diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index c8e8d5b3349dd40fd15fe7c6f4a14c362f931f97..6d1695f0f976376264d81c177db58e425b4f3c06 100755
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -169,45 +169,45 @@ def create_predictor(args, mode, logger):
         if mode == "det":
             min_input_shape = {
                 "x": [1, 3, 50, 50],
-                "conv2d_92.tmp_0": [1, 96, 20, 20],
-                "conv2d_91.tmp_0": [1, 96, 10, 10],
+                "conv2d_92.tmp_0": [1, 120, 20, 20],
+                "conv2d_91.tmp_0": [1, 24, 10, 10],
                 "conv2d_59.tmp_0": [1, 96, 20, 20],
-                "nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
-                "nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
-                "conv2d_124.tmp_0": [1, 96, 20, 20],
-                "nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
-                "nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
-                "nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
+                "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
+                "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
+                "conv2d_124.tmp_0": [1, 256, 20, 20],
+                "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
+                "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
+                "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
                 "elementwise_add_7": [1, 56, 2, 2],
-                "nearest_interp_v2_0.tmp_0": [1, 96, 2, 2]
+                "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
             }
             max_input_shape = {
                 "x": [1, 3, 2000, 2000],
-                "conv2d_92.tmp_0": [1, 96, 400, 400],
-                "conv2d_91.tmp_0": [1, 96, 200, 200],
+                "conv2d_92.tmp_0": [1, 120, 400, 400],
+                "conv2d_91.tmp_0": [1, 24, 200, 200],
                 "conv2d_59.tmp_0": [1, 96, 400, 400],
-                "nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
+                "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
                 "conv2d_124.tmp_0": [1, 256, 400, 400],
-                "nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
-                "nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
-                "nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
-                "nearest_interp_v2_5.tmp_0": [1, 24, 400, 400],
+                "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
+                "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
+                "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
+                "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
                 "elementwise_add_7": [1, 56, 400, 400],
-                "nearest_interp_v2_0.tmp_0": [1, 96, 400, 400]
+                "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
             }
             opt_input_shape = {
                 "x": [1, 3, 640, 640],
-                "conv2d_92.tmp_0": [1, 96, 160, 160],
-                "conv2d_91.tmp_0": [1, 96, 80, 80],
+                "conv2d_92.tmp_0": [1, 120, 160, 160],
+                "conv2d_91.tmp_0": [1, 24, 80, 80],
                 "conv2d_59.tmp_0": [1, 96, 160, 160],
-                "nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
-                "nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
+                "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
+                "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
                 "conv2d_124.tmp_0": [1, 256, 160, 160],
-                "nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
-                "nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
-                "nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
+                "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
+                "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
+                "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
                 "elementwise_add_7": [1, 56, 40, 40],
-                "nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
+                "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
             }
         elif mode == "rec":
             min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}