polish readme

PaddleCV/yolov3/README.md

@@ -9,7 +9,6 @@
 - [Training](#training)
 - [Evaluation](#evaluation)
 - [Inference and Visualization](#inference-and-visualization)
-- [Appendix](#appendix)
 
 ## Installation
 
@@ -45,34 +44,35 @@ Train the model on [MS-COCO dataset](http://cocodataset.org/#download), download
     cd dataset/coco
     ./download.sh
 
+The data catalog structure is as follows:
+
+```
+  data/coco/
+  ├── annotations
+  │   ├── instances_train2014.json
+  │   ├── instances_train2017.json
+  │   ├── instances_val2014.json
+  │   ├── instances_val2017.json
+  |   ...
+  ├── train2017
+  │   ├── 000000000009.jpg
+  │   ├── 000000580008.jpg
+  |   ...
+  ├── val2017
+  │   ├── 000000000139.jpg
+  │   ├── 000000000285.jpg
+  |   ...
+  
+```
 
 ## Training
 
-After data preparation, one can start the training step by:
-
-    python train.py \
-       --model_save_dir=output/ \
-       --pretrain=${path_to_pretrain_model}
-       --data_dir=${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 ./weights/download.sh
-
-Set `pretrain` to load pre-trained model. In addition, this parameter is used to load trained model when finetuning as well.
-Please make sure that pre-trained model is downloaded and loaded correctly, otherwise, the loss may be NAN during training.
-
 **Install the [cocoapi](https://github.com/cocodataset/cocoapi):**
 
 To train the model, [cocoapi](https://github.com/cocodataset/cocoapi) is needed. Install the cocoapi:
 
     git clone https://github.com/cocodataset/cocoapi.git
-    cd PythonAPI
+    cd cocoapi/PythonAPI
     # if cython is not installed
     pip install Cython
     # Install into global site-packages
@@ -81,6 +81,28 @@ 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
 
+**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 ./weights/download.sh
+
+Set `pretrain` to load pre-trained model. In addition, this parameter is used to load trained model when finetuning as well.
+Please make sure that pre-trained model is downloaded and loaded correctly, otherwise, the loss may be NAN during training.
+
+
+**training:** After data preparation, one can start the training step by:
+
+    python train.py \
+       --model_save_dir=output/ \
+       --pretrain=${path_to_pretrain_model}
+       --data_dir=${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` .

PaddleCV/yolov3/README_cn.md

@@ -9,7 +9,6 @@
 - [模型训练](#模型训练)
 - [模型评估](#模型评估)
 - [模型推断及可视化](#模型推断及可视化)
-- [附录](#附录)
 
 ## 安装
 
@@ -47,34 +46,35 @@ YOLOv3 的网络结构由基础特征提取网络、multi-scale特征融合层
     cd dataset/coco
     ./download.sh
 
+数据目录结构如下：
+
+```
+data/coco/
+├── annotations
+│   ├── instances_train2014.json
+│   ├── instances_train2017.json
+│   ├── instances_val2014.json
+│   ├── instances_val2017.json
+|   ...
+├── train2017
+│   ├── 000000000009.jpg
+│   ├── 000000580008.jpg
+|   ...
+├── val2017
+│   ├── 000000000139.jpg
+│   ├── 000000000285.jpg
+|   ...
+
+```
 
 ## 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/ \
-       --pretrain=${path_to_pretrain_model}
-       --data_dir=${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darknet53预训练模型，该模型转换自作者提供的darknet53在ImageNet上预训练的权重，采用如下命令下载预训练模型：
-
-    sh ./weights/download.sh
-
-通过初始化`pretrain` 加载预训练模型。同时在参数微调时也采用该设置加载已训练模型。
-请在训练前确认预训练模型下载与加载正确，否则训练过程中损失可能会出现NAN。
-
 **安装[cocoapi](https://github.com/cocodataset/cocoapi)：**
 
 训练前需要首先下载[cocoapi](https://github.com/cocodataset/cocoapi)：
 
     git clone https://github.com/cocodataset/cocoapi.git
-    cd PythonAPI
+    cd cocoapi/PythonAPI
     # if cython is not installed
     pip install Cython
     # Install into global site-packages
@@ -83,6 +83,27 @@ YOLOv3 的网络结构由基础特征提取网络、multi-scale特征融合层
     # not to install the COCO API into global site-packages
     python2 setup.py install --user
 
+**下载预训练模型：** 本示例提供darknet53预训练模型，该模型转换自作者提供的darknet53在ImageNet上预训练的权重，采用如下命令下载预训练模型：
+
+    sh ./weights/download.sh
+
+通过初始化`pretrain` 加载预训练模型。同时在参数微调时也采用该设置加载已训练模型。
+请在训练前确认预训练模型下载与加载正确，否则训练过程中损失可能会出现NAN。
+
+**开始训练：** 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/ \
+       --pretrain=${path_to_pretrain_model}
+       --data_dir=${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。