Add EfficientNet-B3 weights, trained from scratch with RA.

README.md

@@ -116,6 +116,9 @@ 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  | Image Size |
 |---|---|---|---|---|---|
+| efficientnet_b3a | 81.874 (18.126) | 95.840 (4.160) | 9.11M | bicubic | 320 (1.0 crop) |
+| efficientnet_b3 | 81.498 (18.502) | 95.718 (4.282) | 9.11M | bicubic | 300 |
+| efficientnet_b2a | 80.608 (19.392) | 95.310 (4.690) | 9.11M | bicubic | 288 (1.0 crop) |
 | mixnet_xl | 80.478 (19.522) | 94.932 (5.068) | 11.90M | bicubic | 224 |
 | efficientnet_b2 | 80.402 (19.598) | 95.076 (4.924) | 9.11M | bicubic | 260 |
 | resnext50d_32x4d | 79.674 (20.326) | 94.868 (5.132) | 25.1M | bicubic | 224 |

timm/models/efficientnet.py

@@ -72,8 +72,15 @@ default_cfgs = {
     'efficientnet_b2': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
         input_size=(3, 260, 260), pool_size=(9, 9)),
+    'efficientnet_b2a': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
+        input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0),
     'efficientnet_b3': _cfg(
-        url='', input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra-a5e2fbc7.pth',
+        input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
+    'efficientnet_b3a': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra-a5e2fbc7.pth',
+        input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0),
     'efficientnet_b4': _cfg(
         url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
     'efficientnet_b5': _cfg(
@@ -855,6 +862,15 @@ def efficientnet_b2(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def efficientnet_b2a(pretrained=False, **kwargs):
+    """ EfficientNet-B2 @ 288x288 w/ 1.0 test crop"""
+    # NOTE for train, drop_rate should be 0.3, drop_connect_rate should be 0.2
+    model = _gen_efficientnet(
+        'efficientnet_b2a', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
+    return model
+
+
 @register_model
 def efficientnet_b3(pretrained=False, **kwargs):
     """ EfficientNet-B3 """
@@ -864,6 +880,15 @@ def efficientnet_b3(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def efficientnet_b3a(pretrained=False, **kwargs):
+    """ EfficientNet-B3 @ 320x320 w/ 1.0 test crop-pct """
+    # NOTE for train, drop_rate should be 0.3, drop_connect_rate should be 0.2
+    model = _gen_efficientnet(
+        'efficientnet_b3a', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
+    return model
+
+
 @register_model
 def efficientnet_b4(pretrained=False, **kwargs):
     """ EfficientNet-B4 """