[cherry-pick]fix PicoDet readme docs (#4425)

* fix PicoDet readme docs

configs/picodet/README.md

@@ -3,19 +3,19 @@
 ![](../../docs/images/picedet_demo.jpeg)
 ## Introduction
 
-We developed a series of lightweight models, which named `PP-PicoDet`. Because of its excellent performance, it is very suitable for deployment on mobile or CPU. For more details, please refer to our [report on arXiv](https://arxiv.org/abs/2111.00902).
+We developed a series of lightweight models, named `PP-PicoDet`. Because of the excellent performance, our models are very suitable for deployment on mobile or CPU. For more details, please refer to our [report on arXiv](https://arxiv.org/abs/2111.00902).
 
 - 🌟 Higher mAP: the **first** object detectors that surpass mAP(0.5:0.95) **30+** within 1M parameters when the input size is 416.
 - 🚀 Faster latency: 150FPS on mobile ARM CPU.
 - 😊 Deploy friendly: support PaddleLite/MNN/NCNN/OpenVINO and provide C++/Python/Android implementation.
-- 😍 Advanced algorithm: use the most advanced algorithms and innovate, such as ESNet, CSP-PAN, SimOTA with VFL, etc.
+- 😍 Advanced algorithm: use the most advanced algorithms and offer innovation, such as ESNet, CSP-PAN, SimOTA with VFL, etc.
 
 
 <div align="center">
   <img src="../../docs/images/picodet_map.png" width='600'/>
 </div>
 
-### Comming soon
+### Comming Soon
 - [ ] More series of model, such as smaller or larger model.
 - [ ] Pretrained models for more scenarios.
 - [ ] More features in need.
@@ -35,7 +35,7 @@ We developed a series of lightweight models, which named `PP-PicoDet`. Because o
 | PicoDet-L |  416*416   |          36.6           |        52.5        |        3.30        |       3.76        |              23.36              |            **21.85**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_l_416_coco.yml) |
 | PicoDet-L |  640*640   |          40.9           |        57.6        |        3.30        |       8.91        |              54.11              |            **50.55**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_l_640_coco.yml) |
 
-#### More config
+#### More Configs
 
 | Model     | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) | Latency<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) |  download  | config |
 | :--------------------------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -52,10 +52,26 @@ We developed a series of lightweight models, which named `PP-PicoDet`. Because o
 
 </details>
 
+#### Benchmark of Other Models
+
+| Model     | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) |
+| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: |
+| YOLOv3-Tiny |  416*416   |          16.6           |        33.1      |        8.86        |       5.62        |             25.42               |
+| YOLOv4-Tiny |  416*416   |          21.7           |        40.2        |        6.06           |       6.96           |             23.69               |
+| PP-YOLO-Tiny |  320*320       |          20.6         |        -              |   1.08             |    0.58             |    6.75                           |  
+| PP-YOLO-Tiny |  416*416   |          22.7          |    -               |    1.08               |    1.02             |    10.48                          |  
+| Nanodet-M |  320*320      |          20.6            |    -               |    0.95               |    0.72             |    8.71                           |  
+| Nanodet-M |  416*416   |          23.5             |    -               |    0.95               |    1.2              |  13.35                          |
+| Nanodet-M 1.5x |  416*416   |          26.8        |    -                  | 2.08               |    2.42             |    15.83                          |
+| YOLOX-Nano     |  416*416   |          25.8          |    -               |    0.91               |    1.08             |    19.23                          |
+| YOLOX-Tiny     |  416*416   |          32.8          |    -               |    5.06               |    6.45             |    32.77                          |
+| YOLOv5n |  640*640       |          28.4             |    46.0            |    1.9                |    4.5              |    40.35                          |
+| YOLOv5s |  640*640       |          37.2             |    56.0            |    7.2                |    16.5             |    78.05                          |
+
 
 ## Deployment
 
-### Export and Convert model
+### Export and Convert Model
 
 <details>
 <summary>1. Export model (click to expand)</summary>
@@ -131,14 +147,13 @@ paddle2onnx --model_dir output_inference/picodet_s_320_coco/ \
 - [Android demo](https://github.com/JiweiMaster/PP-PicoDet-Android-Demo)
 
 
+Android demo visualization:
 <div align="center">
   <img src="../../docs/images/picodet_android_demo1.jpg" height="500px" ><img src="../../docs/images/picodet_android_demo2.jpg" height="500px" ><img src="../../docs/images/picodet_android_demo3.jpg" height="500px" ><img src="../../docs/images/picodet_android_demo4.jpg" height="500px" >
 </div>
 
 
-## Slim
-
-### quantization
+## Quantization
 
 <details open>
 <summary>Requirements:</summary>
@@ -176,11 +191,11 @@ python tools/post_quant.py -c configs/picodet/picodet_s_320_coco.yml \
           --slim_config configs/slim/post_quant/picodet_s_ptq.yml
 ```
 
-- Notes: Now the accuracy of post quant is abnormal and it is being debugged.
+- Notes: Now the accuracy of post quant is abnormal and this problem is being solved.
 
 </details>
 
-## Cite PiocDet
+## Cite PP-PiocDet
 If you use PiocDet in your research, please cite our work by using the following BibTeX entry:
 ```
 @misc{yu2021pppicodet,

deploy/lite/README.md

@@ -22,9 +22,11 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 
 预测库有两种获取方式：
 1. [**建议**]直接下载，预测库下载链接如下：
-      |平台|预测库下载链接|
-      |-|-|
-      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz)|
+      |平台| 架构 | 预测库下载链接|
+      |-|-|-|
+      |Android| arm7 | [inference_lite_lib](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) |
+      | Android | arm8 | [inference_lite_lib](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz)  |
+      | Android | arm8(FP16) | [inference_lite_lib](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8_clang_c++_static_with_extra_with_cv_with_fp16.tiny_publish.zip)  |
 
 **注意**：1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，注意选择`with_extra=ON，with_cv=ON`的下载链接。2. 目前只提供Android端demo，IOS端demo可以参考[Paddle-Lite IOS demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/master/PaddleLite-ios-demo)
 
@@ -35,7 +37,10 @@ git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
 # 如果使用编译方式，建议使用develop分支编译预测库
 git checkout develop
-./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
+# FP32
+./lite/tools/build_android.sh --arch=armv8 --toolchain=clang --with_cv=ON --with_extra=ON
+# FP16
+./lite/tools/build_android.sh --arch=armv8 --toolchain=clang --with_cv=ON --with_extra=ON --with_arm82_fp16=ON
 ```
 
 **注意**：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html#id2)。
@@ -131,7 +136,10 @@ python tools/export_model.py -c configs/picodet/picodet_s_320_coco.yml \
               -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams --output_dir=output_inference
 
 # 将inference模型转化为Paddle-Lite优化模型
+# FP32
 paddle_lite_opt  --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model
+# FP16
+paddle_lite_opt  --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model --enable_fp16=true
 
 # 将inference模型配置转化为json格式
 python deploy/lite/convert_yml_to_json.py output_inference/picodet_s_320_coco/infer_cfg.yml
@@ -144,7 +152,7 @@ python deploy/lite/convert_yml_to_json.py output_inference/picodet_s_320_coco/in
 ### 2.2 与手机联调
 
 首先需要进行一些准备工作。
-1. 准备一台arm8的安卓手机，如果编译的预测库和opt文件是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI = arm7`。
+1. 准备一台arm8的安卓手机，如果编译的预测库是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI=arm7`。
 2. 电脑上安装ADB工具，用于调试。 ADB安装方式如下：
 
     2.1. MAC电脑安装ADB: