@@ -217,7 +217,7 @@ def _cfg(url='', **kwargs):
217217 interpolation = 'bicubic' , first_conv = 'conv1.0' , input_size = (3 , 256 , 256 ), crop_pct = 0.94 , pool_size = (8 , 8 )),
218218 'ecaresnet269d' : _cfg (
219219 url = 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet269d_320_ra2-7baa55cb.pth' ,
220- interpolation = 'bicubic' , first_conv = 'conv1.0' , input_size = (3 , 320 , 320 ), pool_size = (8 , 8 ),
220+ interpolation = 'bicubic' , first_conv = 'conv1.0' , input_size = (3 , 320 , 320 ), pool_size = (10 , 10 ),
221221 crop_pct = 1.0 , test_input_size = (3 , 352 , 352 )),
222222
223223 # Efficient Channel Attention ResNeXts
@@ -1029,14 +1029,6 @@ def swsl_resnext101_32x16d(pretrained=True, **kwargs):
10291029 return _create_resnet ('swsl_resnext101_32x16d' , pretrained , ** model_args )
10301030
10311031
1032- @register_model
1033- def ecaresnet18 (pretrained = False , ** kwargs ):
1034- """ Constructs an ECA-ResNet-18 model.
1035- """
1036- model_args = dict (block = BasicBlock , layers = [2 , 2 , 2 , 2 ], block_args = dict (attn_layer = 'eca' ), ** kwargs )
1037- return _create_resnet ('ecaresnet18' , pretrained , ** model_args )
1038-
1039-
10401032@register_model
10411033def ecaresnet26t (pretrained = False , ** kwargs ):
10421034 """Constructs an ECA-ResNeXt-26-T model.
@@ -1049,14 +1041,6 @@ def ecaresnet26t(pretrained=False, **kwargs):
10491041 return _create_resnet ('ecaresnet26t' , pretrained , ** model_args )
10501042
10511043
1052- @register_model
1053- def ecaresnet50 (pretrained = False , ** kwargs ):
1054- """Constructs an ECA-ResNet-50 model.
1055- """
1056- model_args = dict (block = Bottleneck , layers = [3 , 4 , 6 , 3 ], block_args = dict (attn_layer = 'eca' ), ** kwargs )
1057- return _create_resnet ('ecaresnet50' , pretrained , ** model_args )
1058-
1059-
10601044@register_model
10611045def ecaresnet50d (pretrained = False , ** kwargs ):
10621046 """Constructs a ResNet-50-D model with eca.
0 commit comments