Merge pull request #481 from guru4elephant/fix_detection_example

refine detection example

python/examples/faster_rcnn_model/README.md

@@ -2,55 +2,23 @@
 
 ([简体中文](./README_CN.md)|English)
 
-This article requires [Paddle Detection](https://github.com/PaddlePaddle/PaddleDetection) trained models and configuration files. If users want to quickly deploy on Paddle Serving, please read the Chapter 2 directly.
-
-## 1. Train an object detection model
-
-Users can read [Paddle Detection Getting Started](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/tutorials/GETTING_STARTED_cn.md) to understand the background of Paddle Detection. The purpose of PaddleDetection is to provide a rich and easy-to-use object detection model for industry and academia. Not only is it superior in performance and easy to deploy, but it can also flexibly meet the needs of algorithm research.
-
-### Environmental requirements
-
-CPU version: No special requirements
-
-GPU version: CUDA 9.0 and above
-
-```
-git clone https://github.com/PaddlePaddle/PaddleDetection
-cd PaddleDetection
-```
-Next, you can train the faster rcnn model
-```
-python tools/train.py -c configs/faster_rcnn_r50_1x.yml
-```
-The time for training the model depends on the situation and is related to the computing power of the training equipment and the number of iterations.
-In the training process, `faster_rcnn_r50_1x.yml` defines the snapshot of the saved model. After the final training, the model with the best effect will be saved as `best_model.pdmodel`, which is a compressed PaddleDetection Exclusive model files.
-
-**If we want the model to be used by Paddle Serving, we must do export_model.**
-
-Output model
-```
-python export_model.py
-```
-## 2. Start the model and predict
-If users do not use the Paddle Detection project to train models, we are here to provide you with sample model downloads. If you trained the model with Paddle Detection, you can skip the ** Download Model ** section.
-
-### Download model
+### Get The Faster RCNN Model
 ```
 wget https://paddle-serving.bj.bcebos.com/pddet_demo/faster_rcnn_model.tar.gz
-wget https://paddle-serving.bj.bcebos.com/pddet_demo/paddle_serving_app-0.0.1-py2-none-any.whl
 wget https://paddle-serving.bj.bcebos.com/pddet_demo/infer_cfg.yml
-tar xf faster_rcnn_model.tar.gz
-mv faster_rcnn_model/pddet *.
 ```
+If you want to have more detection models, please refer to [Paddle Detection Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/MODEL_ZOO_cn.md)
 
 ### Start the service
 ```
-GLOG_v = 2 python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_id 0
+tar xf faster_rcnn_model.tar.gz
+mv faster_rcnn_model/pddet *.
+GLOG_v=2 python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_id 0
 ```
 
 ### Perform prediction
 ```
-python test_client.py --config_path = infer_cfg.yml --infer_img = 000000570688.jpg --dump_result --visualize
+python test_client.py pddet_client_conf/serving_client_conf.prototxt infer_cfg.yml 000000570688.jpg
 ```
 
 ## 3. Result analysis

python/examples/faster_rcnn_model/README_CN.md

-# Faster RCNN模型
+# 使用Paddle Serving部署Faster RCNN模型
 
 (简体中文|[English](./README.md))
 
-本文需要[Paddle Detection](https://github.com/PaddlePaddle/PaddleDetection)训练的模型和配置文件。如果用户想要快速部署在Paddle Serving上，请直接阅读第二章节。
-
-## 1. 训练物体检测模型
-
-用户可以阅读 [Paddle Detection入门使用](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/tutorials/GETTING_STARTED_cn.md)来了解Paddle Detection的背景。PaddleDetection的目的是为工业界和学术界提供丰富、易用的目标检测模型。不仅性能优越、易于部署，而且能够灵活的满足算法研究的需求。
-
-### 环境要求
-
-CPU版： 没有特别要求
-
-GPU版： CUDA 9.0及以上
-
-```
-git clone https://github.com/PaddlePaddle/PaddleDetection
-cd PaddleDetection
-```
-接下来可以训练faster rcnn模型
-```
-python tools/train.py -c configs/faster_rcnn_r50_1x.yml
-```
-训练模型的时间视情况而定，与训练的设备算力和迭代轮数相关。
-在训练的过程中，`faster_rcnn_r50_1x.yml`当中定义了保存模型的`snapshot`，在最终训练完成后，效果最好的模型，会被保存为`best_model.pdmodel`，这是一个经过压缩的PaddleDetection的专属模型文件。
-
-**如果我们要让模型可被Paddle Serving所使用，必须做export_model。**
-
-输出模型
-```
-python export_model.py
-```
-
-## 2. 启动模型并预测
-如果用户没有用Paddle Detection项目训练模型，我们也在此为您提供示例模型下载。如果您用Paddle Detection训练了模型，可以跳过 **下载模型** 部分。
-
-### 下载模型
+## 获得Faster RCNN模型
 ```
 wget https://paddle-serving.bj.bcebos.com/pddet_demo/faster_rcnn_model.tar.gz
-wget https://paddle-serving.bj.bcebos.com/pddet_demo/paddle_serving_app-0.0.1-py2-none-any.whl
 wget https://paddle-serving.bj.bcebos.com/pddet_demo/infer_cfg.yml
-tar xf faster_rcnn_model.tar.gz
-mv faster_rcnn_model/pddet* .
 ```
+如果你想要更多的检测模型，请参考[Paddle检测模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/MODEL_ZOO_cn.md)
 
 ### 启动服务
 ```
+tar xf faster_rcnn_model.tar.gz
+mv faster_rcnn_model/pddet* .
 GLOG_v=2 python -m paddle_serving_server_gpu.serve --model pddet_serving_model --port 9494 --gpu_id 0
 ```
 
 ### 执行预测
 ```
-python test_client.py --config_path=infer_cfg.yml --infer_img=000000570688.jpg --dump_result --visualize
+python test_client.py pddet_client_conf/serving_client_conf.prototxt infer_cfg.yml 000000570688.jpg
 ```
 
 ## 3. 结果分析