Update changlog and version for release 2.4 (#5486)

* update version and changelog for release/2.4

README.md

-README_en.md
+README_cn.md
\ No newline at end of file

docs/CHANGELOG.md

@@ -4,6 +4,34 @@
 
 ## 最新版本信息
 
+### 2.4(03.24/2022)
+
+- PP-YOLOE：
+  - 发布PP-YOLOE特色模型，COCO数据集精度51.4%，V100预测速度78.1 FPS，精度速度服务器端SOTA
+  - 发布s/m/l/x系列模型，打通TensorRT、ONNX部署能力
+  - 支持混合精度训练，训练较PP-YOLOv2加速33%
+
+- PP-PicoDet:
+  - 发布PP-PicoDet优化模型，精度提升2%左右，CPU预测速度提升63%。
+  - 新增参数量0.7M的PicoDet-XS模型
+  - 后处理集成到网络中，优化端到端部署成本
+
+- 行人分析Pipeline：
+  - 发布PP-Human行人分析Pipeline，覆盖行人检测、属性识别、行人跟踪、跨镜跟踪、人流量统计、动作识别多种功能，打通TensorRT部署
+  - 属性识别支持StrongBaseline模型
+  - ReID支持Centroid模型
+  - 动作识别支持ST-GCN摔倒检测
+
+- 框架功能优化：
+  - 支持混合精度训练，通过`–amp`开启
+  - EMA训练速度优化20%，优化EMA训练模型保存方式
+  - 支持infer预测结果保存为COCO格式
+
+- 部署优化：
+  - RCNN全系列模型支持Paddle2ONNX导出ONNX模型
+  - SSD模型支持导出时融合解码OP，优化边缘端部署速度
+  - 支持NMS导出TensorRT，TensorRT部署端到端速度提升
+
 ### 2.3(11.03/2021)
 
 - 特色模型:

docs/CHANGELOG_en.md

@@ -4,6 +4,34 @@ English | [简体中文](./CHANGELOG.md)
 
 ## Last Version Information
 
+### 2.4(03.24/2022)
+
+- PP-YOLOE：
+  - Release PP-YOLOE object detection models, achieve mAP as 51.4% on COCO test dataset and 78.1 FPS on Nvidia V100, reach SOTA performance for object detection on GPU``
+  - Release series models: s/m/l/x, and support deployment base on TensorRT & ONNX
+  - Spport AMP training and training speed is 33% faster than PP-YOLOv2
+
+- PP-PicoDet:
+  - Release enhanced models of PP-PicoDet, mAP promoted ~2% on COCO and inference speed accelerated 63% on CPU
+  - Release PP-PicoDet-XS model with 0.7M parameters
+  - Post-processing integrated into the network to optimize deployment pipeline 
+
+- PP-Human：
+  - Release PP-Human human analysis pipeline，including pedestrian detection, attribute recognition, human tracking, multi-camera tracking, human statistics, action recognition. Supporting deployment with TensorRT
+  - Release StrongBaseline model for attribute recognition
+  - Release Centroid model for ReID
+  - Release ST-GCN model for falldown action recognition
+
+- Function Optimize：
+  - Support AMP training, enable with `--amp`
+  - Optimize 20% training speed when training with EMA, improve saving method of EMA weights
+  - Support saving inference results in COCO format
+
+- Deployment Optimize：
+  - Support export ONNX model by Paddle2ONNX for all RCNN models
+  - Supoort export model with fused decode OP for SSD models to enhance inference speed in edge side
+  - Support export NMS to TensorRT model, optmize inference speed on TensorRT
+
 ### 2.3(11.03/2021)
 
 - Feature models:

setup.py

@@ -21,7 +21,7 @@ from setuptools import find_packages, setup
 
 # ==============  version definition  ==============
 
-PPDET_VERSION = "2.3.0"
+PPDET_VERSION = "2.4.0"
 
 
 def parse_version():