diff --git a/timm/models/resnet.py b/timm/models/resnet.py
index 9e05f278..18952980 100644
--- a/timm/models/resnet.py
+++ b/timm/models/resnet.py
@@ -3,7 +3,7 @@
 This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
 additional dropout and dynamic global avg/max pool.
 
-ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants added by Ross Wightman
+ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
 """
 import math
 
@@ -42,7 +42,7 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth',
         interpolation='bicubic'),
     'resnet50': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/rw_resnet50-86acaeed.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_am-6c502b37.pth',
         interpolation='bicubic'),
     'resnet50d': _cfg(
         url='',
@@ -243,13 +243,14 @@ class ResNet(nn.Module):
     variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the
     'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default.
 
-    ResNet variants:
+    ResNet variants (the same modifications can be used in SE/ResNeXt models as well):
       * normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b
       * c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64)
       * d - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64), average pool in downsample
       * e - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128), average pool in downsample
       * s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128)
       * t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample
+      * tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample
 
     ResNeXt
       * normal - 7x7 stem, stem_width = 64, standard cardinality and base widths
@@ -285,6 +286,7 @@ class ResNet(nn.Module):
           * '', default - a single 7x7 conv with a width of stem_width
           * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
           * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width//4 * 6, stem_width * 2
+          * 'deep_tiered_narrow' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
     block_reduce_first: int, default 1
         Reduction factor for first convolution output width of residual blocks,
         1 for all archs except senets, where 2