[简体中文](README.md) | English # KeyPoint Detection Models ## Content - [Introduction](#introduction) - [Model Recommendation](#model-recommendation) - [Model Zoo](#model-zoo) - [Getting Start](#getting-start) - [Environmental Installation](#1environmental-installation) - [Dataset Preparation](#2dataset-preparation) - [Training and Testing](#3training-and-testing) - [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) - [Complete Deploy Instruction and Demo](#complete-deploy-instruction-and-demo) - [Train with custom data](#train-with-custom-data) - [BenchMark](#benchmark) ## Introduction 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 detects the specific keypoint. Top-Down models will be more accurate, but slower as the number of objects increases. 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, it won't slow down as the number of objects increases, but it will be less accurate. At the same time, PaddleDetection provides a self-developed real-time keypoint detection model [PP-TinyPose](./tiny_pose/README_en.md) optimized for mobile devices.
## Model Recommendation ### Mobile Terminal | 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](./tiny_pose/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](./tiny_pose/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_256x192.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)。 ### Terminal Server | 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 COCO Dataset | Model | Input Size | AP(coco val) | Model Download | Config File | | :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------------- | | HigherHRNet-w32 | 512 | 67.1 | [higherhrnet_hrnet_w32_512.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512.yml) | | HigherHRNet-w32 | 640 | 68.3 | [higherhrnet_hrnet_w32_640.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_640.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_640.yml) | | HigherHRNet-w32+SWAHR | 512 | 68.9 | [higherhrnet_hrnet_w32_512_swahr.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512_swahr.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512_swahr.yml) | | HRNet-w32 | 256x192 | 76.9 | [hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams) | [config](./hrnet/hrnet_w32_256x192.yml) | | HRNet-w32 | 384x288 | 77.8 | [hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | [config](./hrnet/hrnet_w32_384x288.yml) | | HRNet-w32+DarkPose | 256x192 | 78.0 | [dark_hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_256x192.pdparams) | [config](./hrnet/dark_hrnet_w32_256x192.yml) | | HRNet-w32+DarkPose | 384x288 | 78.3 | [dark_hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_384x288.pdparams) | [config](./hrnet/dark_hrnet_w32_384x288.yml) | | WiderNaiveHRNet-18 | 256x192 | 67.6(+DARK 68.4) | [wider_naive_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/wider_naive_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/wider_naive_hrnet_18_256x192_coco.yml) | | LiteHRNet-18 | 256x192 | 66.5 | [lite_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_256x192_coco.yml) | | LiteHRNet-18 | 384x288 | 69.7 | [lite_hrnet_18_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_384x288_coco.yml) | | LiteHRNet-30 | 256x192 | 69.4 | [lite_hrnet_30_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_256x192_coco.yml) | | LiteHRNet-30 | 384x288 | 72.5 | [lite_hrnet_30_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_384x288_coco.yml) | Note：The AP results of Top-Down models are based on bounding boxes in GroundTruth. MPII Dataset | Model | Input Size | PCKh(Mean) | PCKh(Mean@0.1) | Model Download | Config File | | :---- | -------- | :--------: | :------------: | :----------------------------------------------------------: | -------------------------------------------- | | HRNet-w32 | 256x256 | 90.6 | 38.5 | [hrnet_w32_256x256_mpii.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x256_mpii.pdparams) | [config](./hrnet/hrnet_w32_256x256_mpii.yml) | 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.md) | 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 Guide](../../docs/tutorials/INSTALL.md) to install PaddlePaddle and PaddleDetection correctly. ### 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/data/PrepareKeypointDataSet_en.md) to prepare dataset. ​ 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. ### 3.Training and Testing #### Training on single GPU ```shell #COCO DataSet CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml #MPII DataSet CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml ``` #### Training on multiple GPU ```shell #COCO DataSet CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml #MPII DataSet CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml ``` #### Evaluation ```shell #COCO DataSet CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml #MPII DataSet CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml #If you only need the prediction result, you can set --save_prediction_only. Then the result will be saved at output/keypoints_results.json by default. CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml --save_prediction_only ``` #### Inference ​ 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. ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=./output/higherhrnet_hrnet_w32_512/model_final.pdparams --infer_dir=../images/ --draw_threshold=0.5 --save_txt=True ``` #### Deploy Inference ##### Deployment for Top-Down models ```shell #Export Detection Model python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams #Export Keypoint Model python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams #Deployment for detector and keypoint, which is only for Top-Down models python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../video/xxx.mp4 --device=gpu ``` ##### Deployment for Bottom-Up models ```shell #Export model python tools/export_model.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=output/higherhrnet_hrnet_w32_512/model_final.pdparams #Keypoint independent deployment, which is only for bottom-up models python deploy/python/keypoint_infer.py --model_dir=output_inference/higherhrnet_hrnet_w32_512/ --image_file=./demo/000000014439_640x640.jpg --device=gpu --threshold=0.5 ``` ##### Joint Inference with Multi-Object Tracking Model FairMOT ```shell #export FairMOT model python tools/export_model.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams #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). ### Complete Deploy Instruction and Demo ​ 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](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/README_en.md)。 ## 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) you may need to modity these for your job： ``` num_joints: &num_joints 17 #the number of joints in your job train_height: &train_height 256 #the height of model input train_width: &train_width 192 #the width of model input hmsize: &hmsize [48, 64] #the shape of model output，usually 1/4 of [w,h] flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]] #the correspondence between left and right keypoint id，used for flip transform。You can add an line(by "flip: False") behind of flip_pairs in RandomFlipHalfBodyTransform of TrainReader if you don't need it num_joints_half_body: 8 #The joint numbers of half body, used for half_body transform prob_half_body: 0.3 #The probility of half_body transform, set to 0 if you don't need it upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] #The joint ids of half(upper) body, used to get the upper joints in half_body transform ``` For more configs, please refer to [KeyPointConfigGuide](../../docs/tutorials/KeyPointConfigGuide_en.md)。 #### 2、Others(used for test and visualization) - In keypoint_utils.py, please set: "sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07,.87, .87, .89, .89]) / 10.0", the value indicate the variance of a joint locations，normally 0.25-0.5 means the location is highly accuracy，for example: eyes。0.5-1.0 means the location is not sure so much，for example: shoulder。0.75 is recommand if you not sure。 - In visualizer.py, please set "EDGES" in draw_pose function，this indicate the line to show between joints for visualization。 - In pycocotools you installed, please set "sigmas"，it is the same as that in keypoint_utils.py, but used for coco evaluation。 #### 3、Note for data preparation - The data should has the same format as Coco data, and the keypoints(Nx3) and bbox(N) should be annotated. - please set "area">0 in annotations files otherwise it will be skiped while training. Moreover, due to the evaluation mechanism of COCO, the data with small area may also be filtered out during evaluation. We recommend to set `area = bbox_w * bbox_h` when customizing your dataset. ## 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}, author={Bowen Cheng and Bin Xiao and Jingdong Wang and Honghui Shi and Thomas S. Huang and Lei Zhang}, booktitle={CVPR}, year={2020} } @inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{wang2019deep, title={Deep High-Resolution Representation Learning for Visual Recognition}, author={Wang, Jingdong and Sun, Ke and Cheng, Tianheng and Jiang, Borui and Deng, Chaorui and Zhao, Yang and Liu, Dong and Mu, Yadong and Tan, Mingkui and Wang, Xinggang and Liu, Wenyu and Xiao, Bin}, journal={TPAMI}, year={2019} } @InProceedings{Zhang_2020_CVPR, author = {Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce}, title = {Distribution-Aware Coordinate Representation for Human Pose Estimation}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020} } @inproceedings{Yulitehrnet21, title={Lite-HRNet: A Lightweight High-Resolution Network}, author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong}, booktitle={CVPR}, year={2021} } ```