Update lambda_resnet26t weights with better set

timm/models/byoanet.py

@@ -58,8 +58,8 @@ default_cfgs = {
         input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
 
     'lambda_resnet26t': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_a2h_256-25ded63d.pth',
-        min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_c_256-e5a5c857.pth',
+        min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
     'lambda_resnet50ts': _cfg(
         url='',
         min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),

timm/models/resnet.py

@@ -51,7 +51,7 @@ default_cfgs = {
         interpolation='bicubic', first_conv='conv1.0'),
     'resnet26t': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet26t_256_ra2-6f6fa748.pth',
-        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)),
+        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
     'resnet50': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth',
         interpolation='bicubic', crop_pct=0.95),