Initial add code.

configs/recognition/slowfast/slowfast_multigrid_r50_8x8x1_358e_kinetics400_rgb.py

+model = dict(
+    type='Recognizer3D',
+    backbone=dict(
+        type='ResNet3dSlowFast',
+        pretrained=None,
+        resample_rate=4,  # tau
+        speed_ratio=4,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(
+            type='resnet3d',
+            depth=50,
+            pretrained=None,
+            lateral=True,
+            lateral_norm=True,
+            fusion_kernel=7,
+            conv1_kernel=(1, 7, 7),
+            dilations=(1, 1, 1, 1),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            inflate=(0, 0, 1, 1),
+            norm_eval=False),
+        fast_pathway=dict(
+            type='resnet3d',
+            depth=50,
+            pretrained=None,
+            lateral=False,
+            base_channels=8,
+            conv1_kernel=(5, 7, 7),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            norm_eval=False)),
+    cls_head=dict(
+        type='SlowFastHead',
+        in_channels=2304,  # 2048+256
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5),
+    # model training and testing settings
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob'))
+
+train_cfg = None
+test_cfg = dict(average_clips='prob')
+
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)  # 16gpu 0.1->0.2
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+lr_config = dict(policy='step', step=[94, 154, 196])
+
+total_epochs = 239
+
+evaluation = dict(
+    interval=3, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+log_config = dict(
+    interval=20,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        #    dict(type='TensorboardLoggerHook'),
+    ])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+
+checkpoint_config = dict(interval=3)
+workflow = [('train', 1)]
+
+find_unused_parameters = False
+
+multigrid = dict(
+    long_cycle=True,
+    short_cycle=True,
+    epoch_factor=1.5,
+    long_cycle_factors=[[0.25, 0.7071], [0.5, 0.7071], [0.5, 1], [1, 1]],
+    short_cycle_factors=[0.5, 0.7071],
+    default_s=(224, 224),
+)
+
+precise_bn = dict(num_iters=200, interval=3)
+
+load_from = None
+resume_from = None
+
+work_dir = './work_dirs/slowfast_multigrid_r50_8x8x1_358e_kinetics400_rgb'

configs/recognition/slowfast/slowfast_prebn_r50_4x16x1_256e_kinetics400_rgb.py

+_base_ = [
+    '../../_base_/models/slowfast_r50.py', '../../_base_/default_runtime.py'
+]
+
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.1, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=34)
+total_epochs = 256
+
+# precise bn
+precise_bn = dict(num_iters=200, interval=1)
+
+# runtime settings
+checkpoint_config = dict(interval=4)
+work_dir = './work_dirs/slowfast_prebn_r50_4x16x1_256e_kinetics400_rgb'
+find_unused_parameters = False

configs/recognition/slowfast/slowfast_prebn_r50_8x8x1_256e_kinetics400_rgb_steplr.py

+_base_ = ['./slowfast_r50_8x8x1_256e_kinetics400_rgb.py']
+
+model = dict(backbone=dict(slow_pathway=dict(lateral_norm=True)))
+
+lr_config = dict(
+    policy='step',
+    min_lr=0,
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=34,
+    step=[94, 154, 196])
+
+precise_bn = dict(num_iters=200, interval=5)
+
+work_dir = './work_dirs/slowfast_prebn_r50_8x8x1_256e_kinetics400_rgb_steplr'

configs/recognition/slowfast/slowfast_r101_8x8x1_256e_kinetics400_rgb.py

+model = dict(
+    type='Recognizer3D',
+    backbone=dict(
+        type='ResNet3dSlowFast',
+        pretrained=None,
+        resample_rate=4,  # tau
+        speed_ratio=4,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(
+            type='resnet3d',
+            depth=101,
+            pretrained=None,
+            lateral=True,
+            fusion_kernel=7,
+            conv1_kernel=(1, 7, 7),
+            dilations=(1, 1, 1, 1),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            inflate=(0, 0, 1, 1),
+            norm_eval=False),
+        fast_pathway=dict(
+            type='resnet3d',
+            depth=101,
+            pretrained=None,
+            lateral=False,
+            base_channels=8,
+            conv1_kernel=(5, 7, 7),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            norm_eval=False)),
+    cls_head=dict(
+        type='SlowFastHead',
+        in_channels=2304,  # 2048+256
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5),
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob', max_testing_views=10))
+
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.1, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=34)
+total_epochs = 256
+checkpoint_config = dict(interval=4)
+workflow = [('train', 1)]
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+log_config = dict(
+    interval=20,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        #    dict(type='TensorboardLoggerHook'),
+    ])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/slowfast_r101_8x8x1_256e_kinetics400_rgb'
+load_from = None
+resume_from = None
+find_unused_parameters = False

configs/recognition/slowfast/slowfast_r101_r50_4x16x1_256e_kinetics400_rgb.py

+model = dict(
+    type='Recognizer3D',
+    backbone=dict(
+        type='ResNet3dSlowFast',
+        pretrained=None,
+        resample_rate=8,  # tau
+        speed_ratio=8,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(
+            type='resnet3d',
+            depth=101,
+            pretrained=None,
+            lateral=True,
+            conv1_kernel=(1, 7, 7),
+            dilations=(1, 1, 1, 1),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            inflate=(0, 0, 1, 1),
+            norm_eval=False),
+        fast_pathway=dict(
+            type='resnet3d',
+            depth=50,
+            pretrained=None,
+            lateral=False,
+            base_channels=8,
+            conv1_kernel=(5, 7, 7),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            norm_eval=False)),
+    cls_head=dict(
+        type='SlowFastHead',
+        in_channels=2304,  # 2048+256
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5),
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob', max_testing_views=10))
+
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.1, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=34)
+total_epochs = 256
+checkpoint_config = dict(interval=4)
+workflow = [('train', 1)]
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+log_config = dict(
+    interval=20,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        #    dict(type='TensorboardLoggerHook'),
+    ])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/slowfast_r101_r50_4x16x1_256e_kinetics400_rgb'
+load_from = None
+resume_from = None
+find_unused_parameters = False

configs/recognition/slowfast/slowfast_r152_r50_4x16x1_256e_kinetics400_rgb.py

+model = dict(
+    type='Recognizer3D',
+    backbone=dict(
+        type='ResNet3dSlowFast',
+        pretrained=None,
+        resample_rate=8,  # tau
+        speed_ratio=8,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(
+            type='resnet3d',
+            depth=152,
+            pretrained=None,
+            lateral=True,
+            conv1_kernel=(1, 7, 7),
+            dilations=(1, 1, 1, 1),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            inflate=(0, 0, 1, 1),
+            norm_eval=False),
+        fast_pathway=dict(
+            type='resnet3d',
+            depth=50,
+            pretrained=None,
+            lateral=False,
+            base_channels=8,
+            conv1_kernel=(5, 7, 7),
+            conv1_stride_t=1,
+            pool1_stride_t=1,
+            norm_eval=False)),
+    cls_head=dict(
+        type='SlowFastHead',
+        in_channels=2304,  # 2048+256
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5),
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob', max_testing_views=8))
+
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.1, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=34)
+total_epochs = 256
+checkpoint_config = dict(interval=4)
+workflow = [('train', 1)]
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+log_config = dict(
+    interval=20,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        #    dict(type='TensorboardLoggerHook'),
+    ])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/slowfast_r152_r50_4x16x1_256e_kinetics400_rgb'
+load_from = None
+resume_from = None
+find_unused_parameters = False

