- 请注意，Top-Down方案使用检测框测试时，需要通过检测模型生成bbox.json文件。COCO val2017的检测结果可以参考[Detector having human AP of 56.4 on COCO val2017 dataset](https://paddledet.bj.bcebos.com/data/bbox.json)，下载后放在根目录（PaddleDetection）下，然后修改config配置文件中`use_gt_bbox: False`后生效。然后正常执行测试命令即可。
- 请注意，Top-Down方案使用检测框测试时，需要通过检测模型生成bbox.json文件。COCO val2017的检测结果可以参考[Detector having human AP of 56.4 on COCO val2017 dataset](https://paddledet.bj.bcebos.com/data/bbox.json)，下载后放在根目录（PaddleDetection）下，然后修改config配置文件中`use_gt_bbox: False`后生效。然后正常执行测试命令即可。
-[Deployment for Top-Down models](#deployment-for-top-down-models)
-[Deployment for Bottom-Up models](#deployment-for-bottom-up-models)
-[joint inference with Multi-Object Tracking model FairMOT](#joint-inference-with-multi-object-tracking-model-fairmot)
-[Joint Inference with Multi-Object Tracking Model FairMOT](#joint-inference-with-multi-object-tracking-model-fairmot)
-[BenchMark](#benchmark)
## Introduction
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@@ -27,29 +29,32 @@ Top-Down detects the object first and then detect the specific keypoint. The acc
Differently, Bottom-Up detects the point first and then group or connect those points to form several instances of human pose. The speed of Bottom-Up is fixed and will not increase by the number of objects, but the accuracy will be lower.
At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README.md) specially for mobile devices.
At the same time, PaddleDetection provides [PP-TinyPose](./tiny_pose/README_en.md) specially for mobile devices.
<divalign="center">
<imgsrc="./football_keypoint.gif"width='800'/>
</div>
## Model Recommendation
### Mobile Terminal
|Detection Model| Keypoint Model | Input Size | Accuracy of COCO| Average Inference Time (FP16) | Model Weight | Paddle-Lite Inference Model（FP16)|
| Detection Model | Keypoint Model | Input Size | Accuracy of COCO | Average Inference Time (FP16) | Model Weight | Paddle-Lite Inference Model（FP16) |
We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint detection model optimized for mobile devices. Welcome to experience.
## Getting Start
### 1.Environmental Installation
Please refer to [PaddleDetection Installation Guild](../../docs/tutorials/INSTALL.md) to install PaddlePaddle and PaddleDetection correctly.
Please refer to [PaddleDetection Installation Guide](../../docs/tutorials/INSTALL.md) to install PaddlePaddle and PaddleDetection correctly.
### 2.Dataset Preparation
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@@ -89,7 +92,7 @@ We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint de
About the description for config files, please refer to [Keypoint Config Guild](../../docs/tutorials/KeyPointConfigGuide_en.md).
- Note that, when testing by detected bounding boxes in Top-Down method, We should get `bbox.json` by a detection model. You can download the detected results for COCO val2017 [(Detector having human AP of 56.4 on COCO val2017 dataset)](https://paddledet.bj.bcebos.com/data/bbox.json) directly, put it at the root path (`PaddleDetection/`), and set `use_gt_bbox: False` in config file.
- Note that, when testing by detected bounding boxes in Top-Down method, We should get `bbox.json` by a detection model. You can download the detected results for COCO val2017 [(Detector having human AP of 56.4 on COCO val2017 dataset)](https://paddledet.bj.bcebos.com/data/bbox.json) directly, put it at the root path (`PaddleDetection/`), and set `use_gt_bbox: False` in config file.
#joint inference with Multi-Object Tracking model FairMOT
python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU
```
**Note:**
To export MOT model, please refer to [Here](../../configs/mot/README_en.md).
## BenchMark
We provide benchmarks in different runtime environments for your reference when choosing models. See [Keypoint Inference Benchmark](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/keypoint/KeypointBenchmark.md) for details.
## Reference
```
@inproceedings{cheng2020bottom,
title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
