# Top-down heatmap-based pose estimation Top-down methods divide the task into two stages: object detection, followed by single-object pose estimation given object bounding boxes. Instead of estimating keypoint coordinates directly, the pose estimator will produce heatmaps which represent the likelihood of being a keypoint, following the paradigm introduced in [Simple Baselines for Human Pose Estimation and Tracking](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html).
## Results and Models ### COCO Dataset Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset | Model | Input Size | AP | AR | Details and Download | | :-------------: | :--------: | :---: | :---: | :-------------------------------------------------: | | ViTPose-h | 256x192 | 0.790 | 0.840 | [vitpose_coco.md](./coco/vitpose_coco.md) | | HRNet-w48+UDP | 256x192 | 0.768 | 0.817 | [hrnet_udp_coco.md](./coco/hrnet_udp_coco.md) | | MSPN 4-stg | 256x192 | 0.765 | 0.826 | [mspn_coco.md](./coco/mspn_coco.md) | | HRNet-w48+Dark | 256x192 | 0.764 | 0.814 | [hrnet_dark_coco.md](./coco/hrnet_dark_coco.md) | | HRNet-w48 | 256x192 | 0.756 | 0.809 | [hrnet_coco.md](./coco/hrnet_coco.md) | | HRFormer-B | 256x192 | 0.754 | 0.807 | [hrformer_coco.md](./coco/hrformer_coco.md) | | RSN-50-3x | 256x192 | 0.750 | 0.814 | [rsn_coco.md](./coco/rsn_coco.md) | | CSPNeXt-l | 256x192 | 0.750 | 0.800 | [cspnext_udp_coco.md](./coco/cspnext_udp_coco.md) | | HRNet-w32 | 256x192 | 0.749 | 0.804 | [hrnet_coco.md](./coco/hrnet_coco.md) | | Swin-L | 256x192 | 0.743 | 0.798 | [swin_coco.md](./coco/swin_coco.md) | | ViTPose-s | 256x192 | 0.739 | 0.792 | [vitpose_coco.md](./coco/vitpose_coco.md) | | HRFormer-S | 256x192 | 0.738 | 0.793 | [hrformer_coco.md](./coco/hrformer_coco.md) | | Swin-B | 256x192 | 0.737 | 0.794 | [swin_coco.md](./coco/swin_coco.md) | | SEResNet-101 | 256x192 | 0.734 | 0.790 | [seresnet_coco.md](./coco/seresnet_coco.md) | | SCNet-101 | 256x192 | 0.733 | 0.789 | [scnet_coco.md](./coco/scnet_coco.md) | | ResNet-101+Dark | 256x192 | 0.733 | 0.786 | [resnet_dark_coco.md](./coco/resnet_dark_coco.md) | | CSPNeXt-m | 256x192 | 0.732 | 0.785 | [cspnext_udp_coco.md](./coco/cspnext_udp_coco.md) | | ResNetV1d-101 | 256x192 | 0.732 | 0.785 | [resnetv1d_coco.md](./coco/resnetv1d_coco.md) | | SEResNet-50 | 256x192 | 0.729 | 0.784 | [seresnet_coco.md](./coco/seresnet_coco.md) | | SCNet-50 | 256x192 | 0.728 | 0.784 | [scnet_coco.md](./coco/scnet_coco.md) | | ResNet-101 | 256x192 | 0.726 | 0.783 | [resnet_coco.md](./coco/resnet_coco.md) | | ResNeXt-101 | 256x192 | 0.726 | 0.781 | [resnext_coco.md](./coco/resnext_coco.md) | | HourglassNet | 256x256 | 0.726 | 0.780 | [hourglass_coco.md](./coco/hourglass_coco.md) | | ResNeSt-101 | 256x192 | 0.725 | 0.781 | [resnest_coco.md](./coco/resnest_coco.md) | | RSN-50 | 256x192 | 0.724 | 0.790 | [rsn_coco.md](./coco/rsn_coco.md) | | Swin-T | 256x192 | 0.724 | 0.782 | [swin_coco.md](./coco/swin_coco.md) | | MSPN 1-stg | 256x192 | 0.723 | 0.788 | [mspn_coco.md](./coco/mspn_coco.md) | | ResNetV1d-50 | 256x192 | 0.722 | 0.777 | [resnetv1d_coco.md](./coco/resnetv1d_coco.md) | | ResNeSt-50 | 256x192 | 0.720 | 0.775 | [resnest_coco.md](./coco/resnest_coco.md) | | ResNet-50 | 256x192 | 0.718 | 0.774 | [resnet_coco.md](./coco/resnet_coco.md) | | ResNeXt-50 | 256x192 | 0.715 | 0.771 | [resnext_coco.md](./coco/resnext_coco.md) | | PVT-S | 256x192 | 0.714 | 0.773 | [pvt_coco.md](./coco/pvt_coco.md) | | CSPNeXt-s | 256x192 | 0.697 | 0.753 | [cspnext_udp_coco.md](./coco/cspnext_udp_coco.md) | | LiteHRNet-30 | 256x192 | 0.676 | 0.736 | [litehrnet_coco.md](./coco/litehrnet_coco.md) | | CSPNeXt-tiny | 256x192 | 0.665 | 0.723 | [cspnext_udp_coco.md](./coco/cspnext_udp_coco.md) | | MobileNet-v2 | 256x192 | 0.648 | 0.709 | [mobilenetv2_coco.md](./coco/mobilenetv2_coco.md) | | LiteHRNet-18 | 256x192 | 0.642 | 0.705 | [litehrnet_coco.md](./coco/litehrnet_coco.md) | | CPM | 256x192 | 0.627 | 0.689 | [cpm_coco.md](./coco/cpm_coco.md) | | ShuffleNet-v2 | 256x192 | 0.602 | 0.668 | [shufflenetv2_coco.md](./coco/shufflenetv2_coco.md) | | ShuffleNet-v1 | 256x192 | 0.587 | 0.654 | [shufflenetv1_coco.md](./coco/shufflenetv1_coco.md) | | AlexNet | 256x192 | 0.448 | 0.521 | [alexnet_coco.md](./coco/alexnet_coco.md) | ### MPII Dataset | Model | Input Size | PCKh@0.5 | PCKh@0.1 | Details and Download | | :------------: | :--------: | :------: | :------: | :-------------------------------------------------: | | HRNet-w48+Dark | 256x256 | 0.905 | 0.360 | [hrnet_dark_mpii.md](./mpii/hrnet_dark_mpii.md) | | HRNet-w48 | 256x256 | 0.902 | 0.303 | [hrnet_mpii.md](./mpii/cspnext_udp_mpii.md) | | HRNet-w48 | 256x256 | 0.901 | 0.337 | [hrnet_mpii.md](./mpii/hrnet_mpii.md) | | HRNet-w32 | 256x256 | 0.900 | 0.334 | [hrnet_mpii.md](./mpii/hrnet_mpii.md) | | HourglassNet | 256x256 | 0.889 | 0.317 | [hourglass_mpii.md](./mpii/hourglass_mpii.md) | | ResNet-152 | 256x256 | 0.889 | 0.303 | [resnet_mpii.md](./mpii/resnet_mpii.md) | | ResNetV1d-152 | 256x256 | 0.888 | 0.300 | [resnetv1d_mpii.md](./mpii/resnetv1d_mpii.md) | | SCNet-50 | 256x256 | 0.888 | 0.290 | [scnet_mpii.md](./mpii/scnet_mpii.md) | | ResNeXt-152 | 256x256 | 0.887 | 0.294 | [resnext_mpii.md](./mpii/resnext_mpii.md) | | SEResNet-50 | 256x256 | 0.884 | 0.292 | [seresnet_mpii.md](./mpii/seresnet_mpii.md) | | ResNet-50 | 256x256 | 0.882 | 0.286 | [resnet_mpii.md](./mpii/resnet_mpii.md) | | ResNetV1d-50 | 256x256 | 0.881 | 0.290 | [resnetv1d_mpii.md](./mpii/resnetv1d_mpii.md) | | CPM | 368x368\* | 0.876 | 0.285 | [cpm_mpii.md](./mpii/cpm_mpii.md) | | LiteHRNet-30 | 256x256 | 0.869 | 0.271 | [litehrnet_mpii.md](./mpii/litehrnet_mpii.md) | | LiteHRNet-18 | 256x256 | 0.859 | 0.260 | [litehrnet_mpii.md](./mpii/litehrnet_mpii.md) | | MobileNet-v2 | 256x256 | 0.854 | 0.234 | [mobilenetv2_mpii.md](./mpii/mobilenetv2_mpii.md) | | ShuffleNet-v2 | 256x256 | 0.828 | 0.205 | [shufflenetv2_mpii.md](./mpii/shufflenetv2_mpii.md) | | ShuffleNet-v1 | 256x256 | 0.824 | 0.195 | [shufflenetv1_mpii.md](./mpii/shufflenetv1_mpii.md) | ### CrowdPose Dataset Results on CrowdPose test with [YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) human detector | Model | Input Size | AP | AR | Details and Download | | :--------: | :--------: | :---: | :---: | :--------------------------------------------------------: | | HRNet-w32 | 256x192 | 0.675 | 0.816 | [hrnet_crowdpose.md](./crowdpose/hrnet_crowdpose.md) | | CSPNeXt-m | 256x192 | 0.662 | 0.755 | [hrnet_crowdpose.md](./crowdpose/cspnext_udp_crowdpose.md) | | ResNet-101 | 256x192 | 0.647 | 0.800 | [resnet_crowdpose.md](./crowdpose/resnet_crowdpose.md) | | HRNet-w32 | 256x192 | 0.637 | 0.785 | [resnet_crowdpose.md](./crowdpose/resnet_crowdpose.md) | ### AIC Dataset Results on AIC val set with ground-truth bounding boxes. | Model | Input Size | AP | AR | Details and Download | | :--------: | :--------: | :---: | :---: | :----------------------------------: | | HRNet-w32 | 256x192 | 0.323 | 0.366 | [hrnet_aic.md](./aic/hrnet_aic.md) | | ResNet-101 | 256x192 | 0.294 | 0.337 | [resnet_aic.md](./aic/resnet_aic.md) | ### JHMDB Dataset | Model | Input Size | PCK(norm. by person size) | PCK (norm. by torso size) | Details and Download | | :-------: | :--------: | :-----------------------: | :-----------------------: | :----------------------------------------: | | ResNet-50 | 256x256 | 96.0 | 80.1 | [resnet_jhmdb.md](./jhmdb/resnet_jhmdb.md) | | CPM | 368x368 | 89.8 | 65.7 | [cpm_jhmdb.md](./jhmdb/cpm_jhmdb.md) | ### PoseTrack2018 Dataset Results on PoseTrack2018 val with ground-truth bounding boxes. | Model | Input Size | AP | Details and Download | | :-------: | :--------: | :--: | :----------------------------------------------------------: | | HRNet-w48 | 256x192 | 84.6 | [hrnet_posetrack18.md](./posetrack18/hrnet_posetrack18.md) | | HRNet-w32 | 256x192 | 83.4 | [hrnet_posetrack18.md](./posetrack18/hrnet_posetrack18.md) | | ResNet-50 | 256x192 | 81.2 | [resnet_posetrack18.md](./posetrack18/resnet_posetrack18.md) |