configs/recognition/slowfast/slowfast_r50_16x8x1_22e_sthv1_rgb.py

+_base_ = [
+    '../../_base_/models/slowfast_r50.py', '../../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        resample_rate=4,  # tau
+        speed_ratio=4,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(fusion_kernel=7)),
+    cls_head=dict(num_classes=174))
+
+# dataset settings
+dataset_type = 'RawframeDataset'
+data_root = 'data/sthv1/rawframes'
+data_root_val = 'data/sthv1/rawframes'
+ann_file_train = 'data/sthv1/sthv1_train_list_rawframes.txt'
+ann_file_val = 'data/sthv1/sthv1_val_list_rawframes.txt'
+ann_file_test = 'data/sthv1/sthv1_val_list_rawframes.txt'
+
+sthv1_flip_label_map = {2: 4, 4: 2, 30: 41, 41: 30, 52: 66, 66: 52}
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=64, frame_interval=2, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5, flip_label_map=sthv1_flip_label_map),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=64,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=64,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+
+data = dict(
+    videos_per_gpu=4,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        filename_tmpl='{:05}.jpg',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        filename_tmpl='{:05}.jpg',
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        filename_tmpl='{:05}.jpg',
+        pipeline=test_pipeline))
+
+evaluation = dict(
+    interval=1, metrics=['top_k_accuracy'], start=18, gpu_collect=True)
+
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.06, momentum=0.9,
+    weight_decay=0.000001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    step=[14, 18],
+    warmup='linear',
+    warmup_by_epoch=False,
+    warmup_iters=16343 // 32)
+total_epochs = 22
+
+# runtime settings
+checkpoint_config = dict(interval=1)
+work_dir = './work_dirs/slowfast_r50_16x8x1_22e_sthv1_rgb'
+load_from = 'https://download.openmmlab.com/mmaction/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb/slowfast_r50_8x8x1_256e_kinetics400_rgb_20200716-73547d2b.pth'  # noqa: E501
+find_unused_parameters = False

configs/recognition/slowfast/slowfast_r50_4x16x1_256e_kinetics400_rgb.py

+_base_ = [
+    '../../_base_/models/slowfast_r50.py', '../../_base_/default_runtime.py'
+]
+
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.1, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=34)
+total_epochs = 256
+
+# runtime settings
+checkpoint_config = dict(interval=4)
+work_dir = './work_dirs/slowfast_r50_3d_4x16x1_256e_kinetics400_rgb'
+find_unused_parameters = False

configs/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb.py

+_base_ = ['./slowfast_r50_4x16x1_256e_kinetics400_rgb.py']
+
+model = dict(
+    backbone=dict(
+        resample_rate=4,  # tau
+        speed_ratio=4,  # alpha
+        channel_ratio=8,  # beta_inv
+        slow_pathway=dict(fusion_kernel=7)))
+
+work_dir = './work_dirs/slowfast_r50_3d_8x8x1_256e_kinetics400_rgb'

configs/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb_steplr.py

+_base_ = ['./slowfast_r50_8x8x1_256e_kinetics400_rgb.py']
+
+model = dict(backbone=dict(slow_pathway=dict(lateral_norm=True)))
+
+lr_config = dict(
+    policy='step',
+    min_lr=0,
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=34,
+    step=[94, 154, 196])
+
+work_dir = './work_dirs/slowfast_r50_8x8x1_256e_kinetics400_rgb_steplr'

configs/recognition/slowfast/slowfast_r50_video_4x16x1_256e_kinetics400_rgb.py

+_base_ = ['./slowfast_r50_4x16x1_256e_kinetics400_rgb.py']
+
+model = dict(
+    backbone=dict(
+        resample_rate=8,  # tau
+        speed_ratio=8,  # alpha
+        channel_ratio=8  # beta_inv
+    ))
+
+# dataset settings
+dataset_type = 'VideoDataset'
+data_root = 'data/kinetics400/videos_train'
+data_root_val = 'data/kinetics400/videos_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_videos.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='DecordInit'),
+    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
+    dict(type='DecordDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(type='DecordInit'),
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=1,
+        test_mode=True),
+    dict(type='DecordDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(type='DecordInit'),
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='DecordDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+
+# runtime settings
+work_dir = './work_dirs/slowfast_r50_video_3d_4x16x1_256e_kinetics400_rgb'

configs/recognition/slowfast/slowfast_r50_video_inference_4x16x1_256e_kinetics400_rgb.py

+_base_ = ['../../_base_/models/slowfast_r50.py']
+
+# dataset settings
+dataset_type = 'VideoDataset'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+
+test_pipeline = [
+    dict(type='DecordInit'),
+    dict(
+        type='SampleFrames',
+        clip_len=32,
+        frame_interval=2,
+        num_clips=10,
+        test_mode=True),
+    dict(type='DecordDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+
+data = dict(
+    videos_per_gpu=1,
+    workers_per_gpu=2,
+    test=dict(
+        type=dataset_type,
+        ann_file=None,
+        data_prefix=None,
+        pipeline=test_pipeline))

