Skip to content

P
PaddleDetection

PaddlePaddle / PaddleDetection
1 年多 前同步成功

通知 696
Star 11112
Fork 2696
P
PaddleDetection
未验证 提交 cabd67f0 编写于 作者: J JYChen 提交者： GitHub
浏览文件
操作

fix some deadlink (#6430)

上级 06f22754
隐藏空白更改
内联 并排
Showing with 11 addition and 11 deletion
configs/keypoint/README.md
浏览文件 @ cabd67f0
......@@ -40,11 +40,11 @@ PaddleDetection 关键点检测能力紧跟业内最新最优算法方案，包
| 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_256x192.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) |
| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tiny_pose/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](./tiny_pose/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_256x192.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))。
*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
......@@ -97,7 +97,7 @@ MPII数据集
### 2、数据准备
​ 目前KeyPoint模型支持[COCO](https://cocodataset.org/#keypoints-2017)数据集和[MPII](http://human-pose.mpi-inf.mpg.de/#overview)数据集，数据集的准备方式请参考[关键点数据准备](../../docs/tutorials/PrepareKeypointDataSet_cn.md)。
​ 目前KeyPoint模型支持[COCO](https://cocodataset.org/#keypoints-2017)数据集和[MPII](http://human-pose.mpi-inf.mpg.de/#overview)数据集，数据集的准备方式请参考[关键点数据准备](../../docs/tutorials/data/PrepareDetDataSet.md)。
​ 关于config配置文件内容说明请参考[关键点配置文件说明](../../docs/tutorials/KeyPointConfigGuide_cn.md)。
......@@ -191,7 +191,7 @@ python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inferenc
## 自定义数据训练
我们以[tinypose_256x192](.tiny_pose/README.md)为例来说明对于自定义数据如何修改：
我们以[tinypose_256x192](./tiny_pose/README.md)为例来说明对于自定义数据如何修改：
#### 1、配置文件[tinypose_256x192.yml](../../configs/keypoint/tiny_pose/tinypose_256x192.yml)
......
configs/keypoint/README_en.md
浏览文件 @ cabd67f0
......@@ -49,11 +49,11 @@ At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README_en.m
| 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_256x192.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) |
| [PicoDet-S-Pedestrian](../picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [PP-TinyPose](./tiny_pose/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](./tiny_pose/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_256x192.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))。
*Specific documents of PP-TinyPose, please refer to [Document](./tiny_pose/README.md)。
### Terminal Server
......@@ -93,7 +93,7 @@ MPII Dataset
Model for Scenes
| Model | Strategy | Input Size | Precision | Inference Speed |Model Weights | Model Inference and Deployment | description|
| :---- | ---|----- | :--------: | :-------: |:------------: |:------------: |:-------------------: |
| HRNet-w32 + DarkPose | Top-Down|256x192 | AP: 87.1 (on internal dataset)| 2.9ms per person |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) | Especially optimized for fall scenarios, the model is applied to [PP-Human](../../deploy/pipeline/README_en.md) |
| HRNet-w32 + DarkPose | Top-Down|256x192 | AP: 87.1 (on internal dataset)| 2.9ms per person |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) | Especially optimized for fall scenarios, the model is applied to [PP-Human](../../deploy/pipeline/README.md) |
We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint detection model optimized for mobile devices. Welcome to experience.
......@@ -106,7 +106,7 @@ We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint de
### 2.Dataset Preparation
​ Currently, KeyPoint Detection Models support [COCO](https://cocodataset.org/#keypoints-2017) and [MPII](http://human-pose.mpi-inf.mpg.de/#overview). Please refer to [Keypoint Dataset Preparation](../../docs/tutorials/PrepareKeypointDataSet_en.md) to prepare dataset.
​ Currently, KeyPoint Detection Models support [COCO](https://cocodataset.org/#keypoints-2017) and [MPII](http://human-pose.mpi-inf.mpg.de/#overview). Please refer to [Keypoint Dataset Preparation](../../docs/tutorials/data/PrepareDetDataSet_en.md) to prepare dataset.
​ About the description for config files, please refer to [Keypoint Config Guild](../../docs/tutorials/KeyPointConfigGuide_en.md).
......@@ -201,7 +201,7 @@ python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inferenc
## Train with custom data
We take an example of [tinypose_256x192](.tiny_pose/README_en.md) to show how to train with custom data.
We take an example of [tinypose_256x192](./tiny_pose/README_en.md) to show how to train with custom data.
#### 1、For configs [tinypose_256x192.yml](../../configs/keypoint/tiny_pose/tinypose_256x192.yml)
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或