Update model config and README

README.md

@@ -19,9 +19,9 @@ A collection of hparams (timm .yaml config files) for this training series can b
 - **Model Type:** Image classification / feature backbone
 - **Model Stats:**
   - Params (M): 21.8
-  - GMACs: 10.5
-  - Activations (M): 12.1
-  - Image size: train = 384 x 348, test = 448 x 448
+  - GMACs: 11.5
+  - Activations (M): 13.3
+  - Image size: train = 384 x 384, test = 448 x 448
 - **Dataset:** ImageNet-1k
 - **Papers:**
   - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
@@ -78,11 +78,11 @@ output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batc
 for o in output:
     # print shape of each feature map in output
     # e.g.:
-    #  torch.Size([1, 64, 192, 174])
-    #  torch.Size([1, 64, 96, 87])
-    #  torch.Size([1, 128, 48, 44])
-    #  torch.Size([1, 256, 24, 22])
-    #  torch.Size([1, 512, 12, 11])
+    #  torch.Size([1, 64, 192, 192])
+    #  torch.Size([1, 64, 96, 96])
+    #  torch.Size([1, 128, 48, 48])
+    #  torch.Size([1, 256, 24, 24])
+    #  torch.Size([1, 512, 12, 12])
 
     print(o.shape)
 ```
@@ -113,7 +113,7 @@ output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_featu
 # or equivalently (without needing to set num_classes=0)
 
 output = model.forward_features(transforms(img).unsqueeze(0))
-# output is unpooled, a (1, 512, 12, 11) shaped tensor
+# output is unpooled, a (1, 512, 12, 12) shaped tensor
 
 output = model.forward_head(output, pre_logits=True)
 # output is a (1, num_features) shaped tensor

config.json

@@ -8,7 +8,7 @@
     "input_size": [
       3,
       384,
-      348
+      384
     ],
     "test_input_size": [
       3,