From f70a80fcd40aa0353b03cb7d973e9bbcf2f3b629 Mon Sep 17 00:00:00 2001 From: zhiboniu Date: Wed, 13 Apr 2022 06:41:28 +0000 Subject: [PATCH] keypoint docs add flops¶ms; test=document_fix --- configs/keypoint/README.md | 19 +++++++++++-------- configs/keypoint/README_en.md | 22 ++++++++++++++-------- 2 files changed, 25 insertions(+), 16 deletions(-) diff --git a/configs/keypoint/README.md b/configs/keypoint/README.md index dcec551b9..7a42c1833 100644 --- a/configs/keypoint/README.md +++ b/configs/keypoint/README.md @@ -34,20 +34,23 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案，包 ## 模型推荐 ### 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) | 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
关键点：128x96 | 检测mAP：29.0
关键点AP：58.1 | 检测耗时：2.37ms
关键点耗时：3.27ms | 检测：1.18
关键点：1.36 | 检测：0.35
关键点：0.08 | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[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)
[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
关键点：256x192 | 检测mAP：38.5
关键点AP：68.8 | 检测耗时：6.30ms
关键点耗时：8.33ms | 检测：1.18
关键点：1.36 | 检测：0.97
关键点：0.32 | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[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)
[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) | 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
关键点：128x96 | 检测mAP：29.0
关键点AP：58.1 | 检测耗时：2.37ms
关键点耗时：3.27ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[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)
[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
关键点：256x192 | 检测mAP：38.5
关键点AP：68.8 | 检测耗时：6.30ms
关键点耗时：8.33ms | [检测](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[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)
[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
关键点：384x288 | 检测mAP：49.5
关键点AP：77.8 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[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
关键点：256x192 | 检测mAP：49.5
关键点AP：76.9 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[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
关键点：384x288 | 检测mAP：49.5
关键点AP：77.8 | 检测：54.6
关键点：28.6 | 检测：115.8
关键点：17.3 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[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
关键点：256x192 | 检测mAP：49.5
关键点AP：76.9 | 检测：54.6
关键点：28.6 | 检测：115.8
关键点：7.68 | [检测](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[关键点](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | + + +## 模型库 ## 模型库 COCO数据集 diff --git a/configs/keypoint/README_en.md b/configs/keypoint/README_en.md index 130a7dfd9..96b6b87e2 100644 --- a/configs/keypoint/README_en.md +++ b/configs/keypoint/README_en.md @@ -36,19 +36,25 @@ At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README.md) ## Model Recommendation ### 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
Keypoint：128x96 | Detection mAP：29.0
Keypoint AP：58.1 | Detection：2.37ms
Keypoint：3.27ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[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)
[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
Keypoint：256x192 | Detection mAP：38.5
Keypoint AP：68.8 | Detection：6.30ms
Keypoint：8.33ms | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[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)
[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
Keypoint：128x96 | Detection mAP：29.0
Keypoint AP：58.1 | Detection：2.37ms
Keypoint：3.27ms | Detection：1.18
Keypoint：1.36 | Detection：0.35
Keypoint：0.08 | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams)
[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)
[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
Keypoint：256x192 | Detection mAP：38.5
Keypoint AP：68.8 | Detection：6.30ms
Keypoint：8.33ms | Detection：1.18
Keypoint：1.36 | Detection：0.97
Keypoint：0.32 | [Detection](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams)
[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)
[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))。 ### Teminal 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
Keypoint：384x288 | Detection mAP：49.5
Keypoint AP：77.8 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[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
Keypoint：256x192 | Detection mAP：49.5
Keypoint AP：76.9 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[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
Keypoint：384x288 | Detection mAP：49.5
Keypoint AP：77.8 | Detection：54.6
Keypoint：28.6 | Detection：115.8
Keypoint：17.3 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[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
Keypoint：256x192 | Detection mAP：49.5
Keypoint AP：76.9 | Detection：54.6
Keypoint：28.6 | Detection：115.8
Keypoint：7.68 | [Detection](https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams)
[Keypoint](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | + + +## Model Zoo ## Model Zoo COCO Dataset -- GitLab