[cherry-pick]update some link in README (#1807)

* update some link in README

* fix product news

README_cn.md

@@ -15,8 +15,9 @@ PaddleDetection模块化地实现了多种主流目标检测算法，提供了
 </div>
 
 ### 产品动态
+- 2020.11.20: 发布release/0.5版本,详情请参考[版本更新文档](docs/CHANGELOG.md)。
+- 2020.11.10: 添加实例分割模型[SOLOv2](configs/solov2)，在Tesla V100上达到38.6 FPS, COCO-val数据集上mask ap达到38.8，预测速度提高24%，mAP提高2.4个百分点。
 - 2020.10.30: PP-YOLO支持矩形图像输入，并新增PACT模型量化策略。
-- 2020.10.01: 添加实例分割模型SOLOv2，在Tesla V100上达到38.6 FPS, COCO-val数据集上mask ap达到38.8，预测速度提高24%，mAP提高2.4个百分点。
 - 2020.09.30: 发布[移动端检测demo](deploy/android_demo)，可直接扫码安装体验。
 - 2020.09.21-27: 【目标检测7日打卡课】手把手教你从入门到进阶，深入了解目标检测算法的前世今生。立即加入课程QQ交流群(1136406895)一起学习吧 :)
 - 2020.07.24: 发布**产业最实用**目标检测模型 [PP-YOLO](https://arxiv.org/abs/2007.12099) ，深入考虑产业应用对精度速度的双重面诉求，COCO数据集精度45.2%(最新45.9%)，Tesla V100预测速度72.9 FPS，详细信息见[文档](configs/ppyolo/README_cn.md)。

README_en.md

@@ -16,11 +16,12 @@ After a long time of industry practice polishing, PaddleDetection has had smooth
 
 ### Product dynamic
 
+- 2020.11.20: Release `release/0.5` version, Please refer to [change log](docs/CHANGELOG.md) for details.
+- 2020.11.10: Added [SOLOv2](configs/solov2) as an instance segmentation model, which reached 38.6 FPS on a single Tesla V100, 38.8 mask AP on Coco-Val dataset, and  inference speed increased by 24% and mAP by 2.4 percentage points.
 - 2020.10.30: PP-YOLO support rectangular image input, and add a new PACT quantization strategy for slim。
-- 2020.10.01: Added SOLOv2 as an instance segmentation model, which reached 38.6 FPS on a single Tesla V100, 38.8 mask AP on Coco-Val dataset, and  inference speed increased by 24% and mAP by 2.4 percentage points.
-- 2020.09.30: Released the mobile-side detection demo, and you can directly scan the code for installation experience.
+- 2020.09.30: Released the [mobile-side detection demo](deploy/android_demo), and you can directly scan the code for installation experience.
 - 2020.09.21-27: [Object detection 7 days of punching class] Hand in hand to teach you from the beginning to the advanced level, in-depth understanding of the object detection algorithm life. Join the course QQ group (1136406895) to study together :)
-- 2020.07.24: [PP-YOLO](https://arxiv.org/abs/2007.12099), which is **the most practical** object detection model, was released, it deeply considers the double demands of industrial applications for accuracy and speed, and reached accuracy as 45.2% (the latest 45.9%) on COCO dataset, inference speed as 72.9 FPS on a single Test V100. Please refer to [PP-YOLO](https://arxiv.org/abs/2007.12099) for details.
+- 2020.07.24: [PP-YOLO](https://arxiv.org/abs/2007.12099), which is **the most practical** object detection model, was released, it deeply considers the double demands of industrial applications for accuracy and speed, and reached accuracy as 45.2% (the latest 45.9%) on COCO dataset, inference speed as 72.9 FPS on a single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
 - 2020.06.11: Publish 676 classes of large-scale server-side practical object detection models that are applicable to most application scenarios and can be used directly for prediction or for fine-tuning other tasks.
 
 ### Features