polish changelog (#4422)

README_cn.md

@@ -299,11 +299,7 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
 
 ## 版本更新
 
-v2.2版本已经在`08/2021`发布，全新发布Transformer检测系列模型，新增关键点检测Dark HRNet模型，新增人头、车辆跟踪垂类模型，发布旋转框检测S2ANet优化模型，主流模型支持batch size > 1预测部署，详细内容请参考[版本更新文档](docs/CHANGELOG.md)
-
-v2.1版本已经在`05/2021`发布，全新发布关键点检测和多目标跟踪能力，支持无标注框检测，发布PPYOLO系列模型压缩模型，新增ONNX模型导出教程，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
-
-v2.0版本已经在`04/2021`发布，全面支持动态图版本，新增支持BlazeFace, PSSDet等系列模型和大量骨干网络，发布PP-YOLO v2, PP-YOLO tiny和旋转框检测S2ANet模型。支持模型蒸馏、VisualDL，新增动态图预测部署benchmark，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
+版本更新内容请参考[版本更新文档](docs/CHANGELOG.md)
 
 
 ## 许可证书

README_en.md

@@ -305,11 +305,7 @@ The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of represen
 
 ## Updates
 
-v2.2 was released at `08/2021`, release Transformer detection models, release Dark HRNet keypoint detection model, release tracking models of head and vehicle, release optimized S2ANet model, inference with batch size > 1 supported for main architectures. Please refer to [change log](docs/CHANGELOG_en.md) for details.
-
-v2.1 was released at `05/2021`, Release Keypoint Detection and Multi-Object Tracking. Release model compression for PPYOLO series. Update documents such as export ONNX model. Please refer to [change log](docs/CHANGELOG_en.md) for details.
-
-v2.0 was released at `04/2021`, fully support dygraph version, which add BlazeFace, PSS-Det and plenty backbones, release `PP-YOLOv2`, `PP-YOLO tiny` and `S2ANet`, support model distillation and VisualDL, add inference benchmark, etc. Please refer to [change log](docs/CHANGELOG_en.md) for details.
+Updates please refer to [change log](docs/CHANGELOG_en.md) for details.
 
 
 ## License

docs/CHANGELOG.md

+简体中文 | [English](./CHANGELOG_en.md)
+
 # 版本更新信息
 
 ## 最新版本信息
@@ -17,7 +19,7 @@
     - 发布针对EdgeBoard优化的PP-YOLO-EB模型
 
   - 跟踪
-    - 发布FairMOT高精度模型、小尺度模型和轻量级模型
+    - 发布FairMot高精度模型、小尺度模型和轻量级模型
     - 发布行人、人头和车辆实跟踪垂类模型库，覆盖航拍监控、自动驾驶、密集人群、极小目标等场景
     - DeepSORT模型适配PP-YOLO, PP-PicoDet等更多检测器
 

docs/CHANGELOG_en.md

+English | [简体中文](./CHANGELOG.md)
+
 # Version Update Information
 
 ## Last Version Information
 
+### 2.3(11.03/2021)
+
+- Feature models:
+    - Object detection: The lightweight object detection model PP-PicoDet, performace and inference speed reaches SOTA on mobile side
+    - Keypoint detection: The lightweight keypoint detection model PP-TinyPose for mobile side
+
+- Model richness:
+    - Object detection:
+        - Publish Swin-Transformer object detection model
+        - Publish TOOD(Task-aligned One-stage Object Detection) model
+        - Publish GFL(Generalized Focal Loss) object detection model
+        - Publish Sniper optimization method for tiny object detection, supporting Faster RCNN and PP-YOLO series models
+        - Publish PP-YOLO optimized model PP-YOLO-EB for EdgeBoard
+    - Multi-object tracking:
+        - Publish high-precision, small-scale and lightweight model based on FairMot
+        - Publish real-time tracking model zoo for pedestrian, head and vehicle tracking, including scenarios such as aerial surveillance, autonomous driving, dense crowds, and tiny object tracking
+        - DeepSort support PP-YOLO, PP-PicoDet as object detector
+    - Keypoint detection:
+        - Publish Lite HRNet model
+
+- Inference deployment:
+    - Support NPU deployment for YOLOv3 series
+    - Support C++ deployment for FairMot
+    - Support C++ and PaddleLite deployment for keypoint detection series model
+
+- Documents:
+    - Add series English documents
+
+
 ### 2.2(08.10/2021)
 
 - Model richness:
@@ -13,7 +44,7 @@
 - Model optimization:
     - AlignConv optimization model was released by S2ANet, and DOTA dataset mAP was optimized to 74.0
 
-- Predict deployment
+- Inference deployment
     - Mainstream models support batch size>1 predictive deployment, including YOLOv3, PP-YOLO, Faster RCNN, SSD, TTFNet,  FCOS
     - New addition of target tracking models (JDE, Fair Mot, Deep Sort) Python side prediction deployment support, and support for TensorRT prediction
     - FairMot joint key point detection model deployment Python side predictive deployment support
@@ -23,7 +54,7 @@
     - New TensorRT version notes to Windows Predictive Deployment documentation
     - FAQ documents are updated
 
-- Problem fixes:
+- Bug fixes:
     - Fixed PP-YOLO series model training convergence problem
     - Fixed the problem of no label data training when batch_size > 1