configs/recognition/slowonly/README.md

+# SlowOnly
+
+[Slowfast networks for video recognition](https://openaccess.thecvf.com/content_ICCV_2019/html/Feichtenhofer_SlowFast_Networks_for_Video_Recognition_ICCV_2019_paper.html)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/34324155/143044111-94676f64-7ba8-4081-9011-f8054bed7030.png" width="800"/>
+</div>
+
+## Results and Models
+
+### Kinetics-400
+
+| config                                                                                                                                                          |   resolution   | gpus | backbone | pretrain | top1 acc | top5 acc | inference_time(video/s) | gpu_mem(M) |                                                                                                          ckpt                                                                                                          |                                                                                                    log                                                                                                     |                                                                                                     json                                                                                                     |
+| :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------: | :--: | :------: | :------: | :------: | :------: | :---------------------: | :--------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py)                                           | short-side 256 | 8x4  | ResNet50 |   None   |  72.76   |  90.51   |            x            |    3168    |                 [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb_20200820-bea7701f.pth)                 |                                   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log)                                    |                                 [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log.json)                                  |
+| [slowonly_r50_video_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_video_4x16x1_256e_kinetics400_rgb.py)                               | short-side 320 | 8x2  | ResNet50 |   None   |  72.90   |  90.82   |            x            |    8472    |           [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014-c9cdc656.pth)           |          [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014.log)          |          [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014.json)          |
+| [slowonly_r50_8x8x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py)                                             | short-side 256 | 8x4  | ResNet50 |   None   |  74.42   |  91.49   |            x            |    5820    |                  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb_20200820-75851a7d.pth)                  |                                    [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/20200817_003320.log)                                    |                                  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/20200817_003320.log.json)                                  |
+| [slowonly_r50_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py)                                           | short-side 320 | 8x2  | ResNet50 |   None   |  73.02   |  90.77   |    4.0 (40x3 frames)    |    3168    |                      [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth)                      |                                          [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/so_4x16.log)                                          |                             [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16_73.02_90.77.log.json)                             |
+| [slowonly_r50_8x8x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py)                                             | short-side 320 | 8x3  | ResNet50 |   None   |  74.93   |  91.92   |    2.3 (80x3 frames)    |    5820    |                       [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/slowonly_r50_8x8x1_256e_kinetics400_rgb_20200703-a79c555a.pth)                       |                                           [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/so_8x8.log)                                           |                              [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/slowonly_r50_8x8_74.93_91.92.log.json)                              |
+| [slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb.py)   | short-side 320 | 8x2  | ResNet50 | ImageNet |  73.39   |  91.12   |            x            |    3168    |  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912-1e8fc736.pth)  | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912.json) |
+| [slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb.py)     | short-side 320 | 8x4  | ResNet50 | ImageNet |  75.55   |  92.04   |            x            |    5820    |   [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912-3f9ce182.pth)   |  [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912.log)  |  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912.json)  |
+| [slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb.py) | short-side 320 | 8x2  | ResNet50 | ImageNet |  74.54   |  91.73   |            x            |    4435    | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb_20210308-0d6e5a69.pth) |                           [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/20210305_152630.log)                            |                         [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/20210305_152630.log.json)                          |
+| [slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb.py)   | short-side 320 | 8x4  | ResNet50 | ImageNet |  76.07   |  92.42   |            x            |    8895    |  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb_20210308-e8dd9e82.pth)  |                            [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/20210308_212250.log)                            |                          [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/20210308_212250.log.json)                          |
+| [slowonly_r50_4x16x1_256e_kinetics400_flow](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow.py)                                         | short-side 320 | 8x2  | ResNet50 | ImageNet |  61.79   |  83.62   |            x            |    8450    |                     [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_20200704-decb8568.pth)                     |                   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_61.8_83.6.log)                    |                 [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_61.8_83.6.log.json)                  |
+| [slowonly_r50_8x8x1_196e_kinetics400_flow](/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow.py)                                           | short-side 320 | 8x4  | ResNet50 | ImageNet |  65.76   |  86.25   |            x            |    8455    |                      [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_256e_kinetics400_flow_20200704-6b384243.pth)                      |                    [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_196e_kinetics400_flow_65.8_86.3.log)                     |                  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_196e_kinetics400_flow_65.8_86.3.log.json)                   |
+
+### Kinetics-400 Data Benchmark
+
+In data benchmark, we compare two different data preprocessing methods: (1) Resize video to 340x256, (2) Resize the short edge of video to 320px, (3) Resize the short edge of video to 256px.
+
+| config                                                                                                                                                                                   |   resolution   | gpus | backbone | Input | pretrain | top1 acc | top5 acc |  testing protocol  |                                                                                                                      ckpt                                                                                                                       |                                                                                                                  log                                                                                                                  |                                                                                                                  json                                                                                                                   |
+| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------: | :--: | :------: | :---: | :------: | :------: | :------: | :----------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb.py) |    340x256     | 8x2  | ResNet50 | 4x16  |   None   |  71.61   |  90.05   | 10 clips x 3 crops | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb_20200803-dadca1a3.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb_20200803.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb_20200803.json) |
+| [slowonly_r50_randomresizedcrop_320p_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_320p_4x16x1_256e_kinetics400_rgb.py)       | short-side 320 | 8x2  | ResNet50 | 4x16  |   None   |  73.02   |  90.77   | 10 clips x 3 crops |                                  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth)                                   |                                                       [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/so_4x16.log)                                                        |                                          [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16_73.02_90.77.log.json)                                           |
+| [slowonly_r50_randomresizedcrop_256p_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_256p_4x16x1_256e_kinetics400_rgb.py)       | short-side 256 | 8x4  | ResNet50 | 4x16  |   None   |  72.76   |  90.51   | 10 clips x 3 crops |                             [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb_20200820-bea7701f.pth)                              |                                                 [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log)                                                 |                                               [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log.json)                                               |
+
+### Kinetics-400 OmniSource Experiments
+
+|                                                        config                                                         |   resolution   | backbone  | pretrain |   w. OmniSource    | top1 acc | top5 acc |                                                                                     ckpt                                                                                     |                                                           log                                                            |                                                                         json                                                                         |
+| :-------------------------------------------------------------------------------------------------------------------: | :------------: | :-------: | :------: | :----------------: | :------: | :------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py) | short-side 320 | ResNet50  |   None   |        :x:         |   73.0   |   90.8   | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/so_4x16.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16_73.02_90.77.log.json) |
+|                                                           x                                                           |       x        | ResNet50  |   None   | :heavy_check_mark: |   76.8   |   92.5   |                   [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r50_omni_4x16x1_kinetics400_rgb_20200926-51b1f7ea.pth)                   |                                                            x                                                             |                                                                          x                                                                           |
+| [slowonly_r101_8x8x1_196e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r101_8x8x1_196e_kinetics400_rgb.py) |       x        | ResNet101 |   None   |        :x:         |   76.5   |   92.7   |               [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r101_without_omni_8x8x1_kinetics400_rgb_20200926-0c730aef.pth)               |                                                            x                                                             |                                                                          x                                                                           |
+|                                                           x                                                           |       x        | ResNet101 |   None   | :heavy_check_mark: |   80.4   |   94.4   |                   [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r101_omni_8x8x1_kinetics400_rgb_20200926-b5dbb701.pth)                   |                                                            x                                                             |                                                                          x                                                                           |
+
+### Kinetics-600
+
+| config                                                                                                                          |   resolution   | gpus | backbone | pretrain | top1 acc | top5 acc |                                                                                          ckpt                                                                                          |                                                                                     log                                                                                      |                                                                                      json                                                                                      |
+| :------------------------------------------------------------------------------------------------------------------------------ | :------------: | :--: | :------: | :------: | :------: | :------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_video_8x8x1_256e_kinetics600_rgb](/configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb.py) | short-side 256 | 8x4  | ResNet50 |   None   |   77.5   |   93.7   | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015-81e5153e.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015.json) |
+
+### Kinetics-700
+
+| config                                                                                                                          |   resolution   | gpus | backbone | pretrain | top1 acc | top5 acc |                                                                                          ckpt                                                                                          |                                                                                     log                                                                                      |                                                                                      json                                                                                      |
+| :------------------------------------------------------------------------------------------------------------------------------ | :------------: | :--: | :------: | :------: | :------: | :------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_video_8x8x1_256e_kinetics700_rgb](/configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb.py) | short-side 256 | 8x4  | ResNet50 |   None   |   65.0   |   86.1   | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015-9250f662.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015.json) |
+
+### GYM99
+
+| config                                                                                                                                            |   resolution   | gpus | backbone | pretrain | top1 acc | mean class acc |                                                                                                    ckpt                                                                                                    |                                                                                               log                                                                                                |                                                                                                json                                                                                                |
+| :------------------------------------------------------------------------------------------------------------------------------------------------ | :------------: | :--: | :------: | :------: | :------: | :------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb.py) | short-side 256 | 8x2  | ResNet50 | ImageNet |   79.3   |      70.2      |  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111-a9c34b54.pth)  |  [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111.log)  |  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111.json)  |
+| [slowonly_k400_pretrained_r50_4x16x1_120e_gym99_flow](/configs/recognition/slowonly/slowonly_k400_pretrained_r50_4x16x1_120e_gym99_flow.py)       | short-side 256 | 8x2  | ResNet50 | Kinetics |   80.3   |      71.0      | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111-66ecdb3c.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111.json) |
+| 1: 1 Fusion                                                                                                                                       |                |      |          |          |   83.7   |      74.8      |                                                                                                                                                                                                            |                                                                                                                                                                                                  |                                                                                                                                                                                                    |
+
+### Jester
+
+| config                                                                                                                                          | resolution | gpus | backbone | pretrain | top1 acc |                                                                                             ckpt                                                                                              |                                                                                         log                                                                                         |                                                                                         json                                                                                          |
+| :---------------------------------------------------------------------------------------------------------------------------------------------- | :--------: | :--: | :------: | :------: | :------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.py) | height 100 |  8   | ResNet50 | ImageNet |   97.2   | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb-b56a5389.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.json) |
+
+### HMDB51
+
+| config                                                                                                                                          | gpus | backbone |  pretrain   | top1 acc | top5 acc | gpu_mem(M) |                                                                                                  ckpt                                                                                                  |                                                                      log                                                                      |                                                                        json                                                                         |
+| :---------------------------------------------------------------------------------------------------------------------------------------------- | :--: | :------: | :---------: | :------: | :------: | :--------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb.py) |  8   | ResNet50 |  ImageNet   |  37.52   |  71.50   |    5812    | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb_20210630-16faeb6a.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/20210605_185256.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/20210605_185256.log.json) |
+| [slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb](/configs/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb.py)         |  8   | ResNet50 | Kinetics400 |  65.95   |  91.05   |    5812    |     [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb_20210630-cee5f725.pth)     |   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/20210606_010153.log)   |   [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/20210606_010153.log.json)   |
+
+### UCF101
+
+| config                                                                                                                                          | gpus | backbone |  pretrain   | top1 acc | top5 acc | gpu_mem(M) |                                                                                                  ckpt                                                                                                  |                                                                      log                                                                      |                                                                        json                                                                         |
+| :---------------------------------------------------------------------------------------------------------------------------------------------- | :--: | :------: | :---------: | :------: | :------: | :--------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb.py) |  8   | ResNet50 |  ImageNet   |  71.35   |  89.35   |    5812    | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb_20210630-181e1661.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/20210605_213503.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/20210605_213503.log.json) |
+| [slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb](/configs/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb.py)         |  8   | ResNet50 | Kinetics400 |  92.78   |  99.42   |    5812    |     [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb_20210630-ee8c850f.pth)     |   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/20210606_010231.log)   |   [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/20210606_010231.log.json)   |
+
+### Something-Something V1
+
+| config                                                                                                                                        | gpus | backbone | pretrain | top1 acc | top5 acc | gpu_mem(M) |                                                                                                 ckpt                                                                                                 |                                                                                        log                                                                                        |                                                                                        json                                                                                         |
+| :-------------------------------------------------------------------------------------------------------------------------------------------- | :--: | :------: | :------: | :------: | :------: | :--------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.py) |  8   | ResNet50 | ImageNet |  47.76   |  77.49   |    7759    | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb_20211202-d034ff12.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.json) |
+
+:::{note}
+
+1. The **gpus** indicates the number of gpu we used to get the checkpoint. It is noteworthy that the configs we provide are used for 8 gpus as default.
+   According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU,
+   e.g., lr=0.01 for 4 GPUs x 2 video/gpu and lr=0.08 for 16 GPUs x 4 video/gpu.
+2. The **inference_time** is got by this [benchmark script](/tools/analysis/benchmark.py), where we use the sampling frames strategy of the test setting and only care about the model inference time, not including the IO time and pre-processing time. For each setting, we use 1 gpu and set batch size (videos per gpu) to 1 to calculate the inference time.
+3. The validation set of Kinetics400 we used consists of 19796 videos. These videos are available at [Kinetics400-Validation](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155136485_link_cuhk_edu_hk/EbXw2WX94J1Hunyt3MWNDJUBz-nHvQYhO9pvKqm6g39PMA?e=a9QldB). The corresponding [data list](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_val_list.txt) (each line is of the format 'video_id, num_frames, label_index') and the [label map](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_class2ind.txt) are also available.
+
+:::
+
+For more details on data preparation, you can refer to corresponding parts in [Data Preparation](/docs/data_preparation.md).
+
+## Train
+
+You can use the following command to train a model.
+
+```shell
+python tools/train.py ${CONFIG_FILE} [optional arguments]
+```
+
+Example: train SlowOnly model on Kinetics-400 dataset in a deterministic option with periodic validation.
+
+```shell
+python tools/train.py configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py \
+    --work-dir work_dirs/slowonly_r50_4x16x1_256e_kinetics400_rgb \
+    --validate --seed 0 --deterministic
+```
+
+For more details, you can refer to **Training setting** part in [getting_started](/docs/getting_started.md#training-setting).
+
+## Test
+
+You can use the following command to test a model.
+
+```shell
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
+```
+
+Example: test SlowOnly model on Kinetics-400 dataset and dump the result to a json file.
+
+```shell
+python tools/test.py configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py \
+    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
+    --out result.json --average-clips=prob
+```
+
+For more details, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset).
+
+## Citation
+
+```BibTeX
+@inproceedings{feichtenhofer2019slowfast,
+  title={Slowfast networks for video recognition},
+  author={Feichtenhofer, Christoph and Fan, Haoqi and Malik, Jitendra and He, Kaiming},
+  booktitle={Proceedings of the IEEE international conference on computer vision},
+  pages={6202--6211},
+  year={2019}
+}
+```

