添加mmclassification-0.24.1代码，删除mmclassification-speed-benchmark

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/conformer/tiny-p16.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='Conformer', arch='tiny', drop_path_rate=0.1, init_cfg=None),
+    neck=None,
+    head=dict(
+        type='ConformerHead',
+        num_classes=1000,
+        in_channels=[256, 384],
+        init_cfg=None,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convmixer/convmixer-1024-20.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='ConvMixer', arch='1024/20'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convmixer/convmixer-1536-20.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='ConvMixer', arch='1536/20'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convmixer/convmixer-768-32.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='ConvMixer', arch='768/32', act_cfg=dict(type='ReLU')),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convnext/convnext-base.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ConvNeXt',
+        arch='base',
+        out_indices=(3, ),
+        drop_path_rate=0.5,
+        gap_before_final_norm=True,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+        ]),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convnext/convnext-large.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ConvNeXt',
+        arch='large',
+        out_indices=(3, ),
+        drop_path_rate=0.5,
+        gap_before_final_norm=True,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+        ]),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convnext/convnext-small.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ConvNeXt',
+        arch='small',
+        out_indices=(3, ),
+        drop_path_rate=0.4,
+        gap_before_final_norm=True,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+        ]),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convnext/convnext-tiny.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ConvNeXt',
+        arch='tiny',
+        out_indices=(3, ),
+        drop_path_rate=0.1,
+        gap_before_final_norm=True,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+        ]),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/convnext/convnext-xlarge.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ConvNeXt',
+        arch='xlarge',
+        out_indices=(3, ),
+        drop_path_rate=0.5,
+        gap_before_final_norm=True,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+        ]),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/densenet/densenet121.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='DenseNet', arch='121'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/densenet/densenet161.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='DenseNet', arch='161'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2208,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/densenet/densenet169.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='DenseNet', arch='169'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1664,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/densenet/densenet201.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='DenseNet', arch='201'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1920,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/efficientnet_b0.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='EfficientNet', arch='b0'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1280,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/efficientnet_b1.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='EfficientNet', arch='b1'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1280,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/efficientnet_b2.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='EfficientNet', arch='b2'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1408,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/efficientnet_b3.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='EfficientNet', arch='b3'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/efficientnet_b4.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='EfficientNet', arch='b4'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1792,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/efficientnet_b5.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='EfficientNet', arch='b5'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/efficientnet_b6.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='EfficientNet', arch='b6'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2304,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))