Add native PyTorch weights for EfficientNet-B0 w/ top-1 > 76.9

* also add pooling details to default cfg for efficiennets so testtimepool wrapper works

README.md

@@ -67,6 +67,7 @@ I've leveraged the training scripts in this repository to train a few of the mod
 |---|---|---|---|---|
 | resnext50_32x4d | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic |
 | seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
+| efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.29M | bicubic |
 | mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic |
 | fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear |
 | resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear |

models/gen_efficientnet.py

@@ -74,23 +74,29 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth'),
     'spnasnet_100': _cfg(
         url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1'),
-    'efficientnet_b0': _cfg(url=''),
-    'efficientnet_b1': _cfg(url='', input_size=(3, 240, 240)),
-    'efficientnet_b2': _cfg(url='', input_size=(3, 260, 260)),
-    'efficientnet_b3': _cfg(url='', input_size=(3, 300, 300)),
-    'efficientnet_b4': _cfg(url='', input_size=(3, 380, 380)),
+    'efficientnet_b0': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth',
+        interpolation='bicubic'),
+    'efficientnet_b1': _cfg(
+        url='', input_size=(3, 240, 240), pool_size=(8, 8)),
+    'efficientnet_b2': _cfg(
+        url='', input_size=(3, 260, 260), pool_size=(9, 9)),
+    'efficientnet_b3': _cfg(
+        url='', input_size=(3, 300, 300), pool_size=(10, 10)),
+    'efficientnet_b4': _cfg(
+        url='', input_size=(3, 380, 380), pool_size=(12, 12)),
     'tf_efficientnet_b0': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0-0af12548.pth',
         input_size=(3, 224, 224), interpolation='bicubic'),
     'tf_efficientnet_b1': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1-5c1377c4.pth',
-        input_size=(3, 240, 240), interpolation='bicubic', crop_pct=0.882),
+        input_size=(3, 240, 240), pool_size=(8, 8), interpolation='bicubic', crop_pct=0.882),
     'tf_efficientnet_b2': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2-e393ef04.pth',
-        input_size=(3, 260, 260), interpolation='bicubic', crop_pct=0.890),
+        input_size=(3, 260, 260), pool_size=(9, 9), interpolation='bicubic', crop_pct=0.890),
     'tf_efficientnet_b3': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3-e3bd6955.pth',
-        input_size=(3, 300, 300), interpolation='bicubic', crop_pct=0.904),
+        input_size=(3, 300, 300), pool_size=(10, 10), interpolation='bicubic', crop_pct=0.904),
 }
 
 _DEBUG = False

models/test_time_pool.py

@@ -31,7 +31,7 @@ def apply_test_time_pool(model, config, args):
     if not args.no_test_pool and \
             config['input_size'][-1] > model.default_cfg['input_size'][-1] and \
             config['input_size'][-2] > model.default_cfg['input_size'][-2]:
-        print('Target input size (%s) > pretrained default (%s), using test time pooling' %
+        print('Target input size %s > pretrained default %s, using test time pooling' %
               (str(config['input_size'][-2:]), str(model.default_cfg['input_size'][-2:])))
         model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
         test_time_pool = True