diff --git a/model_zoo/official/cv/ssd/postprocess.py b/model_zoo/official/cv/ssd/postprocess.py
index 3e0212ede9..05c570817f 100644
--- a/model_zoo/official/cv/ssd/postprocess.py
+++ b/model_zoo/official/cv/ssd/postprocess.py
@@ -25,6 +25,7 @@ batch_size = 1
 parser = argparse.ArgumentParser(description="ssd_mobilenet_v1_fpn inference")
 parser.add_argument("--result_path", type=str, required=True, help="result files path.")
 parser.add_argument("--img_path", type=str, required=True, help="image file path.")
+parser.add_argument("--drop", action="store_true", help="drop iscrowd images or not.")
 args = parser.parse_args()
 
 def get_imgSize(file_name):
@@ -33,15 +34,43 @@ def get_imgSize(file_name):
 
 def get_result(result_path, img_id_file_path):
     anno_json = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
+
+    if args.drop:
+        from pycocotools.coco import COCO
+        train_cls = config.classes
+        train_cls_dict = {}
+        for i, cls in enumerate(train_cls):
+            train_cls_dict[cls] = i
+        coco = COCO(anno_json)
+        classs_dict = {}
+        cat_ids = coco.loadCats(coco.getCatIds())
+        for cat in cat_ids:
+            classs_dict[cat["id"]] = cat["name"]
+
     files = os.listdir(img_id_file_path)
     pred_data = []
 
     for file in files:
         img_ids_name = file.split('.')[0]
         img_id = int(np.squeeze(img_ids_name))
+        if args.drop:
+            anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
+            anno = coco.loadAnns(anno_ids)
+            annos = []
+            iscrowd = False
+            for label in anno:
+                bbox = label["bbox"]
+                class_name = classs_dict[label["category_id"]]
+                iscrowd = iscrowd or label["iscrowd"]
+                if class_name in train_cls:
+                    x_min, x_max = bbox[0], bbox[0] + bbox[2]
+                    y_min, y_max = bbox[1], bbox[1] + bbox[3]
+                    annos.append(list(map(round, [y_min, x_min, y_max, x_max])) + [train_cls_dict[class_name]])
+            if iscrowd or (not annos):
+                continue
+
         img_size = get_imgSize(os.path.join(img_id_file_path, file))
         image_shape = np.array([img_size[1], img_size[0]])
-
         result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
         result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
 
diff --git a/model_zoo/official/cv/ssd/scripts/run_infer_310.sh b/model_zoo/official/cv/ssd/scripts/run_infer_310.sh
index 7e77f276a5..59be2e3cd9 100644
--- a/model_zoo/official/cv/ssd/scripts/run_infer_310.sh
+++ b/model_zoo/official/cv/ssd/scripts/run_infer_310.sh
@@ -78,7 +78,7 @@ function infer()
 
 function cal_acc()
 {
-    python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path &> acc.log &
+    python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --drop &> acc.log &
 }
 
 compile_app