Added comment

model/reverse_perspective.py

@@ -24,7 +24,7 @@ class PerspectiveEstimator(nn.Module):
     Perspective estimator submodule of the wider reverse-perspective network.
 
     Input: Pre-processed, uniformly-sized image data
-    Output: Perspective factor
+    Output: Perspective factor :math:`\\in \\mathbb{R}`
 
     **Note**
     --------

model/transcrowd_gap.py

@@ -21,6 +21,13 @@ from timm.models.registry import register_model
 from timm.models.layers import trunc_normal_
 
 class VisionTransformer_GAP(VisionTransformer):
+    # [XXX] It might be a bad idea to use vision transformer for small datasets.
+    # ref: ViT paper -- "transformers lack some of the inductive biases inherent
+    # to CNNs, such as translation equivariance and locality".
+    # convolution is specifically equivariant in translation (linear and
+    # shift-equivariant), specifically.
+    # tl;dr: CNNs might perform better for small datasets. Not sure abt performance.
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         num_patches = self.patch_embed.num_patches