refine README (#1664)

fluid/PaddleCV/faster_rcnn/README.md

@@ -38,18 +38,6 @@ Train the model on [MS-COCO dataset](http://cocodataset.org/#download), download
 
 ## Training
 
-After data preparation, one can start the training step by:
-
-    python train.py \
-       --model_save_dir=output/ \
-       --pretrained_model=${path_to_pretrain_model}
-       --data_dir=${path_to_data}
-
-- Set ```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7``` to specifiy 8 GPU to train.
-- For more help on arguments:
-
-    python train.py --help
-
 **download the pre-trained model:** This sample provides Resnet-50 pre-trained model which is converted from Caffe. The model fuses the parameters in batch normalization layer. One can download pre-trained model as:
 
     sh ./pretrained/download.sh
@@ -72,6 +60,18 @@ To train the model, [cocoapi](https://github.com/cocodataset/cocoapi) is needed.
     # not to install the COCO API into global site-packages
     python2 setup.py install --user
 
+After data preparation, one can start the training step by:
+
+    python train.py \
+       --model_save_dir=output/ \
+       --pretrained_model=${path_to_pretrain_model}
+       --data_dir=${path_to_data}
+
+- Set ```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7``` to specifiy 8 GPU to train.
+- For more help on arguments:
+
+    python train.py --help
+
 **data reader introduction:**
 
 * Data reader is defined in `reader.py`.

fluid/PaddleCV/faster_rcnn/README_cn.md

@@ -37,18 +37,6 @@ Faster RCNN 目标检测模型
 
 ## 模型训练
 
-数据准备完毕后，可以通过如下的方式启动训练：
-
-    python train.py \
-       --model_save_dir=output/ \
-       --pretrained_model=${path_to_pretrain_model}
-       --data_dir=${path_to_data}
-
-- 通过设置export CUDA\_VISIBLE\_DEVICES=0,1,2,3,4,5,6,7指定8卡GPU训练。
-- 可选参数见：
-
-    python train.py --help
-
 **下载预训练模型：** 本示例提供Resnet-50预训练模型，该模性转换自Caffe，并对批标准化层(Batch Normalization Layer)进行参数融合。采用如下命令下载预训练模型：
 
     sh ./pretrained/download.sh
@@ -71,6 +59,18 @@ Faster RCNN 目标检测模型
     # not to install the COCO API into global site-packages
     python2 setup.py install --user
 
+数据准备完毕后，可以通过如下的方式启动训练：
+
+    python train.py \
+       --model_save_dir=output/ \
+       --pretrained_model=${path_to_pretrain_model}
+       --data_dir=${path_to_data}
+
+- 通过设置export CUDA\_VISIBLE\_DEVICES=0,1,2,3,4,5,6,7指定8卡GPU训练。
+- 可选参数见：
+
+    python train.py --help
+
 **数据读取器说明：** 数据读取器定义在reader.py中。所有图像将短边等比例缩放至`scales`，若长边大于`max_size`, 则再次将长边等比例缩放至`max_size`。在训练阶段，对图像采用水平翻转。支持将同一个batch内的图像padding为相同尺寸。
 
 **模型设置：**