configs/recognition/slowonly/README_zh-CN.md

+# SlowOnly
+
+## 简介
+
+<!-- [ALGORITHM] -->
+
+```BibTeX
+@inproceedings{feichtenhofer2019slowfast,
+  title={Slowfast networks for video recognition},
+  author={Feichtenhofer, Christoph and Fan, Haoqi and Malik, Jitendra and He, Kaiming},
+  booktitle={Proceedings of the IEEE international conference on computer vision},
+  pages={6202--6211},
+  year={2019}
+}
+```
+
+## 模型库
+
+### Kinetics-400
+
+| 配置文件                                                                                                                                                        |  分辨率  | GPU 数量 | 主干网络 |  预训练  | top1 准确率 | top5 准确率 | 推理时间 (video/s) | GPU 显存占用 (M) |                                                                                                          ckpt                                                                                                          |                                                                                                    log                                                                                                     |                                                                                                     json                                                                                                     |
+| :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :------: | :---------: | :---------: | :----------------: | :--------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py)                                           | 短边 256 |   8x4    | ResNet50 |   None   |    72.76    |    90.51    |         x          |       3168       |                 [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb_20200820-bea7701f.pth)                 |                                   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log)                                    |                                 [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log.json)                                  |
+| [slowonly_r50_video_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_video_4x16x1_256e_kinetics400_rgb.py)                               | 短边 320 |   8x2    | ResNet50 |   None   |    72.90    |    90.82    |         x          |       8472       |           [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014-c9cdc656.pth)           |          [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014.log)          |          [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014.json)          |
+| [slowonly_r50_8x8x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py)                                             | 短边 256 |   8x4    | ResNet50 |   None   |    74.42    |    91.49    |         x          |       5820       |                  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb_20200820-75851a7d.pth)                  |                                    [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/20200817_003320.log)                                    |                                  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/20200817_003320.log.json)                                  |
+| [slowonly_r50_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py)                                           | 短边 320 |   8x2    | ResNet50 |   None   |    73.02    |    90.77    | 4.0 (40x3 frames)  |       3168       |                      [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth)                      |                                          [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/so_4x16.log)                                          |                             [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16_73.02_90.77.log.json)                             |
+| [slowonly_r50_8x8x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py)                                             | 短边 320 |   8x3    | ResNet50 |   None   |    74.93    |    91.92    | 2.3 (80x3 frames)  |       5820       |                       [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/slowonly_r50_8x8x1_256e_kinetics400_rgb_20200703-a79c555a.pth)                       |                                           [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/so_8x8.log)                                           |                              [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/slowonly_r50_8x8_74.93_91.92.log.json)                              |
+| [slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb.py)   | 短边 320 |   8x2    | ResNet50 | ImageNet |    73.39    |    91.12    |         x          |       3168       |  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912-1e8fc736.pth)  | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912.json) |
+| [slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb.py)     | 短边 320 |   8x4    | ResNet50 | ImageNet |    75.55    |    92.04    |         x          |       5820       |   [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912-3f9ce182.pth)   |  [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912.log)  |  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912.json)  |
+| [slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb.py) | 短边 320 |   8x2    | ResNet50 | ImageNet |    74.54    |    91.73    |         x          |       4435       | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb_20210308-0d6e5a69.pth) |                           [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/20210305_152630.log)                            |                         [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/20210305_152630.log.json)                          |
+| [slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb.py)   | 短边 320 |   8x4    | ResNet50 | ImageNet |    76.07    |    92.42    |         x          |       8895       |  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb_20210308-e8dd9e82.pth)  |                            [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/20210308_212250.log)                            |                          [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/20210308_212250.log.json)                          |
+| [slowonly_r50_4x16x1_256e_kinetics400_flow](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow.py)                                         | 短边 320 |   8x2    | ResNet50 | ImageNet |    61.79    |    83.62    |         x          |       8450       |                     [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_20200704-decb8568.pth)                     |                   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_61.8_83.6.log)                    |                 [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_61.8_83.6.log.json)                  |
+| [slowonly_r50_8x8x1_196e_kinetics400_flow](/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow.py)                                           | 短边 320 |   8x4    | ResNet50 | ImageNet |    65.76    |    86.25    |         x          |       8455       |                      [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_256e_kinetics400_flow_20200704-6b384243.pth)                      |                    [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_196e_kinetics400_flow_65.8_86.3.log)                     |                  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_196e_kinetics400_flow_65.8_86.3.log.json)                   |
+
+### Kinetics-400 数据基准测试
+
+在数据基准测试中，比较两种不同的数据预处理方法 (1) 视频分辨率为 340x256, (2) 视频分辨率为短边 320px, (3) 视频分辨率为短边 256px.
+
+| 配置文件                                                                                                                                                                                 |  分辨率  | GPU 数量 | 主干网络 | 输入 | 预训练 | top1 准确率 | top5 准确率 |      测试方案      |                                                                                                                      ckpt                                                                                                                       |                                                                                                                  log                                                                                                                  |                                                                                                                  json                                                                                                                   |
+| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :--: | :----: | :---------: | :---------: | :----------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb.py) | 340x256  |   8x2    | ResNet50 | 4x16 |  None  |    71.61    |    90.05    | 10 clips x 3 crops | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb_20200803-dadca1a3.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb_20200803.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb_20200803.json) |
+| [slowonly_r50_randomresizedcrop_320p_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_320p_4x16x1_256e_kinetics400_rgb.py)       | 短边 320 |   8x2    | ResNet50 | 4x16 |  None  |    73.02    |    90.77    | 10 clips x 3 crops |                                  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth)                                   |                                                       [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/so_4x16.log)                                                        |                                          [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16_73.02_90.77.log.json)                                           |
+| [slowonly_r50_randomresizedcrop_256p_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_256p_4x16x1_256e_kinetics400_rgb.py)       | 短边 256 |   8x4    | ResNet50 | 4x16 |  None  |    72.76    |    90.51    | 10 clips x 3 crops |                             [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb_20200820-bea7701f.pth)                              |                                                 [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log)                                                 |                                               [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log.json)                                               |
+
+### Kinetics-400 OmniSource Experiments
+
+|                                                       配置文件                                                        |  分辨率  | 主干网络  | 预训练 |   w. OmniSource    | top1 准确率 | top5 准确率 |                                                                                     ckpt                                                                                     |                                                           log                                                            |                                                                         json                                                                         |
+| :-------------------------------------------------------------------------------------------------------------------: | :------: | :-------: | :----: | :----------------: | :---------: | :---------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_4x16x1_256e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py) | 短边 320 | ResNet50  |  None  |        :x:         |    73.0     |    90.8     | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/so_4x16.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16_73.02_90.77.log.json) |
+|                                                           x                                                           |    x     | ResNet50  |  None  | :heavy_check_mark: |    76.8     |    92.5     |                   [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r50_omni_4x16x1_kinetics400_rgb_20200926-51b1f7ea.pth)                   |                                                            x                                                             |                                                                          x                                                                           |
+| [slowonly_r101_8x8x1_196e_kinetics400_rgb](/configs/recognition/slowonly/slowonly_r101_8x8x1_196e_kinetics400_rgb.py) |    x     | ResNet101 |  None  |        :x:         |    76.5     |    92.7     |               [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r101_without_omni_8x8x1_kinetics400_rgb_20200926-0c730aef.pth)               |                                                            x                                                             |                                                                          x                                                                           |
