first commit

configs/resnet/resnet50_32xb64-warmup_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py',
+    '../_base_/schedules/imagenet_bs2048.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb128_coslr-90e_in21k.py

+_base_ = [
+    '../_base_/models/resnet50.py', '../_base_/datasets/imagenet21k_bs128.py',
+    '../_base_/schedules/imagenet_bs1024_coslr.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(head=dict(num_classes=21843))
+
+# runtime settings
+train_cfg = dict(by_epoch=True, max_epochs=90)

configs/resnet/resnet50_8xb16-mixup_cifar10.py

+_base_ = [
+    '../_base_/models/resnet50_cifar_mixup.py',
+    '../_base_/datasets/cifar10_bs16.py',
+    '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb16_cifar10.py

+_base_ = [
+    '../_base_/models/resnet50_cifar.py', '../_base_/datasets/cifar10_bs16.py',
+    '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb16_cifar100.py

+_base_ = [
+    '../_base_/models/resnet50_cifar.py',
+    '../_base_/datasets/cifar100_bs16.py',
+    '../_base_/schedules/cifar10_bs128.py',
+    '../_base_/default_runtime.py',
+]
+
+# model settings
+model = dict(head=dict(num_classes=100))
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(weight_decay=0.0005))
+
+param_scheduler = dict(
+    type='MultiStepLR',
+    by_epoch=True,
+    milestones=[60, 120, 160],
+    gamma=0.2,
+)

configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/imagenet_bs256_rsb_a12.py',
+    '../_base_/schedules/imagenet_bs2048_rsb.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        drop_path_rate=0.05,
+    ),
+    head=dict(
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            mode='original',
+            use_sigmoid=True,
+        )),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.2),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# dataset settings
+train_dataloader = dict(sampler=dict(type='RepeatAugSampler', shuffle=True))
+
+# schedule settings
+optim_wrapper = dict(
+    optimizer=dict(weight_decay=0.01),
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.),
+)
+
+param_scheduler = [
+    # warm up learning rate scheduler
+    dict(
+        type='LinearLR',
+        start_factor=0.0001,
+        by_epoch=True,
+        begin=0,
+        end=5,
+        # update by iter
+        convert_to_iter_based=True),
+    # main learning rate scheduler
+    dict(
+        type='CosineAnnealingLR',
+        T_max=595,
+        eta_min=1.0e-6,
+        by_epoch=True,
+        begin=5,
+        end=600)
+]
+
+train_cfg = dict(by_epoch=True, max_epochs=600)

configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/imagenet_bs256_rsb_a12.py',
+    '../_base_/schedules/imagenet_bs2048_rsb.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        drop_path_rate=0.05,
+    ),
+    head=dict(loss=dict(use_sigmoid=True)),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.1),
+        dict(type='CutMix', alpha=1.0)
+    ]))
+
+# dataset settings
+train_dataloader = dict(sampler=dict(type='RepeatAugSampler', shuffle=True))
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
+
+param_scheduler = [
+    # warm up learning rate scheduler
+    dict(
+        type='LinearLR',
+        start_factor=0.0001,
+        by_epoch=True,
+        begin=0,
+        end=5,
+        # update by iter
+        convert_to_iter_based=True),
+    # main learning rate scheduler
+    dict(
+        type='CosineAnnealingLR',
+        T_max=295,
+        eta_min=1.0e-6,
+        by_epoch=True,
+        begin=5,
+        end=300)
+]
+train_cfg = dict(by_epoch=True, max_epochs=300)

configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/imagenet_bs256_rsb_a3.py',
+    '../_base_/schedules/imagenet_bs2048_rsb.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    backbone=dict(norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    head=dict(loss=dict(use_sigmoid=True)),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.1),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# schedule settings
+optim_wrapper = dict(
+    optimizer=dict(lr=0.008),
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.),
+)

configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py

+_base_ = 'resnet50_8xb32-coslr_in1k.py'
+
+# Precise BN hook will update the bn stats, so this hook should be executed
+# before CheckpointHook(priority of 'VERY_LOW') and
+# EMAHook(priority of 'NORMAL') So set the priority of PreciseBNHook to
+# 'ABOVENORMAL' here.
+custom_hooks = [
+    dict(
+        type='PreciseBNHook',
+        num_samples=8192,
+        interval=1,
+        priority='ABOVE_NORMAL')
+]

configs/resnet/resnet50_8xb32-coslr_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256_coslr.py',
+    '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32-cutmix_in1k.py

+_base_ = [
+    '../_base_/models/resnet50_cutmix.py',
+    '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py

+_base_ = ['./resnet50_8xb32_in1k.py']
+
+# schedule settings
+optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')

configs/resnet/resnet50_8xb32-fp16_in1k.py

+_base_ = ['./resnet50_8xb32_in1k.py']
+
+# schedule settings
+optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=512.)

configs/resnet/resnet50_8xb32-lbs_in1k.py

+_base_ = [
+    '../_base_/models/resnet50_label_smooth.py',
+    '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32-mixup_in1k.py

+_base_ = [
+    '../_base_/models/resnet50_mixup.py',
+    '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb8_cub.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/cub_bs8_448.py',
+    '../_base_/schedules/cub_bs64.py',
+    '../_base_/default_runtime.py',
+]
+
+# model settings
+# use pre-train weight converted from https://github.com/Alibaba-MIIL/ImageNet21K # noqa
+pretrained = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth'  # noqa
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', checkpoint=pretrained, prefix='backbone')),
+    head=dict(num_classes=200, ))
+
+# runtime settings
+default_hooks = dict(logger=dict(type='LoggerHook', interval=20))

configs/resnet/resnetv1c101_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(depth=101))

configs/resnet/resnetv1c152_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(depth=152))

configs/resnet/resnetv1c50_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]