diff --git a/keras_hub/src/models/retinanet/retinanet_object_detector.py b/keras_hub/src/models/retinanet/retinanet_object_detector.py
index 83a6788587..cd80405213 100644
--- a/keras_hub/src/models/retinanet/retinanet_object_detector.py
+++ b/keras_hub/src/models/retinanet/retinanet_object_detector.py
@@ -68,6 +68,10 @@ class for details. If None, a default encoder is created with
             the convolution layers in the prediction heads. Defaults to `False`.
         classification_head_prior_probability: float.  Prior probability for the
             classification head (used for focal loss). Defaults to 0.01.
+        pre_logits_num_conv_layers: int. Number of convolutional layers in the
+            head before the logits layer. These convolutional layers are applied
+            before the final linear layer (logits) that produces the output
+            predictions (bounding box regressions, classification scores).
         preprocessor: Optional. An instance of
             `RetinaNetObjectDetectorPreprocessor`or a custom preprocessor.
             Handles image preprocessing before feeding into the backbone.
@@ -94,6 +98,7 @@ def __init__(
         label_encoder=None,
         use_prediction_head_norm=False,
         classification_head_prior_probability=0.01,
+        pre_logits_num_conv_layers=4,
         preprocessor=None,
         activation=None,
         dtype=None,
@@ -104,9 +109,25 @@ def __init__(
         image_input = keras.layers.Input(backbone.image_shape, name="images")
         head_dtype = dtype or backbone.dtype_policy
 
+        anchor_generator = anchor_generator or AnchorGenerator(
+            bounding_box_format,
+            min_level=backbone.min_level,
+            max_level=backbone.max_level,
+            num_scales=3,
+            aspect_ratios=[0.5, 1.0, 2.0],
+            anchor_size=4,
+        )
+        # As weights are ported from torch they use encoded format
+        # as "center_xywh"
+        label_encoder = label_encoder or RetinaNetLabelEncoder(
+            anchor_generator,
+            bounding_box_format=bounding_box_format,
+            encoding_format="center_xywh",
+        )
+
         box_head = PredictionHead(
             output_filters=anchor_generator.num_base_anchors * 4,
-            num_conv_layers=4,
+            num_conv_layers=pre_logits_num_conv_layers,
             num_filters=256,
             use_group_norm=use_prediction_head_norm,
             use_prior_probability=True,
@@ -116,7 +137,7 @@ def __init__(
         )
         classification_head = PredictionHead(
             output_filters=anchor_generator.num_base_anchors * num_classes,
-            num_conv_layers=4,
+            num_conv_layers=pre_logits_num_conv_layers,
             num_filters=256,
             use_group_norm=use_prediction_head_norm,
             dtype=head_dtype,
@@ -165,23 +186,11 @@ def __init__(
         self.backbone = backbone
         self.preprocessor = preprocessor
         self.activation = activation
+        self.pre_logits_num_conv_layers = pre_logits_num_conv_layers
         self.box_head = box_head
         self.classification_head = classification_head
-        self.anchor_generator = anchor_generator or AnchorGenerator(
-            self.bounding_box_format,
-            min_level=backbone.min_level,
-            max_level=backbone.max_level,
-            num_scales=3,
-            aspect_ratios=[0.5, 1.0, 2.0],
-            anchor_size=4,
-        )
-        # As weights are ported from torch they use encoded format
-        # as "center_xywh"
-        self.label_encoder = label_encoder or RetinaNetLabelEncoder(
-            anchor_generator,
-            bounding_box_format=bounding_box_format,
-            encoding_format="center_xywh",
-        )
+        self.anchor_generator = anchor_generator
+        self.label_encoder = label_encoder
         self._prediction_decoder = prediction_decoder or NonMaxSuppression(
             from_logits=(activation != keras.activations.sigmoid),
             bounding_box_format=bounding_box_format,
@@ -325,6 +334,7 @@ def get_config(self):
             {
                 "num_classes": self.num_classes,
                 "use_prediction_head_norm": self.use_prediction_head_norm,
+                "pre_logits_num_conv_layers": self.pre_logits_num_conv_layers,
                 "bounding_box_format": self.bounding_box_format,
                 "anchor_generator": keras.layers.serialize(
                     self.anchor_generator