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+## 模型功能
+
+ 对图像中的物体进行分类。
+
+## 原始模型
+
+参考实现 ：
+
+https://github.com/KaimingHe/deep-residual-networks/tree/master/prototxt
+
+原始模型权重下载地址 :
+
+https://modelzoo-train-atc.obs.cn-north-4.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/resnet50/resnet50.caffemodel
+
+原始模型网络下载地址 ：
+
+https://modelzoo-train-atc.obs.cn-north-4.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/resnet50/resnet50.prototxt
+
+
+## om模型
+
+om模型下载地址：
+
+https://modelzoo-train-atc.obs.cn-north-4.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/resnet50/resnet50.om
+
+使用ATC模型转换工具进行模型转换时可以参考如下指令，具体操作详情和参数设置可以参考  [ATC工具使用指导](https://support.huaweicloud.com/ti-atc-A200dk_3000/altasatc_16_002.html) 
+
+```
+wget https://c7xcode.obs.cn-north-4.myhuaweicloud.com/models/resnet50/insert_op.cfg
+```
+
+```
+atc --model=model/resnet50.prototxt --weight=model/resnet50.caffemodel --framework=0 --output=model/resnet50 --soc_version=Ascend310
+```
+
+## 使用msame工具推理
+
+参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。
+
+获取到msame可执行文件之后，将待检测om文件放在model文件夹，然后进行性能测试。
+
+## 性能测试
+
+使用msame推理工具，发起推理性能测试。可以参考如下指令： 
+
+```
+ ./msame --model ../../../models/resnet50.om --output output/ --loop 100
+
+```
+
+性能测试数据为：
+
+```
+[INFO] output data success
+Inference average time: 2.788920 ms
+Inference average time without first time: 2.784808 ms
+[INFO] unload model success, model Id is 1
+[INFO] Execute sample success.
+Test Finish!
+```
+
+Batch: 1, shape:  224 *224 *3，带有AIPP，平均推理性能 2.78ms
+
+## 精度测试
+
+待完善
+
+推理效果
+
+![输入图片说明](https://images.gitee.com/uploads/images/2020/1218/101122_8d4dad5f_5578318.jpeg "out_dog2_1024_683.jpg")
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