diff --git a/research/object_detection/model_lib.py b/research/object_detection/model_lib.py
index 5ba6f9b6..2239bb8b 100644
--- a/research/object_detection/model_lib.py
+++ b/research/object_detection/model_lib.py
@@ -411,7 +411,7 @@ def tpu_scaffold():
 
       # Eval metrics on a single example.
       eval_metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
-          eval_config, category_index.values(), eval_dict)
+          eval_config, list(category_index.values()), eval_dict)
       for loss_key, loss_tensor in iter(losses_dict.items()):
         eval_metric_ops[loss_key] = tf.metrics.mean(loss_tensor)
       for var in optimizer_summary_vars:
diff --git a/research/object_detection/utils/object_detection_evaluation.py b/research/object_detection/utils/object_detection_evaluation.py
index 5826c581..1c155c2d 100644
--- a/research/object_detection/utils/object_detection_evaluation.py
+++ b/research/object_detection/utils/object_detection_evaluation.py
@@ -304,7 +304,7 @@ def evaluate(self):
       if idx + self._label_id_offset in category_index:
         category_name = category_index[idx + self._label_id_offset]['name']
         try:
-          category_name = unicode(category_name, 'utf-8')
+          category_name = str(category_name, 'utf-8')
         except TypeError:
           pass
         category_name = unicodedata.normalize(
@@ -795,8 +795,7 @@ def add_single_detected_image_info(self, image_key, detected_boxes,
       if scores[i].shape[0] > 0:
         self.scores_per_class[i].append(scores[i])
         self.tp_fp_labels_per_class[i].append(tp_fp_labels[i])
-    (self.num_images_correctly_detected_per_class
-    ) += is_class_correctly_detected_in_image
+    self.num_images_correctly_detected_per_class += is_class_correctly_detected_in_image
 
   def _update_ground_truth_statistics(self, groundtruth_class_labels,
                                       groundtruth_is_difficult_list,