keypoint docs add flops&params;

test=document_fix

configs/keypoint/README.md

@@ -37,19 +37,21 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案，包
 
 ### 移动端模型推荐
 
-| 检测模型                                                                                                | 关键点模型                                 | 输入尺寸                      | COCO数据集精度                | 平均推理耗时 (FP16)               | 模型权重                                                                                                                                                                             | Paddle-Lite部署模型（FP16)                                                                                                                                                           |
-|:--------------------------------------------------------------------------------------------------- |:------------------------------------- |:-------------------------:|:------------------------:|:---------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml)  | 检测：192x192<br>关键点：128x96  | 检测mAP：29.0<br>关键点AP：58.1 | 检测耗时：2.37ms<br>关键点耗时：3.27ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb)  |
-| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | 检测：320x320<br>关键点：256x192 | 检测mAP：38.5<br>关键点AP：68.8 | 检测耗时：6.30ms<br>关键点耗时：8.33ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
+| 检测模型                                                     | 关键点模型                            |             输入尺寸             |         COCO数据集精度          |          平均推理耗时 (FP16)           | 参数量 （M）                |          Flops (G)          |                           模型权重                           |                  Paddle-Lite部署模型（FP16)                  |
+| :----------------------------------------------------------- | :------------------------------------ | :------------------------------: | :-----------------------------: | :------------------------------------: | --------------------------- | :-------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
+| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml)  | 检测：192x192<br>关键点：128x96  | 检测mAP：29.0<br>关键点AP：58.1 | 检测耗时：2.37ms<br>关键点耗时：3.27ms | 检测：1.18<br/>关键点：1.36 | 检测：0.35<br/>关键点：0.08 | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
+| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | 检测：320x320<br>关键点：256x192 | 检测mAP：38.5<br>关键点AP：68.8 | 检测耗时：6.30ms<br>关键点耗时：8.33ms | 检测：1.18<br/>关键点：1.36 | 检测：0.97<br/>关键点：0.32 | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)<br>[关键点](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
+
 
 *详细关于PP-TinyPose的使用请参考[文档]((./tiny_pose/README.md))。
 
 ### 服务端模型推荐
 
-| 检测模型                                                                                                                          | 关键点模型                                      | 输入尺寸                      | COCO数据集精度                | 模型权重                                                                                                                                                                    |
-|:----------------------------------------------------------------------------------------------------------------------------- |:------------------------------------------ |:-------------------------:|:------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | 检测：640x640<br>关键点：384x288 | 检测mAP：49.5<br>关键点AP：77.8 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) |
-| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | 检测：640x640<br>关键点：256x192 | 检测mAP：49.5<br>关键点AP：76.9 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) |
+| 检测模型                                                     | 关键点模型                                 |             输入尺寸             |         COCO数据集精度          |       参数量 （M）       |        Flops (G)         |                           模型权重                           |
+| :----------------------------------------------------------- | :----------------------------------------- | :------------------------------: | :-----------------------------: | :----------------------: | :----------------------: | :----------------------------------------------------------: |
+| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | 检测：640x640<br>关键点：384x288 | 检测mAP：49.5<br>关键点AP：77.8 | 检测：54.6<br/>关键点：28.6 | 检测：115.8<br/>关键点：17.3 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) |
+| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | 检测：640x640<br>关键点：256x192 | 检测mAP：49.5<br>关键点AP：76.9 | 检测：54.6<br/>关键点：28.6 | 检测：115.8<br/>关键点：7.68 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) |
+
 
 ## 模型库
 

configs/keypoint/README_en.md

@@ -41,19 +41,23 @@ At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README_en.m
 
 ### Mobile Terminal
 
-| Detection Model                                                                                     | Keypoint Model                        | Input Size                            | Accuracy of COCO                       | Average Inference Time (FP16)       | Model Weight                                                                                                                                                                                 | Paddle-Lite Inference Model（FP16)                                                                                                                                                           |
-|:--------------------------------------------------------------------------------------------------- |:------------------------------------- |:-------------------------------------:|:--------------------------------------:|:-----------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml)  | Detection：192x192<br>Keypoint：128x96  | Detection mAP：29.0<br>Keypoint AP：58.1 | Detection：2.37ms<br>Keypoint：3.27ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb)  |
-| [PicoDet-S-Pedestrian](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | Detection：320x320<br>Keypoint：256x192 | Detection mAP：38.5<br>Keypoint AP：68.8 | Detection：6.30ms<br>Keypoint：8.33ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
+
+
+| Detection Model                                              | Keypoint Model                        |               Input Size                |             Accuracy of COCO             |     Average Inference Time (FP16)     |             Params (M)             |             Flops (G)              |                         Model Weight                         |              Paddle-Lite Inference Model（FP16)              |
+| :----------------------------------------------------------- | :------------------------------------ | :-------------------------------------: | :--------------------------------------: | :-----------------------------------: | :--------------------------------: | :--------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
+| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tinypose_128x96.yml)  | Detection：192x192<br>Keypoint：128x96  | Detection mAP：29.0<br>Keypoint AP：58.1 | Detection：2.37ms<br>Keypoint：3.27ms | Detection：1.18<br/>Keypoint：1.36 | Detection：0.35<br/>Keypoint：0.08 | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
+| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [PP-TinyPose](./tinypose_256x192.yml) | Detection：320x320<br>Keypoint：256x192 | Detection mAP：38.5<br>Keypoint AP：68.8 | Detection：6.30ms<br>Keypoint：8.33ms | Detection：1.18<br/>Keypoint：1.36 | Detection：0.97<br/>Keypoint：0.32 | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb)<br>[Keypoint](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
+
 
 *Specific documents of PP-TinyPose, please refer to [Document]((./tiny_pose/README.md))。
 
 ### Terminal Server
 
-| Detection Model                                                                                                               | Keypoint Model                             | Input Size                            | Accuracy of COCO                       | Model Weight                                                                                                                                                                        |
-|:----------------------------------------------------------------------------------------------------------------------------- |:------------------------------------------ |:-------------------------------------:|:--------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | Detection：640x640<br>Keypoint：384x288 | Detection mAP：49.5<br>Keypoint AP：77.8 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) |
-| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | Detection：640x640<br>Keypoint：256x192 | Detection mAP：49.5<br>Keypoint AP：76.9 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) |
+| Detection Model                                              | Keypoint Model                             |               Input Size                |             Accuracy of COCO             |           Params (M)            |            Flops (G)            |                         Model Weight                         |
+| :----------------------------------------------------------- | :----------------------------------------- | :-------------------------------------: | :--------------------------------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------------------------: |
+| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_384x288.yml) | Detection：640x640<br>Keypoint：384x288 | Detection mAP：49.5<br>Keypoint AP：77.8 | Detection：54.6<br/>Keypoint：28.6 | Detection：115.8<br/>Keypoint：17.3 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) |
+| [PP-YOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml) | [HRNet-w32](./hrnet/hrnet_w32_256x192.yml) | Detection：640x640<br>Keypoint：256x192 | Detection mAP：49.5<br>Keypoint AP：76.9 | Detection：54.6<br/>Keypoint：28.6 | Detection：115.8<br/>Keypoint：7.68 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)<br>[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) |
+
 
 ## Model Zoo