diff --git a/transforms.py b/transforms.py
index 7828557..0b1ae97 100644
--- a/transforms.py
+++ b/transforms.py
@@ -139,25 +139,25 @@ def detection_ssdlite_pipeline_builder(*, input_type, api_version):
 
     pipeline = []
     if api_version == "v1":
-        pipeline.append(DetectionReferenceConvertCocoPolysToMaskV1())
+        pipeline.append(ConvertCocoPolysToMaskV1())
 
         if input_type == "Tensor":
-            pipeline.append(DetectionReferencePILToTensorV1())
+            pipeline.append(PILToTensorV1())
 
         pipeline.extend(
             [
-                DetectionReferenceRandomIoUCropV1(),
-                DetectionReferenceRandomHorizontalFlipV1(p=0.5),
+                RandomIoUCropV1(),
+                RandomHorizontalFlipV1(p=0.5),
             ]
         )
 
         if input_type == "PIL":
-            pipeline.append(DetectionReferencePILToTensorV1())
+            pipeline.append(PILToTensorV1())
 
-        pipeline.append(DetectionReferenceConvertImageDtypeV1(torch.float))
+        pipeline.append(ConvertImageDtypeV1(torch.float))
 
     elif api_version == "v2":
-        pipeline.append(WrapCocoDetectionReferenceSampleForTransformsV2())
+        pipeline.append(WrapCocoSampleForTransformsV2())
 
         if input_type == "Tensor":
             pipeline.append(transforms_v2.PILToTensor())
@@ -197,7 +197,7 @@ def _transform(self, inpt, params):
         return F_v2.crop(inpt, **params)
 
 
-class WrapCocoDetectionReferenceSampleForTransformsV2:
+class WrapCocoSampleForTransformsV2:
     def __init__(self):
         num_samples = 117_266
         wrapper_factory = WRAPPER_FACTORIES[datasets.CocoDetection]
@@ -237,7 +237,7 @@ def convert_coco_poly_to_mask(segmentations, height, width):
     return masks
 
 
-class DetectionReferenceConvertCocoPolysToMaskV1:
+class ConvertCocoPolysToMaskV1:
     def __call__(self, image, target):
         w, h = image.size
 
@@ -296,7 +296,7 @@ def __call__(self, image, target):
         return image, target
 
 
-class DetectionReferenceRandomHorizontalFlipV1(transforms_v1.RandomHorizontalFlip):
+class RandomHorizontalFlipV1(transforms_v1.RandomHorizontalFlip):
     def forward(
         self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
     ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
@@ -314,7 +314,7 @@ def forward(
         return image, target
 
 
-class DetectionReferencePILToTensorV1(nn.Module):
+class PILToTensorV1(nn.Module):
     def forward(
         self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
     ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
@@ -322,7 +322,7 @@ def forward(
         return image, target
 
 
-class DetectionReferenceConvertImageDtypeV1(nn.Module):
+class ConvertImageDtypeV1(nn.Module):
     def __init__(self, dtype: torch.dtype) -> None:
         super().__init__()
         self.dtype = dtype
@@ -334,7 +334,7 @@ def forward(
         return image, target
 
 
-class DetectionReferenceRandomIoUCropV1(nn.Module):
+class RandomIoUCropV1(nn.Module):
     def __init__(
         self,
         min_scale: float = 0.3,