Add prelim efficientnet_v2s weights from 224x224 train, eval 83.3 @ 288. Add eca_nfnet_l1 weights, train at 256, eval 84 @ 320.

timm/models/efficientnet.py

@@ -155,7 +155,8 @@ default_cfgs = {
         input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
 
     'efficientnet_v2s': _cfg(
-        url='', input_size=(3, 224, 224), test_size=(3, 300, 300), pool_size=(7, 7)),  # FIXME WIP
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2-b265c1ba.pth',
+        input_size=(3, 224, 224), test_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0),  # FIXME WIP
 
     'tf_efficientnet_b0': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',

timm/models/nfnet.py

@@ -108,7 +108,7 @@ default_cfgs = dict(
         hf_hub='timm/eca_nfnet_l0',
         pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0),
     eca_nfnet_l1=_dcfg(
-        url='',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l1_ra2-7dce93cd.pth',
         pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 320, 320), crop_pct=1.0),
 
     nf_regnet_b0=_dcfg(