update readme

fluid/PaddleCV/yolov3/README.md

@@ -19,14 +19,14 @@ Running sample code in this directory requires PaddelPaddle Fluid v.1.1.0 and la
 
 [YOLOv3](https://arxiv.org/abs/1804.02767) is a one stage end to end detector。the detection principle of YOLOv3 is as follow:
 <p align="center">
-<img src"image/YOLOv3.jpg" height=400 width=400 hspace='10'/> <br />
+<img src="image/YOLOv3.jpg" height=400 width=400 hspace='10'/> <br />
 YOLOv3 detection principle
 </p>
 
 YOLOv3 divides the input image in to S\*S grids and predict B bounding boxes in each grid, predictions of boxes include Location(x, y, w, h), Confidence Score and probabilities of C classes, therefore YOLOv3 output layer has S\*S\*B\*(5 + C) channels. YOLOv3 loss consist of three parts: location loss, IoU loss and classification loss.
 The bone network of YOLOv3 is darknet53, the structure of YOLOv3 is as follow:
 <p align="center">
-<img src"image/YOLOv3_structure.jpg" height=400 width=400 hspace='10'/> <br />
+<img src="image/YOLOv3_structure.jpg" height=400 width=400 hspace='10'/> <br />
 YOLOv3 structure
 </p>
 
@@ -134,10 +134,10 @@ Inference is used to get prediction score or image features based on trained mod
 
 Visualization of infer result is shown as below:
 <p align="center">
-<img src="image/000000000139.jpg" height=300 width=400 hspace='10'/>
-<img src="image/000000127517.jpg" height=300 width=400 hspace='10'/>
-<img src="image/000000203864.jpg" height=300 width=400 hspace='10'/>
-<img src="image/000000515077.jpg" height=300 width=400 hspace='10'/> <br />
+<img src="image/000000000139.png" height=300 width=400 hspace='10'/>
+<img src="image/000000127517.png" height=300 width=400 hspace='10'/>
+<img src="image/000000203864.png" height=300 width=400 hspace='10'/>
+<img src="image/000000515077.png" height=300 width=400 hspace='10'/> <br />
 YOLOv3 Visualization Examples
 </p>
 

fluid/PaddleCV/yolov3/README_cn.md

@@ -19,7 +19,7 @@
 
 [YOLOv3](https://arxiv.org/abs/1804.02767) 是一阶段End2End的目标检测器。其目标检测原理如下图所示:
 <p align="center">
-<img src"image/YOLOv3.jpg" height=400 width=400 hspace='10'/> <br />
+<img src="image/YOLOv3.jpg" height=400 width=400 hspace='10'/> <br />
 YOLOv3检测原理
 </p>
 
@@ -27,7 +27,7 @@ YOLOv3将输入图像分成S\*S个格子，每个格子预测B个bounding box，
 
 YOLOv3的网络结构如下图所示:
 <p align="center">
-<img src"image/YOLOv3_structure.jpg" height=400 width=400 hspace='10'/> <br />
+<img src="image/YOLOv3_structure.jpg" height=400 width=400 hspace='10'/> <br />
 YOLOv3网络结构
 </p>
 
@@ -131,10 +131,10 @@ YOLOv3
 
 下图为模型可视化预测结果：
 <p align="center">
-<img src="image/000000000139.jpg" height=300 width=400 hspace='10'/>
-<img src="image/000000127517.jpg" height=300 width=400 hspace='10'/>
-<img src="image/000000203864.jpg" height=300 width=400 hspace='10'/>
-<img src="image/000000515077.jpg" height=300 width=400 hspace='10'/> <br />
+<img src="image/000000000139.png" height=300 width=400 hspace='10'/>
+<img src="image/000000127517.png" height=300 width=400 hspace='10'/>
+<img src="image/000000203864.png" height=300 width=400 hspace='10'/>
+<img src="image/000000515077.png" height=300 width=400 hspace='10'/> <br />
 YOLOv3 预测可视化
 </p>