Add Res2Net and DLA to README

README.md

@@ -25,6 +25,10 @@ I've included a few of my favourite models, but this is not an exhaustive collec
     * ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, ResNeXt50 (32x4d), ResNeXt101 (32x4d and 64x4d)
     * 'Bag of Tricks' / Gluon C, D, E, S variations (https://arxiv.org/abs/1812.01187)
     * Instagram trained / ImageNet tuned ResNeXt101-32x8d to 32x48d from from [facebookresearch](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/)
+    * Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169)
+* DLA
+    * Original (https://github.com/ucbdrive/dla, https://arxiv.org/abs/1707.06484)
+    * Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169)
 * DenseNet (from [torchvision](https://github.com/pytorch/vision/tree/master/torchvision/models))
     * DenseNet-121, DenseNet-169, DenseNet-201, DenseNet-161
 * Squeeze-and-Excitation ResNet/ResNeXt (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) with some pretrained weight additions by myself)

timm/models/res2net.py

@@ -119,17 +119,6 @@ class Bottle2neck(nn.Module):
         return out
 
 
-
-@register_model
-def res2net50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
-    """Constructs a Res2Net-50 model.
-    Res2Net-50 refers to the Res2Net-50_26w_4s.
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    return res2net50_26w_4s(pretrained, num_classes, in_chans, **kwargs)
-
-
 @register_model
 def res2net50_26w_4s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """Constructs a Res2Net-50_26w_4s model.