diff --git a/configs/yolov3_mobilenet_v1_fruit.yml b/configs/yolov3_mobilenet_v1_fruit.yml
index c89aefc4199123c3ec764e798a15bbb4775a3298..c5d1a5ed3e98fbae68d61c025c7efb3590a14ebd 100644
--- a/configs/yolov3_mobilenet_v1_fruit.yml
+++ b/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
diff --git a/dataset/fruit/label_list.txt b/dataset/fruit/label_list.txt
new file mode 100755
index 0000000000000000000000000000000000000000..1f60d62c399939cd92e667c1fb938764b3ec2901
--- /dev/null
+++ b/dataset/fruit/label_list.txt
@@ -0,0 +1,3 @@
+apple
+banana
+orange
diff --git a/docs/QUICK_STARTED.md b/docs/QUICK_STARTED.md
index 926bdf39d0ba4b48c4284b4e83bef7549e432913..e8eb313c1894454d4782d9df7f1307ab28fee828 100644
--- a/docs/QUICK_STARTED.md
+++ b/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:
@@ -26,7 +26,7 @@ Training:
 python -u tools/train.py -c configs/yolov3_mobilenet_v1_fruit.yml \
                         --use_tb=True \
                         --tb_log_dir=tb_fruit_dir/scalar \
-                        --eval 
+                        --eval
 ```
 
 Use `yolov3_mobilenet_v1` to fine-tune the model from COCO dataset. Meanwhile, loss and mAP can be observed on tensorboard.  
diff --git a/docs/QUICK_STARTED_cn.md b/docs/QUICK_STARTED_cn.md
index c11f041ba4405ebbf6c60365bbe937df55e6374d..b8c1513db436447dc694d38d2d023f72285abb4f 100644
--- a/docs/QUICK_STARTED_cn.md
+++ b/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：