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- _base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py' # noqa
- # TODO: delete custom_imports after mmcls supports auto import
- # please install mmcls>=1.0
- # import mmcls.models to trigger register_module in mmcls
- custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
- checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth' # noqa
- model = dict(
- backbone=dict(
- _delete_=True,
- type='mmcls.ConvNeXt',
- arch='small',
- out_indices=[0, 1, 2, 3],
- drop_path_rate=0.6,
- layer_scale_init_value=1.0,
- gap_before_final_norm=False,
- init_cfg=dict(
- type='Pretrained', checkpoint=checkpoint_file,
- prefix='backbone.')))
- optim_wrapper = dict(paramwise_cfg={
- 'decay_rate': 0.7,
- 'decay_type': 'layer_wise',
- 'num_layers': 12
- })
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