Fix fruit config (#236)

* Fix fruit config
* Update docs/QUICK_STARTED doc

configs/yolov3_mobilenet_v1_fruit.yml

@@ -119,4 +119,5 @@ YoloTestFeed:
   image_shape: [3, 608, 608]
   dataset:
     dataset_dir: dataset/fruit
+    annotation: label_list.txt
     use_default_label: false

dataset/fruit/label_list.txt

+apple
+banana
+orange

docs/QUICK_STARTED.md

@@ -4,20 +4,20 @@ English | [简体中文](QUICK_STARTED_cn.md)
 
 This tutorial fine-tunes a tiny dataset by pretrained detection model for users to get a model and learn PaddleDetection quickly. The model can be trained in around 20min with good performance.
 
-## Data Preparation
 
-Dataset refers to [Kaggle](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection), which contains 240 images in train dataset and 60 images in test dataset. Data categories are apple, orange and banana. Download [here](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar) and uncompress the dataset after download, script for data preparation is located at [download_fruit.py](../dataset/fruit/download_fruit.py). Command is as follows:
+- **Note: before started, need to set PYTHONPATH and specifiy the GPU device as follows in Linux platform. For Windows users, also need to set PYTHONPATH correctly.**
 
 ```bash
 export PYTHONPATH=$PYTHONPATH:.
-python dataset/fruit/download_fruit.py
+export CUDA_VISIBLE_DEVICES=0
 ```
 
-- **Note: before started, run the following command and specifiy the GPU**
+## Data Preparation
+
+Dataset refers to [Kaggle](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection), which contains 240 images in train dataset and 60 images in test dataset. Data categories are apple, orange and banana. Download [here](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar) and uncompress the dataset after download, script for data preparation is located at [download_fruit.py](../dataset/fruit/download_fruit.py). Command is as follows:
 
 ```bash
-export PYTHONPATH=$PYTHONPATH:.
-export CUDA_VISIBLE_DEVICES=0
+python dataset/fruit/download_fruit.py
 ```
 
 Training:

docs/QUICK_STARTED_cn.md

@@ -4,20 +4,19 @@
 
 为了使得用户能够在很短的时间内快速产出模型，掌握PaddleDetection的使用方式，这篇教程通过一个预训练检测模型对小数据集进行finetune。在P40上单卡大约20min即可产出一个效果不错的模型。
 
-## 数据准备
-
-数据集参考[Kaggle数据集](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection)，其中训练数据集240张图片，测试数据集60张图片，数据类别为3类：苹果，橘子，香蕉。[下载链接](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar)。数据下载后分别解压即可, 数据准备脚本位于[download_fruit.py](../dataset/fruit/download_fruit.py)。下载数据方式如下：
+- **注：在开始前，对于Linux用户, 运行如下命令设置PYTHONPATH，如果有GPU设备，指定GPU设备号。对于Windows用户，同样需要将PaddleDetection的根目录设置到PYTHONPATH中。**
 
 ```bash
 export PYTHONPATH=$PYTHONPATH:.
-python dataset/fruit/download_fruit.py
+export CUDA_VISIBLE_DEVICES=0
 ```
 
-- **注：在开始前，运行如下命令并指定GPU**
+## 数据准备
+
+数据集参考[Kaggle数据集](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection)，其中训练数据集240张图片，测试数据集60张图片，数据类别为3类：苹果，橘子，香蕉。[下载链接](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar)。数据下载后分别解压即可, 数据准备脚本位于[download_fruit.py](../dataset/fruit/download_fruit.py)。下载数据方式如下：
 
 ```bash
-export PYTHONPATH=$PYTHONPATH:.
-export CUDA_VISIBLE_DEVICES=0
+python dataset/fruit/download_fruit.py
 ```
 
 训练命令如下：