+|                                                           x                                                           |    x     | ResNet101 |  None  | :heavy_check_mark: |    80.4     |    94.4     |                   [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r101_omni_8x8x1_kinetics400_rgb_20200926-b5dbb701.pth)                   |                                                            x                                                             |                                                                          x                                                                           |
+
+### Kinetics-600
+
+| 配置文件                                                                                                                        |  分辨率  | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 |                                                                                          ckpt                                                                                          |                                                                                     log                                                                                      |                                                                                      json                                                                                      |
+| :------------------------------------------------------------------------------------------------------------------------------ | :------: | :------: | :------: | :----: | :---------: | :---------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_video_8x8x1_256e_kinetics600_rgb](/configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb.py) | 短边 256 |   8x4    | ResNet50 |  None  |    77.5     |    93.7     | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015-81e5153e.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015.json) |
+
+### Kinetics-700
+
+| 配置文件                                                                                                                        |  分辨率  | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 |                                                                                          ckpt                                                                                          |                                                                                     log                                                                                      |                                                                                      json                                                                                      |
+| :------------------------------------------------------------------------------------------------------------------------------ | :------: | :------: | :------: | :----: | :---------: | :---------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_r50_video_8x8x1_256e_kinetics700_rgb](/configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb.py) | 短边 256 |   8x4    | ResNet50 |  None  |    65.0     |    86.1     | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015-9250f662.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015.json) |
+
+### GYM99
+
+| 配置文件                                                                                                                                          |  分辨率  | GPU 数量 | 主干网络 |  预训练  | top1 准确率 | 类别平均准确率 |                                                                                                    ckpt                                                                                                    |                                                                                               log                                                                                                |                                                                                                json                                                                                                |
+| :------------------------------------------------------------------------------------------------------------------------------------------------ | :------: | :------: | :------: | :------: | :---------: | :------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb.py) | 短边 256 |   8x2    | ResNet50 | ImageNet |    79.3     |      70.2      |  [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111-a9c34b54.pth)  |  [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111.log)  |  [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111.json)  |
+| [slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow](/configs/recognition/slowonly/slowonly_k400_pretrained_r50_4x16x1_120e_gym99_flow.py)   | 短边 256 |   8x2    | ResNet50 | Kinetics |    80.3     |      71.0      | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111-66ecdb3c.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111.json) |
+| 1: 1 融合                                                                                                                                         |          |          |          |          |    83.7     |      74.8      |                                                                                                                                                                                                            |                                                                                                                                                                                                  |                                                                                                                                                                                                    |
+
+### Jester
+
+| 配置文件                                                                                                                                        | 分辨率 | GPU 数量 | 主干网络 |  预训练  | top1 准确率 |                                                                                             ckpt                                                                                              |                                                                                         log                                                                                         |                                                                                         json                                                                                          |
+| :---------------------------------------------------------------------------------------------------------------------------------------------- | :----: | :------: | :------: | :------: | :---------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.py) | 高 100 |    8     | ResNet50 | ImageNet |    97.2     | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb-b56a5389.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.json) |
+
+### HMDB51
+
+| 配置文件                                                                                                                                        | GPU 数量 | 主干网络 |   预训练    | top1 准确率 | top5 准确率 | GPU 显存占用 (M) |                                                                                                  ckpt                                                                                                  |                                                                      log                                                                      |                                                                        json                                                                         |
+| :---------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :---------: | :---------: | :---------: | :--------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb.py) |    8     | ResNet50 |  ImageNet   |    37.52    |    71.50    |       5812       | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb_20210630-16faeb6a.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/20210605_185256.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/20210605_185256.log.json) |
+| [slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb](/configs/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb.py)         |    8     | ResNet50 | Kinetics400 |    65.95    |    91.05    |       5812       |     [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb_20210630-cee5f725.pth)     |   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/20210606_010153.log)   |   [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/20210606_010153.log.json)   |
+
+### UCF101
+
+| 配置文件                                                                                                                                        | GPU 数量 | 主干网络 |   预训练    | top1 准确率 | top5 准确率 | GPU 显存占用 (M) |                                                                                                  ckpt                                                                                                  |                                                                      log                                                                      |                                                                        json                                                                         |
+| :---------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :---------: | :---------: | :---------: | :--------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb.py) |    8     | ResNet50 |  ImageNet   |    71.35    |    89.35    |       5812       | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb_20210630-181e1661.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/20210605_213503.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/20210605_213503.log.json) |
+| [slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb](/configs/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb.py)         |    8     | ResNet50 | Kinetics400 |    92.78    |    99.42    |       5812       |     [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb_20210630-ee8c850f.pth)     |   [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/20210606_010231.log)   |   [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/20210606_010231.log.json)   |
+
+### Something-Something V1
+
+| 配置文件                                                                                                                                      | GPU 数量 | 主干网络 |  预训练  | top1 准确率 | top5 准确率 | GPU 显存占用 (M) |                                                                                                 ckpt                                                                                                 |                                                                                        log                                                                                        |                                                                                        json                                                                                         |
+| :-------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :---------: | :---------: | :--------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb](/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.py) |    8     | ResNet50 | ImageNet |    47.76    |    77.49    |       7759       | [ckpt](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb_20211202-d034ff12.pth) | [log](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.json) |
+
+注：
+
+1. 这里的 **GPU 数量** 指的是得到模型权重文件对应的 GPU 个数。默认地，MMAction2 所提供的配置文件对应使用 8 块 GPU 进行训练的情况。
+   依据 [线性缩放规则](https://arxiv.org/abs/1706.02677)，当用户使用不同数量的 GPU 或者每块 GPU 处理不同视频个数时，需要根据批大小等比例地调节学习率。
+   如，lr=0.01 对应 4 GPUs x 2 video/gpu，以及 lr=0.08 对应 16 GPUs x 4 video/gpu。
+2. 这里的 **推理时间** 是根据 [基准测试脚本](/tools/analysis/benchmark.py) 获得的，采用测试时的采帧策略，且只考虑模型的推理时间，
+   并不包括 IO 时间以及预处理时间。对于每个配置，MMAction2 使用 1 块 GPU 并设置批大小（每块 GPU 处理的视频个数）为 1 来计算推理时间。
+3. 我们使用的 Kinetics400 验证集包含 19796 个视频，用户可以从 [验证集视频](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155136485_link_cuhk_edu_hk/EbXw2WX94J1Hunyt3MWNDJUBz-nHvQYhO9pvKqm6g39PMA?e=a9QldB) 下载这些视频。同时也提供了对应的 [数据列表](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_val_list.txt) （每行格式为：视频 ID，视频帧数目，类别序号）以及 [标签映射](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_class2ind.txt) （类别序号到类别名称）。
+
+对于数据集准备的细节，用户可参考 [数据集准备文档](/docs_zh_CN/data_preparation.md) 中的 Kinetics400 部分。
+
+## 如何训练
+
+用户可以使用以下指令进行模型训练。
+
+```shell
+python tools/train.py ${CONFIG_FILE} [optional arguments]
+```
+
+例如：以一个确定性的训练方式，辅以定期的验证过程进行 SlowOnly 模型在 Kinetics400 数据集上的训练。
+
+```shell
+python tools/train.py configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py \
+    --work-dir work_dirs/slowonly_r50_4x16x1_256e_kinetics400_rgb \
+    --validate --seed 0 --deterministic
+```
+
+更多训练细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#%E8%AE%AD%E7%BB%83%E9%85%8D%E7%BD%AE) 中的 **训练配置** 部分。
+
+## 如何测试
+
+用户可以使用以下指令进行模型测试。
+
+```shell
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
+```
+
+例如：在 Kinetics400 数据集上测试 SlowOnly 模型，并将结果导出为一个 json 文件。
+
+```shell
+python tools/test.py configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py \
+    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
+    --out result.json --average-clips=prob
+```
+
+更多测试细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#%E6%B5%8B%E8%AF%95%E6%9F%90%E4%B8%AA%E6%95%B0%E6%8D%AE%E9%9B%86) 中的 **测试某个数据集** 部分。

configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_256p_4x16x1_256e_kinetics400_rgb.py

+model = dict(
+    type='Recognizer3D',
+    backbone=dict(
+        type='ResNet3dSlowOnly',
+        depth=50,
+        pretrained=None,
+        lateral=False,
+        conv1_kernel=(1, 7, 7),
+        conv1_stride_t=1,
+        pool1_stride_t=1,
+        inflate=(0, 0, 1, 1),
+        norm_eval=False),
+    cls_head=dict(
+        type='I3DHead',
+        in_channels=2048,
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5),
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob'))
+
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train_256p'
+data_root_val = 'data/kinetics400/rawframes_val_256p'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes_256p.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes_256p.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes_256p.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=4, frame_interval=16, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.6, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
+total_epochs = 256
+checkpoint_config = dict(interval=4)
+workflow = [('train', 1)]
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+log_config = dict(
+    interval=20,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        #    dict(type='TensorboardLoggerHook'),
+    ])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = ('./work_dirs/slowonly_r50_randomresizedcrop_256p_4x16x1'
+            '_256e_kinetics400_rgb')
+load_from = None
+resume_from = None
+find_unused_parameters = False

configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_320p_4x16x1_256e_kinetics400_rgb.py

+model = dict(
+    type='Recognizer3D',
+    backbone=dict(
+        type='ResNet3dSlowOnly',
+        depth=50,
+        pretrained=None,
+        lateral=False,
+        conv1_kernel=(1, 7, 7),
+        conv1_stride_t=1,
+        pool1_stride_t=1,
+        inflate=(0, 0, 1, 1),
+        norm_eval=False),
+    cls_head=dict(
+        type='I3DHead',
+        in_channels=2048,
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5),
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob'))
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train_320p'
+data_root_val = 'data/kinetics400/rawframes_val_320p'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes_320p.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes_320p.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes_320p.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=4, frame_interval=16, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.6, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
+total_epochs = 256
+checkpoint_config = dict(interval=4)
+workflow = [('train', 1)]
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+log_config = dict(
+    interval=20,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        #    dict(type='TensorboardLoggerHook'),
+    ])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = ('./work_dirs/slowonly_r50_randomresizedcrop_320p_4x16x1'
+            '_256e_kinetics400_rgb')
+load_from = None
+resume_from = None
+find_unused_parameters = False

configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb.py

+model = dict(
+    type='Recognizer3D',
+    backbone=dict(
+        type='ResNet3dSlowOnly',
+        depth=50,
+        pretrained=None,
+        lateral=False,
+        conv1_kernel=(1, 7, 7),
+        conv1_stride_t=1,
+        pool1_stride_t=1,
+        inflate=(0, 0, 1, 1),
+        norm_eval=False),
+    cls_head=dict(
+        type='I3DHead',
+        in_channels=2048,
+        num_classes=400,
+        spatial_type='avg',
+        dropout_ratio=0.5),
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob'))
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=4, frame_interval=16, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.6, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(policy='CosineAnnealing', min_lr=0)
+total_epochs = 256
+checkpoint_config = dict(interval=4)
+workflow = [('train', 1)]
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+log_config = dict(
+    interval=20,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        #    dict(type='TensorboardLoggerHook'),
+    ])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = ('slowonly_r50_randomresizedcrop_320p_4x16x1'
+            '_256e_kinetics400_rgb')
+load_from = None
+resume_from = None
+find_unused_parameters = False

configs/recognition/slowonly/metafile.yml

+Collections:
+- Name: SlowOnly
+  README: configs/recognition/slowonly/README.md
+  Paper:
+    URL: https://arxiv.org/abs/1812.03982
+    Title: SlowFast Networks for Video Recognition
+Models:
+- Config: configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 256
+    FLOPs: 27430649856
+    Parameters: 32454096
+    Pretrained: None
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+  Modality: RGB
+  Name: slowonly_r50_omnisource_4x16x1_256e_kinetics400_rgb
+  Converted From:
+    Weights: https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmaction/models/kinetics400/omnisource/slowonly_OmniSource_kinetics400_se_rgb_r50_seg1_4x16_scratch-71f7b8ee.pth
+    Code: https://github.com/open-mmlab/mmaction/
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 76.8
+      Top 5 Accuracy: 92.5
+    Task: Action Recognition
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r50_omni_4x16x1_kinetics400_rgb_20200926-51b1f7ea.pth
+- Config: configs/recognition/slowonly/slowonly_r101_8x8x1_196e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet101
+    Batch Size: 8
+    Epochs: 196
+    FLOPs: 112063447040
+    Parameters: 60359120
+    Pretrained: None
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+  Modality: RGB
+  Name: slowonly_r101_8x8x1_196e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 76.5
+      Top 5 Accuracy: 92.7
+    Task: Action Recognition
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r101_without_omni_8x8x1_kinetics400_rgb_20200926-0c730aef.pth
+- Config: configs/recognition/slowonly/slowonly_r101_8x8x1_196e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet101
+    Batch Size: 8
+    Epochs: 196
+    FLOPs: 112063447040
+    Parameters: 60359120
+    Pretrained: None
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+  Modality: RGB
+  Name: slowonly_r101_omnisource_8x8x1_196e_kinetics400_rgb
+  Converted From:
+    Weights: https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmaction/models/kinetics400/omnisource/slowonly_OmniSource_kinetics400_se_rgb_r101_seg1_8x8_scratch-2f838cb0.pth
+    Code: https://github.com/open-mmlab/mmaction/
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 80.4
+      Top 5 Accuracy: 94.4
+    Task: Action Recognition
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/omni/slowonly_r101_omni_8x8x1_kinetics400_rgb_20200926-b5dbb701.pth
+- Config: configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 256
+    FLOPs: 27430649856
+    Parameters: 32454096
+    Pretrained: None
+    Resolution: short-side 256
+    Training Data: Kinetics-400
+    Training Resources: 32 GPUs
+  Modality: RGB
+  Name: slowonly_r50_4x16x1_256e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 72.76
+      Top 5 Accuracy: 90.51
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/20200817_001411.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb/slowonly_r50_256p_4x16x1_256e_kinetics400_rgb_20200820-bea7701f.pth
+- Config: configs/recognition/slowonly/slowonly_r50_video_4x16x1_256e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 24
+    Epochs: 256
+    FLOPs: 27430649856
+    Parameters: 32454096
+    Pretrained: None
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 16 GPUs
+  Modality: RGB
+  Name: slowonly_r50_video_4x16x1_256e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 72.9
+      Top 5 Accuracy: 90.82
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb/slowonly_r50_video_320p_4x16x1_256e_kinetics400_rgb_20201014-c9cdc656.pth
+- Config: configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 256
+    FLOPs: 54860480512
+    Parameters: 32454096
+    Pretrained: None
+    Resolution: short-side 256
+    Training Data: Kinetics-400
+    Training Resources: 32 GPUs
+  Modality: RGB
+  Name: slowonly_r50_8x8x1_256e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 74.42
+      Top 5 Accuracy: 91.49
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/20200817_003320.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/20200817_003320.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb/slowonly_r50_256p_8x8x1_256e_kinetics400_rgb_20200820-75851a7d.pth
+- Config: configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 256
+    FLOPs: 27430649856
+    Parameters: 32454096
+    Pretrained: None
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 16 GPUs
+  Modality: RGB
+  Name: slowonly_r50_4x16x1_256e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 73.02
+      Top 5 Accuracy: 90.77
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16_73.02_90.77.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/so_4x16.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb/slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth
+- Config: configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 256
+    FLOPs: 54860480512
+    Parameters: 32454096
+    Pretrained: None
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 24 GPUs
+  Modality: RGB
+  Name: slowonly_r50_8x8x1_256e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 74.93
+      Top 5 Accuracy: 91.92
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/slowonly_r50_8x8_74.93_91.92.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/so_8x8.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb/slowonly_r50_8x8x1_256e_kinetics400_rgb_20200703-a79c555a.pth
+- Config: configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 150
+    FLOPs: 27430649856
+    Parameters: 32454096
+    Pretrained: ImageNet
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 16 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 73.39
+      Top 5 Accuracy: 91.12
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb_20200912-1e8fc736.pth
+- Config: configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 150
+    FLOPs: 54860480512
+    Parameters: 32454096
+    Pretrained: ImageNet
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 32 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 75.55
+      Top 5 Accuracy: 92.04
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912-3f9ce182.pth
+- Config: configs/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 150
+    FLOPs: 38201098240
+    Parameters: 39808464
+    Pretrained: ImageNet
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 16 GPUs
+  Modality: RGB
+  Name: slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 74.54
+      Top 5 Accuracy: 91.73
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/20210305_152630.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/20210305_152630.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb_20210308-0d6e5a69.pth
+- Config: configs/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 150
+    FLOPs: 76401377280
+    Parameters: 39808464
+    Pretrained: ImageNet
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 32 GPUs
+  Modality: RGB
+  Name: slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 76.07
+      Top 5 Accuracy: 92.42
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/20210308_212250.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/20210308_212250.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb_20210308-e8dd9e82.pth
+- Config: configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 24
+    Epochs: 256
+    FLOPs: 27225128960
+    Parameters: 32450960
+    Pretrained: ImageNet
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 16 GPUs
+  Modality: Flow
+  Name: slowonly_r50_4x16x1_256e_kinetics400_flow
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 61.79
+      Top 5 Accuracy: 83.62
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_61.8_83.6.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_61.8_83.6.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow/slowonly_r50_4x16x1_256e_kinetics400_flow_20200704-decb8568.pth
+- Config: configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 12
+    Epochs: 196
+    FLOPs: 54449438720
+    Parameters: 32450960
+    Pretrained: ImageNet
+    Resolution: short-side 320
+    Training Data: Kinetics-400
+    Training Resources: 32 GPUs
+  Modality: Flow
+  Name: slowonly_r50_8x8x1_196e_kinetics400_flow
+  Results:
+  - Dataset: Kinetics-400
+    Metrics:
+      Top 1 Accuracy: 65.76
+      Top 5 Accuracy: 86.25
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_196e_kinetics400_flow_65.8_86.3.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_196e_kinetics400_flow_65.8_86.3.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow/slowonly_r50_8x8x1_256e_kinetics400_flow_20200704-6b384243.pth
+- Config: configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 12
+    Epochs: 256
+    FLOPs: 54860890112
+    Parameters: 32863896
+    Pretrained: None
+    Resolution: short-side 256
+    Training Data: Kinetics-600
+    Training Resources: 32 GPUs
+  Modality: RGB
+  Name: slowonly_r50_video_8x8x1_256e_kinetics600_rgb
+  Results:
+  - Dataset: Kinetics-600
+    Metrics:
+      Top 1 Accuracy: 77.5
+      Top 5 Accuracy: 93.7
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb/slowonly_r50_video_8x8x1_256e_kinetics600_rgb_20201015-81e5153e.pth
+- Config: configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 12
+    Epochs: 256
+    FLOPs: 54861094912
+    Parameters: 33068796
+    Pretrained: None
+    Resolution: short-side 256
+    Training Data: Kinetics-700
+    Training Resources: 32 GPUs
+  Modality: RGB
+  Name: slowonly_r50_video_8x8x1_256e_kinetics700_rgb
+  Results:
+  - Dataset: Kinetics-700
+    Metrics:
+      Top 1 Accuracy: 65.0
+      Top 5 Accuracy: 86.1
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb/slowonly_r50_video_8x8x1_256e_kinetics700_rgb_20201015-9250f662.pth
+- Config: configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 24
+    Epochs: 120
+    FLOPs: 27430649856
+    Parameters: 32454096
+    Pretrained: ImageNet
+    Resolution: short-side 256
+    Training Data: GYM99
+    Training Resources: 16 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb
+  Results:
+  - Dataset: GYM99
+    Metrics:
+      Top 1 Accuracy: 79.3
+      mean Top 1 Accuracy: 70.2
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb_20201111-a9c34b54.pth
+- Config: configs/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 24
+    Epochs: 120
+    FLOPs: 27225128960
+    Parameters: 32450960
+    Pretrained: Kinetics
+    Resolution: short-side 256
+    Training Data: GYM99
+    Training Resources: 16 GPUs
+  Modality: Flow
+  Name: slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow
+  Results:
+  - Dataset: GYM99
+    Metrics:
+      Top 1 Accuracy: 80.3
+      mean Top 1 Accuracy: 71.0
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow_20201111-66ecdb3c.pth
+- Config: configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 64
+    FLOPs: 54859716608
+    Parameters: 31689819
+    Pretrained: ImageNet
+    Resolution: height 100
+    Training Data: Jester
+    Training Resources: 8 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb
+  Results:
+  - Dataset: Jester
+    Metrics:
+      Top 1 Accuracy: 97.2
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb/slowonly_imagenet_pretrained_r50_8x8x1_64e_jester_rgb-b56a5389.pth
+- Config: configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 64
+    FLOPs: 54859765760
+    Parameters: 31738995
+    Pretrained: ImageNet
+    Training Data: HMDB51
+    Training Resources: 8 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb
+  Results:
+  - Dataset: HMDB51
+    Metrics:
+      Top 1 Accuracy: 37.52
+      Top 5 Accuracy: 71.5
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/20210605_185256.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/20210605_185256.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb_20210630-16faeb6a.pth
+- Config: configs/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 40
+    FLOPs: 54859765760
+    Parameters: 31738995
+    Pretrained: Kinetics400
+    Training Data: HMDB51
+    Training Resources: 8 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_8x4x1_64e_hmdb51_rgb
+  Results:
+  - Dataset: HMDB51
+    Metrics:
+      Top 1 Accuracy: 65.95
+      Top 5 Accuracy: 91.05
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/20210606_010153.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/20210606_010153.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb/slowonly_k400_pretrained_r50_8x4x1_40e_hmdb51_rgb_20210630-cee5f725.pth
+- Config: configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 64
+    FLOPs: 54859868160
+    Parameters: 31841445
+    Pretrained: ImageNet
+    Training Data: UCF101
+    Training Resources: 8 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb
+  Results:
+  - Dataset: UCF101
+    Metrics:
+      Top 1 Accuracy: 71.35
+      Top 5 Accuracy: 89.35
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/20210605_213503.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/20210605_213503.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_ucf101_rgb_20210630-181e1661.pth
+- Config: configs/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 40
+    FLOPs: 54859868160
+    Parameters: 31841445
+    Pretrained: Kinetics400
+    Training Data: UCF101
+    Training Resources: 8 GPUs
+  Modality: RGB
+  Name: slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb
+  Results:
+  - Dataset: UCF101
+    Metrics:
+      Top 1 Accuracy: 92.78
+      Top 5 Accuracy: 99.42
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/20210606_010231.log.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/20210606_010231.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb/slowonly_k400_pretrained_r50_8x4x1_40e_ucf101_rgb_20210630-ee8c850f.pth
+- Config: configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.py
+  In Collection: SlowOnly
+  Metadata:
+    Architecture: ResNet50
+    Batch Size: 8
+    Epochs: 64
+    FLOPs: 53907910656
+    Parameters: 31991022
+    Pretrained: ImageNet
+    Training Data: SthV1
+    Training Resources: 8 GPUs
+  Modality: RGB
+  Name: slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb
+  Results:
+  - Dataset: SthV1
+    Metrics:
+      Top 1 Accuracy: 47.76
+      Top 5 Accuracy: 77.49
+    Task: Action Recognition
+  Training Json Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.json
+  Training Log: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb.log
+  Weights: https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb/slowonly_imagenet_pretrained_r50_8x4x1_64e_sthv1_rgb_20211202-d034ff12.pth

configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb.py

+_base_ = [
+    '../../_base_/models/slowonly_r50.py', '../../_base_/default_runtime.py'
+]
+
+# dataset settings
+dataset_type = 'RawframeDataset'
+data_root = 'data/gym/rawframes'
+data_root_val = 'data/gym/rawframes'
+ann_file_train = 'data/gym/annotations/gym99_train_list_rawframes.txt'
+ann_file_val = 'data/gym/annotations/gym99_val_list_rawframes.txt'
+ann_file_test = 'data/gym/annotations/gym99_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=4, frame_interval=16, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=24,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.03, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(policy='step', step=[90, 110])
+total_epochs = 120
+
+# runtime settings
+work_dir = './work_dirs/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb'
+find_unused_parameters = False

configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb.py

+_base_ = [
+    '../../_base_/models/slowonly_r50.py', '../../_base_/default_runtime.py'
+]
+
+# dataset settings
+dataset_type = 'RawframeDataset'
+data_root = 'data/kinetics400/rawframes_train'
+data_root_val = 'data/kinetics400/rawframes_val'
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=4, frame_interval=16, num_clips=1),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=1,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=4,
+        frame_interval=16,
+        num_clips=10,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='ThreeCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=8,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+evaluation = dict(
+    interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.01, momentum=0.9,
+    weight_decay=0.0001)  # this lr is used for 8 gpus
+optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    step=[90, 130],
+    warmup='linear',
+    warmup_by_epoch=True,
+    warmup_iters=10)
+total_epochs = 150
+
+# runtime settings
+checkpoint_config = dict(interval=4)
+work_dir = ('./work_dirs/slowonly_imagenet_pretrained_r50_4x16x1_150e'
+            '_kinetics400_rgb')
+find_unused_parameters = False