-[Training on single GPU](#training-on-single-gpu)
-[Training on multiple GPU](#training-on-multiple-gpu)
-[Evaluation](#evaluation)
-[Inference](#inference)
-[Deploy Inference](#deploy-inference)
-[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)
## Introduction
- The keypoint detection part in PaddleDetection follows the state-of-the-art algorithm closely, including Top-Down and BottomUp methods, which can meet the different needs of users.
The keypoint detection part in PaddleDetection follows the state-of-the-art algorithm closely, including Top-Down and Bottom-Up methods, which can satisfy the different needs of users.
Top-Down detects the object first and then detect the specific keypoint. The accuracy of Top-Down models will be higher, but the time required will increase by the number of objects.
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.
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<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)|
@@ -44,11 +79,12 @@ We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint de
## Getting Start
### 1.Environmental Installation
### 1.Environmental Installation
Please refer to [PaddleDetection Installation Guild](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/INSTALL.md) to install PaddlePaddle and PaddleDetection correctly.
### 2. Dataset Preparation
### 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.
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@@ -56,9 +92,9 @@ We also release [PP-TinyPose](./tiny_pose/README_en.md), a real-time keypoint de
- 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：Top-down models only support inference for a cropped image with single person. If you want to do inference on image with several people, please see "joint inference by detection and keypoint". Or you can choose a Bottom-up model.
#keypoint inference for a single model of top-down/bottom-up method. In this mode, top-down model only support inference for a cropped image with single person.
To export MOT model, please refer to [Here](../../configs/mot/README_en.md).
### 4、Deploy standalone
We provide standalone deploy of PaddleInference(Server-GPU)、PaddleLite(mobile、ARM)、Third-Engine(MNN、OpenVino), which is independent of training codes。For detail, please click [Deploy-docs](../../deploy/README_en.md)。
## Benchmark
We provide benchmarks in different runtime environments for your reference when choosing models. See [Keypoint Inference Benchmark](./KeypointBenchmark.md) for details.
