From 4f20cd582da2b06baccf052a19168874bd5368d6 Mon Sep 17 00:00:00 2001 From: JYChen Date: Fri, 19 Nov 2021 14:11:15 +0800 Subject: [PATCH] update tinypose readme (#4621) --- configs/keypoint/tiny_pose/README.md | 18 ++++++++++++++---- docs/images/tinypose_demo.png | Bin 0 -> 2520527 bytes docs/images/tinypose_pipeline.png | Bin 0 -> 524348 bytes 3 files changed, 14 insertions(+), 4 deletions(-) create mode 100644 docs/images/tinypose_demo.png create mode 100644 docs/images/tinypose_pipeline.png diff --git a/configs/keypoint/tiny_pose/README.md b/configs/keypoint/tiny_pose/README.md index 043fe7a50..d8d34485b 100644 --- a/configs/keypoint/tiny_pose/README.md +++ b/configs/keypoint/tiny_pose/README.md @@ -1,5 +1,10 @@ # PP-TinyPose +
+ +
图片来源:COCO2017开源数据集
+
+ ## 简介 PP-TinyPose是PaddleDetecion针对移动端设备优化的实时姿态检测模型,可流畅地在移动端设备上执行多人姿态估计任务。借助PaddleDetecion自研的优秀轻量级检测模型[PicoDet](../../picodet/README.md),我们同时提供了特色的轻量级垂类行人检测模型。TinyPose的运行环境有以下依赖要求: - [PaddlePaddle](https://github.com/PaddlePaddle/Paddle)>=2.2 @@ -7,15 +12,20 @@ PP-TinyPose是PaddleDetecion针对移动端设备优化的实时姿态检测模 如希望在移动端部署,则还需要: - [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite)>=2.10 + +
+ +
+ ## 模型库 ### 姿态检测模型 -| 模型 | 输入尺寸 | AP (coco val) | 单人推理耗时 (FP32)| 单人推理耗时(FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型(FP32) | Paddle-Lite部署模型(FP16)| +| 模型 | 输入尺寸 | AP (COCO Val) | 单人推理耗时 (FP32)| 单人推理耗时(FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型(FP32) | Paddle-Lite部署模型(FP16)| | :------------------------ | :-------: | :------: | :------: |:---: | :---: | :---: | :---: | :---: | :---: | | PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) | -| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.nb) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) | +| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) | ### 行人检测模型 -| 模型 | 输入尺寸 | mAP (coco val) | 平均推理耗时 (FP32) | 平均推理耗时 (FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型(FP32) | Paddle-Lite部署模型(FP16)| +| 模型 | 输入尺寸 | mAP (COCO Val) | 平均推理耗时 (FP32) | 平均推理耗时 (FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型(FP32) | Paddle-Lite部署模型(FP16)| | :------------------------ | :-------: | :------: | :------: | :---: | :---: | :---: | :---: | :---: | :---: | | PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms | 2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb) | | PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms | 6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb) | @@ -28,7 +38,7 @@ PP-TinyPose是PaddleDetecion针对移动端设备优化的实时姿态检测模 - 推理速度测试环境为 Qualcomm Snapdragon 865,采用arm8下4线程推理得到。 ### Pipeline性能 -| 行人检测模型 | 姿态检测模型 | mAP (coco val) | 单人耗时 (FP32) | 单人耗时 (FP16) | 6人耗时 (FP32) | 6人耗时 (FP16)| +| 行人检测模型 | 姿态检测模型 | mAP (COCO Val) | 单人耗时 (FP32) | 单人耗时 (FP16) | 6人耗时 (FP32) | 6人耗时 (FP16)| | :------------------------ | :-------: | :------: | :---: | :---: | :---: | :---: | | PicoDet-S-Pedestrian-192*192 | PP-TinyPose-128*96 | 36.7 | 11.72 ms| 8.18 ms | 36.22 ms| 26.33 ms | | PicoDet-S-Pedestrian-320*320 | PP-TinyPose-128*96 | 44.2 | 19.45 ms| 14.41 ms | 44.0 ms| 32.57 ms | diff --git a/docs/images/tinypose_demo.png b/docs/images/tinypose_demo.png new file mode 100644 index 0000000000000000000000000000000000000000..17dfcc9a16cc8e4afdf7f5f05b610c4b322ebd1f GIT binary 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