diff --git a/research/object_detection/g3doc/using_your_own_dataset.md b/research/object_detection/g3doc/using_your_own_dataset.md
index c44acb2bf230dfe6f4cbeb171be518810e432e4b..6192af2dda1320bc7c0961ee7f2c9dc9972148d1 100644
--- a/research/object_detection/g3doc/using_your_own_dataset.md
+++ b/research/object_detection/g3doc/using_your_own_dataset.md
@@ -3,7 +3,7 @@
 [TOC]
 
 To use your own dataset in TensorFlow Object Detection API, you must convert it
-into the [TFRecord file format](https://www.tensorflow.org/tutorials/load_data/tfrecord).
+into the [TFRecord file format](https://www.tensorflow.org/api_guides/python/python_io#tfrecords_format_details).
 This document outlines how to write a script to generate the TFRecord file.
 
 ## Label Maps
diff --git a/research/object_detection/models/ssd_efficientnet_bifpn_feature_extractor.py b/research/object_detection/models/ssd_efficientnet_bifpn_feature_extractor.py
index 3184a1c682dc4df68f31dd8554db846698c667e0..c688bacc3bc18d971de9d9fd0cea4f20051cf2a5 100644
--- a/research/object_detection/models/ssd_efficientnet_bifpn_feature_extractor.py
+++ b/research/object_detection/models/ssd_efficientnet_bifpn_feature_extractor.py
@@ -31,7 +31,10 @@ from object_detection.utils import shape_utils
 from object_detection.utils import tf_version
 # pylint: disable=g-import-not-at-top
 if tf_version.is_tf2():
-  from official.vision.image_classification.efficientnet import efficientnet_model
+  try:
+    from official.legacy.image_classification.efficientnet import efficientnet_model
+  except ModuleNotFoundError:
+    from official.vision.image_classification.efficientnet import efficientnet_model
 
 _EFFICIENTNET_LEVEL_ENDPOINTS = {
     1: 'stack_0/block_0/project_bn',