Replace ResNet-34 default weights with a great result from my experiments.

README.md

@@ -64,12 +64,13 @@ I've leveraged the training scripts in this repository to train a few of the mod
 
 |Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling  |
 |---|---|---|---|---|
-| ResNeXt-50 (32x4d) | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic |
+| resnext50_32x4d | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic |
-| SE-ResNeXt-26 (32x4d) | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
+| seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
-| SE-ResNet-34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear |
+| resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear |
-| SE-ResNet-18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic |
+| fbnetc_100 | 74.830 (25.170 | 92.124 (7.876) | 5.6M | bilinear |
-| FBNet-C | 74.830 (25.170 | 92.124 (7.876) | 5.6M | bilinear |
+| seresnet34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear |
-| Single-Path NASNet 1.00 | 74.084 (25.916)  | 91.818 (8.182) | 4.42M | bilinear |
+| spnasnet_100 | 74.084 (25.916)  | 91.818 (8.182) | 4.42M | bilinear |
+| seresnet18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic |
 
 ### Ported Weights
 

models/resnet.py

@@ -30,7 +30,8 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = {
     'resnet18': _cfg(url='https://download.pytorch.org/models/resnet18-5c106cde.pth'),
-    'resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'),
+    'resnet34': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth'),
     'resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'),
     'resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'),
     'resnet152': _cfg(url='https://download.pytorch.org/models/resnet152-b121ed2d.pth'),