diff --git a/tutorials/ImageDetectionSample/python/README.md b/tutorials/ImageDetectionSample/python/README.md
index 4dddd4a0b881562fef94bdcef435135110c8276a..6601c5028827e4788928ea2641af55460ae1230e 100644
--- a/tutorials/ImageDetectionSample/python/README.md
+++ b/tutorials/ImageDetectionSample/python/README.md
@@ -4,8 +4,7 @@
 提供的demo，实现图像检测样例运行并且输出检测结果写入图片result.jpg
 
 ### 准备工作
-样例获取链接(https://gitee.com/zhangdwe/mindxsdk-referenceapps/tree/master/tutorials/ImageDetectionSample/python)
-将样例目录python 从mxsdkreferenceapps/tutorials/ImageDetectionSample文件夹下 移动到${SDK安装路径}/mxVision/samples/mxVision/python/路径下
+将样例目录python 从mxsdkreferenceapps/utorials/ImageDetectionSample文件夹下 移动到${SDK安装路径}/mxVision/samples/mxVision/python/路径下
 
 可以使用mv 命令
 进入到移动后的工程路径下
@@ -18,7 +17,7 @@ bash model_conversion.sh
 ```
 
 ### pipeline 准备
-将main.py 文件中 mxpi_modelinfer0插件中的labelPath路径中的${SDK安装路径} 替换为自己的SDK安装路径
+将main.py 文件中 mxpi_objectpostprocessor0插件中的postProcessLibPath路径中的${SDK安装路径} 替换为自己的SDK安装路径
 
 ### 配置环境变量
 将${SDK安装路径}替换为自己的SDK安装路径; 将${MX_SDK_HOME}替换成对应路径
diff --git a/tutorials/ImageDetectionSample/python/main.py b/tutorials/ImageDetectionSample/python/main.py
index 5989c075551a78ecfad00e1647dae8185449e9ab..40c4f3aeb9031a3c5982a6c3cf1a654a44a765d8 100644
--- a/tutorials/ImageDetectionSample/python/main.py
+++ b/tutorials/ImageDetectionSample/python/main.py
@@ -20,7 +20,9 @@ limitations under the License.
 import json
 import os
 import cv2
-from StreamManagerApi import StreamManagerApi, MxDataInput
+
+import MxpiDataType_pb2 as MxpiDataType
+from StreamManagerApi import StreamManagerApi, MxDataInput, StringVector
 
 if __name__ == '__main__':
     streamManagerApi = StreamManagerApi()
@@ -36,41 +38,46 @@ if __name__ == '__main__':
             "stream_config": {
                 "deviceId": "0"
             },
+            "appsrc0": {
+                "props": {
+                    "blocksize": "409600"
+                },
+                "factory": "appsrc",
+                "next": "mxpi_imagedecoder0"
+            },
             "mxpi_imagedecoder0": {
+                "props": {
+                    "deviceId": "0"
+                },
                 "factory": "mxpi_imagedecoder",
                 "next": "mxpi_imageresize0"
             },
             "mxpi_imageresize0": {
-                "factory": "mxpi_imageresize",
-                "next": "mxpi_modelinfer0"
-            },
-            "mxpi_modelinfer0": {
                 "props": {
-                    "modelPath": "models/yolov3_tf_bs1_fp16.om",
-                    "postProcessConfigPath": "models/yolov3_tf_bs1_fp16.cfg",
-                    "labelPath": "models/coco.names",
-                    "postProcessLibPath": "libMpYOLOv3PostProcessor.so"
+                    "dataSource": "mxpi_imagedecoder0",
+                    "resizeHeight": "416",
+                    "resizeWidth": "416"
                 },
-                "factory": "mxpi_modelinfer",
-                "next": "mxpi_imagecrop0"
-            },
-            "mxpi_imagecrop0": {
-                "factory": "mxpi_imagecrop",
-                "next": "mxpi_dataserialize0"
+                "factory": "mxpi_imageresize",
+                "next": "mxpi_tensorinfer0"
             },
-            "mxpi_dataserialize0": {
+            "mxpi_tensorinfer0": {
                 "props": {
-                    "outputDataKeys": "mxpi_modelinfer0"
