| _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 | |
| }) | |