Add first 'Normalizer Free' models. nf_regnet_b1 79.3 @ 288x288 test, and nf_resnet50 80.3 @ 256x256 test (80.68 @ 288x288).

Author: rwightman

README.md

@@ -3,6 +3,9 @@
 ## What's New
 
 ### Feb 10, 2021
+* First Normalizer-Free model training experiments done,
+  * nf_resnet50 - 80.68 top-1 @ 288x288, 80.31 @ 256x256
+  * nf_regnet_b1 - 79.30 @ 288x288, 78.75 @ 256x256
 * More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks')
   * GPU-Efficient-Networks (https://github.com/idstcv/GPU-Efficient-Networks), impl in `byobnet.py`
   * RepVGG (https://github.com/DingXiaoH/RepVGG), impl in `byobnet.py`

timm/models/nfnet.py

@@ -34,17 +34,21 @@ def _dcfg(url='', **kwargs):
         **kwargs
     }
 
-# FIXME finish
+
 default_cfgs = {
     'nf_regnet_b0': _dcfg(url=''),
-    'nf_regnet_b1': _dcfg(url='', input_size=(3, 240, 240), pool_size=(8, 8)),
+    'nf_regnet_b1': _dcfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_regnet_b1_256_ra2-ad85cfef.pth',
+        pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288), crop_pct=0.9),
     'nf_regnet_b2': _dcfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
     'nf_regnet_b3': _dcfg(url='', input_size=(3, 272, 272), pool_size=(9, 9)),
     'nf_regnet_b4': _dcfg(url='', input_size=(3, 320, 320), pool_size=(10, 10)),
     'nf_regnet_b5': _dcfg(url='', input_size=(3, 384, 384), pool_size=(12, 12)),
 
     'nf_resnet26': _dcfg(url='', first_conv='stem.conv'),
-    'nf_resnet50': _dcfg(url='', first_conv='stem.conv'),
+    'nf_resnet50': _dcfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_resnet50_ra2-9f236009.pth',
+        first_conv='stem.conv', pool_size=(8, 8), input_size=(3, 256, 256), crop_pct=0.94),
     'nf_resnet101': _dcfg(url='', first_conv='stem.conv'),
 
     'nf_seresnet26': _dcfg(url='', first_conv='stem.conv'),