+                    "dataSource": "mxpi_imageresize0",
+                    "modelPath": "models/yolov3_tf_bs1_fp16.om"
                 },
-                "factory": "mxpi_dataserialize",
-                "next": "appsink0"
+                "factory": "mxpi_tensorinfer",
+                "next": "mxpi_objectpostprocessor0"
             },
-            "appsrc0": {
+            "mxpi_objectpostprocessor0": {
                 "props": {
-                    "blocksize": "409600"
+                    "dataSource": "mxpi_tensorinfer0",
+                    "postProcessConfigPath": "models/yolov3_tf_bs1_fp16.cfg",
+                    "labelPath": "models/coco.names",
+                    "postProcessLibPath": "${SDK安装路径}/lib/modelpostprocessors/libyolov3postprocess.so"
                 },
-                "factory": "appsrc",
-                "next": "mxpi_imagedecoder0"
+                "factory": "mxpi_objectpostprocessor",
+                "next": "appsink0"
             },
             "appsink0": {
                 "props": {
@@ -98,31 +105,43 @@ if __name__ == '__main__':
     # Inputs data to a specified stream based on streamName.
     streamName = b'detection'
     inPluginId = 0
-    uniqueId = streamManagerApi.SendDataWithUniqueId(streamName, inPluginId, dataInput)
+
+    uniqueId = streamManagerApi.SendData(streamName, inPluginId, dataInput)
 
     if uniqueId < 0:
         print("Failed to send data to stream.")
         exit()
 
-    # Obtain the inference result by specifying streamName and uniqueId.
-    infer_result = streamManagerApi.GetResultWithUniqueId(streamName, uniqueId, 3000)
-    if infer_result.errorCode != 0:
-        print("GetResultWithUniqueId error. errorCode=%d, errorMsg=%s" % (
-            infer_result.errorCode, infer_result.data.decode()))
+    key = b"mxpi_objectpostprocessor0"
+    keyVec = StringVector()
+    keyVec.push_back(key)
+
+    infer_result = streamManagerApi.GetProtobuf(streamName, 0, keyVec)
+
+    if infer_result.size() == 0:
+        print("infer_result is null")
         exit()
 
+    if infer_result[0].errorCode != 0:
+        print("GetProtobuf error. errorCode=%d, errorMsg=%s" % (
+            infer_result[0].errorCode, infer_result[0].data.decode()))
+        exit()
+
+    # mxpi_objectpostprocessor0模型后处理插件输出信息
+    objectList = MxpiDataType.MxpiObjectList()
+    objectList.ParseFromString(infer_result[0].messageBuf)
+
     # print the infer result
-    print(infer_result.data.decode())
+    print(objectList)
+    results = objectList.objectVec[0]
 
-    results = json.loads(infer_result.data.decode())
     bboxes = []
-    for bbox in results['MxpiObject']:
-        bboxes = {'x0': int(bbox['x0']),
-                  'x1': int(bbox['x1']),
-                  'y0': int(bbox['y0']),
-                  'y1': int(bbox['y1']),
-                  'confidence': round(bbox['classVec'][0]['confidence'], 4),
-                  'text': bbox['classVec'][0]['className']}
+    bboxes = {'x0': int(results.x0),
+              'x1': int(results.x1),
+              'y0': int(results.y0),
+              'y1': int(results.y1),
+              'confidence': round(results.classVec[0].confidence, 4),
+              'text': results.classVec[0].className}
     img_path = "test.jpg"
     img = cv2.imread(img_path)
     text = "{}{}".format(str(bboxes['confidence']), " ")