diff --git a/classy_vision/dataset/transforms/__init__.py b/classy_vision/dataset/transforms/__init__.py
index 2c2d0d34c7..5b6e50380e 100644
--- a/classy_vision/dataset/transforms/__init__.py
+++ b/classy_vision/dataset/transforms/__init__.py
@@ -12,6 +12,7 @@
 import torchvision.transforms as transforms
 import torchvision.transforms._transforms_video as transforms_video
 from classy_vision.generic.registry_utils import import_all_modules
+from classy_vision.generic.util import log_class_usage
 
 from .classy_transform import ClassyTransform
 
@@ -49,20 +50,23 @@ def build_transform(transform_config: Dict[str, Any]) -> Callable:
     transform_args = copy.deepcopy(transform_config)
     del transform_args["name"]
     if name in TRANSFORM_REGISTRY:
-        return TRANSFORM_REGISTRY[name].from_config(transform_args)
-    # the name should be available in torchvision.transforms
-    # if users specify the torchvision transform name in snake case,
-    # we need to convert it to title case.
-    if not (hasattr(transforms, name) or hasattr(transforms_video, name)):
-        name = name.title().replace("_", "")
-    assert hasattr(transforms, name) or hasattr(transforms_video, name), (
-        f"{name} isn't a registered tranform"
-        ", nor is it available in torchvision.transforms"
-    )
-    if hasattr(transforms, name):
-        return getattr(transforms, name)(**transform_args)
+        transform = TRANSFORM_REGISTRY[name].from_config(transform_args)
     else:
-        return getattr(transforms_video, name)(**transform_args)
+        # the name should be available in torchvision.transforms
+        # if users specify the torchvision transform name in snake case,
+        # we need to convert it to title case.
+        if not (hasattr(transforms, name) or hasattr(transforms_video, name)):
+            name = name.title().replace("_", "")
+        assert hasattr(transforms, name) or hasattr(transforms_video, name), (
+            f"{name} isn't a registered tranform"
+            ", nor is it available in torchvision.transforms"
+        )
+        if hasattr(transforms, name):
+            transform = getattr(transforms, name)(**transform_args)
+        else:
+            transform = getattr(transforms_video, name)(**transform_args)
+    log_class_usage("Transform", transform.__class__)
+    return transform
 
 
 def build_transforms(transforms_config: List[Dict[str, Any]]) -> Callable: