diff --git a/paddle/fluid/operators/detection/multiclass_nms_op.cc b/paddle/fluid/operators/detection/multiclass_nms_op.cc
index 265bfc6c75c273416e9fa9c5a803118c7a759e8d..f357e3ccf905309e6656f3fa87fbee45dc357c1e 100644
--- a/paddle/fluid/operators/detection/multiclass_nms_op.cc
+++ b/paddle/fluid/operators/detection/multiclass_nms_op.cc
@@ -520,7 +520,7 @@ independently for each class. The outputs is a 2-D LoDTenosr, for each
 image, the offsets in first dimension of LoDTensor are called LoD, the number
 of offset is N + 1, where N is the batch size. If LoD[i + 1] - LoD[i] == 0,
 means there is no detected bbox for this image. If there is no detected boxes
-for all images, all the elements in LoD are set to {0,1}, and the Out only 
+for all images, all the elements in LoD are set to {1}, and the Out only 
 contains one value which is -1.
 )DOC");
   }
diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py
index 4ee0cce62a60e7e7eb404592d7b88226b9948921..7cf575d2539ce770f50411048f8ba948809b3c31 100644
--- a/python/paddle/fluid/layers/detection.py
+++ b/python/paddle/fluid/layers/detection.py
@@ -263,8 +263,10 @@ def detection_output(loc,
             number is N + 1, N is the batch size. The i-th image has
             `LoD[i + 1] - LoD[i]` detected results, if it is 0, the i-th image
             has no detected results. If all images have not detected results,
-            all the elements in LoD are 0, and output tensor only contains one
+            LoD will be set to {1}, and output tensor only contains one
             value, which is -1.
+            (After version 1.3, when no boxes detected, the lod is changed
+             from {0} to {1}.)
 
     Examples:
         .. code-block:: python
@@ -1967,8 +1969,8 @@ def multiclass_nms(bboxes,
                    scores,
                    score_threshold,
                    nms_top_k,
-                   nms_threshold,
                    keep_top_k,
+                   nms_threshold=0.3,
                    normalized=True,
                    nms_eta=1.,
                    background_label=0,
@@ -2035,8 +2037,10 @@ def multiclass_nms(bboxes,
              Each row has 10 values: 
              [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the 
              total number of detections. If there is no detected boxes for all
-             images, lod will be set to {0, 1} and Out only contains one value
-             which is -1. 
+             images, lod will be set to {1} and Out only contains one value
+             which is -1.
+             (After version 1.3, when no boxes detected, the lod is changed 
+             from {0} to {1}) 
 
     Examples:
         .. code-block:: python
diff --git a/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py b/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
index 2a50e0bd856004a46dae01652d8507e88beaa884..8fc391a1ff2529460b038979c0c7d0a9d905a7e0 100644
--- a/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
+++ b/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
@@ -19,7 +19,7 @@ import copy
 from op_test import OpTest
 
 
-def iou(box_a, box_b, normalized):
+def iou(box_a, box_b, norm):
     """Apply intersection-over-union overlap between box_a and box_b
     """
     xmin_a = min(box_a[0], box_a[2])
@@ -32,10 +32,10 @@ def iou(box_a, box_b, normalized):
     xmax_b = max(box_b[0], box_b[2])
     ymax_b = max(box_b[1], box_b[3])
 
-    area_a = (ymax_a - ymin_a + (normalized == False)) * \
-             (xmax_a - xmin_a + (normalized == False))
-    area_b = (ymax_b - ymin_b + (normalized == False)) * \
-             (xmax_b - xmin_b + (normalized == False))
+    area_a = (ymax_a - ymin_a + (norm == False)) * (xmax_a - xmin_a +
+                                                    (norm == False))
+    area_b = (ymax_b - ymin_b + (norm == False)) * (xmax_b - xmin_b +
+                                                    (norm == False))
     if area_a <= 0 and area_b <= 0:
         return 0.0
 
@@ -44,8 +44,8 @@ def iou(box_a, box_b, normalized):
     xb = min(xmax_a, xmax_b)
     yb = min(ymax_a, ymax_b)
 
-    inter_area = max(xb - xa + (normalized == False), 0.0) * \
-                 max(yb - ya + (normalized == False), 0.0)
+    inter_area = max(xb - xa + (norm == False),
+                     0.0) * max(yb - ya + (norm == False), 0.0)
 
     iou_ratio = inter_area / (area_a + area_b - inter_area)
 
@@ -210,7 +210,6 @@ def batched_multiclass_nms(boxes,
             normalized,
             shared=True)
         if nmsed_num == 0:
-            #    lod.append(1)
             continue
 
         lod.append(nmsed_num)