: test_config.py
14 |
--------------------------------------------------------------------------------
/tests/test_models/test_backbones/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/test_models/test_utils/test_position_encoding.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | import torch
3 |
4 | from mmcls.models.utils import ConditionalPositionEncoding
5 |
6 |
7 | def test_conditional_position_encoding_module():
8 | CPE = ConditionalPositionEncoding(in_channels=32, embed_dims=32, stride=2)
9 | outs = CPE(torch.randn(1, 3136, 32), (56, 56))
10 | assert outs.shape == torch.Size([1, 784, 32])
11 |
--------------------------------------------------------------------------------