fix yolov3 readme (#3213)

PaddleCV/yolov3/README.md

@@ -220,7 +220,7 @@ YOLOv3检测原理
 
 ### 模型结构
 
-YOLOv3将输入图像分成S\*S个格子，每个格子预测B个bounding box，每个bounding box预测内容包括: Location(x, y, w, h)、Confidence Score和C个类别的概率，因此YOLOv3输出层的channel数为S\*S\*B\*(5 + C)。YOLOv3的loss函数也有三部分组成：Location误差，Confidence误差和分类误差。
+YOLOv3将输入图像分成S\*S个格子，每个格子预测B个bounding box，每个bounding box预测内容包括: Location(x, y, w, h)、Confidence Score和C个类别的概率，因此YOLOv3输出层的channel数为B\*(5 + C)。YOLOv3的loss函数也有三部分组成：Location误差，Confidence误差和分类误差。
 
 YOLOv3的网络结构如下图所示:
 <p align="center">

PaddleCV/yolov3/README_en.md

@@ -221,7 +221,7 @@ YOLOv3 detection principle
 
 ### Model structure
 
-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 consists of three parts: location loss, confidence loss and classification loss.
+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 B\*(5 + C) channels. YOLOv3 loss consists of three parts: location loss, confidence 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 />