diff --git a/keras_cv/models/object_detection/retinanet/retinanet_label_encoder.py b/keras_cv/models/object_detection/retinanet/retinanet_label_encoder.py
index c2410d2ac1..4142f7bac5 100644
--- a/keras_cv/models/object_detection/retinanet/retinanet_label_encoder.py
+++ b/keras_cv/models/object_detection/retinanet/retinanet_label_encoder.py
@@ -124,7 +124,7 @@ def _encode_sample(self, box_labels, anchor_boxes, image_shape):
             gt_boxes, matched_gt_idx
         )
         matched_gt_boxes = ops.reshape(
-            matched_gt_boxes, (-1, matched_gt_boxes.shape[1], 4)
+            matched_gt_boxes, (-1, ops.shape(matched_gt_boxes)[1], 4)
         )
 
         box_target = bounding_box._encode_box_to_deltas(
@@ -166,7 +166,7 @@ def _encode_sample(self, box_labels, anchor_boxes, image_shape):
         box_shape = ops.shape(gt_boxes)
         batch_size = box_shape[0]
         n_boxes = box_shape[1]
-        box_ids = ops.arange(gt_boxes.shape[1], dtype=matched_gt_idx.dtype)
+        box_ids = ops.arange(n_boxes, dtype=matched_gt_idx.dtype)
         matched_ids = ops.expand_dims(matched_gt_idx, axis=-1)
         matches = box_ids == matched_ids
         matches = ops.any(matches, axis=1)
@@ -197,7 +197,8 @@ def call(self, images, box_labels):
                 f"Received `type(images)={type(images)}`."
             )
 
-        image_shape = tuple(images[0].shape)
+        image_shape = ops.shape(images)
+        image_shape = (image_shape[1], image_shape[2], image_shape[3])
         box_labels = bounding_box.to_dense(box_labels)
         if len(box_labels["classes"].shape) == 2:
             box_labels["classes"] = ops.expand_dims(
@@ -215,7 +216,7 @@ def call(self, images, box_labels):
         result = self._encode_sample(box_labels, anchor_boxes, image_shape)
         encoded_box_targets = result["boxes"]
         encoded_box_targets = ops.reshape(
-            encoded_box_targets, (-1, encoded_box_targets.shape[1], 4)
+            encoded_box_targets, (-1, ops.shape(encoded_box_targets)[1], 4)
         )
         class_targets = result["classes"]
         return encoded_box_targets, class_targets
diff --git a/keras_cv/models/object_detection/retinanet/retinanet_test.py b/keras_cv/models/object_detection/retinanet/retinanet_test.py
index cc0f7c9131..45026262f4 100644
--- a/keras_cv/models/object_detection/retinanet/retinanet_test.py
+++ b/keras_cv/models/object_detection/retinanet/retinanet_test.py
@@ -20,6 +20,7 @@
 from absl.testing import parameterized
 
 import keras_cv
+from keras_cv import backend
 from keras_cv.backend import keras
 from keras_cv.backend import ops
 from keras_cv.models.backbones.test_backbone_presets import (
@@ -249,6 +250,49 @@ def test_call_with_custom_label_encoder(self):
         )
         model(ops.ones(shape=(2, 224, 224, 3)))
 
+    def test_tf_dataset_data_generator(self):
+        if backend.multi_backend() and keras.backend.backend() != "tensorflow":
+            pytest.skip("TensorFlow required for `tf.data.Dataset` test.")
+
+        def data_generator():
+            image = tf.ones((512, 512, 3), dtype=tf.float32)
+
+            bounding_boxes = {
+                "boxes": tf.ones((3, 4), dtype=tf.float32),
+                "classes": tf.ones((3,), dtype=tf.float32),
+            }
+
+            yield {"images": image, "bounding_boxes": bounding_boxes}
+
+        data = tf.data.Dataset.from_generator(
+            generator=data_generator,
+            output_signature={
+                "images": tf.TensorSpec(shape=(512, 512, 3), dtype=tf.float32),
+                "bounding_boxes": {
+                    "boxes": tf.TensorSpec(shape=(None, 4), dtype=tf.float32),
+                    "classes": tf.TensorSpec(shape=(None,), dtype=tf.float32),
+                },
+            },
+        ).batch(1)
+
+        model = keras_cv.models.RetinaNet(
+            num_classes=2,
+            bounding_box_format="xyxy",
+            backbone=keras_cv.models.ResNet50Backbone.from_preset(
+                "resnet50_imagenet",
+                load_weights=False,
+            ),
+        )
+
+        model.compile(
+            classification_loss="focal",
+            box_loss="smoothl1",
+            optimizer="adam",
+            jit_compile=False,
+        )
+
+        model.fit(data, epochs=1, batch_size=1, steps_per_epoch=1)
+
 
 @pytest.mark.large
 class RetinaNetSmokeTest(TestCase):