mmpose/configs/body_2d_keypoint/topdown_heatmap

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.

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
HRNet-w48+UDP	256x192	0.768	0.817	hrnet_udp_coco.md
MSPN 4-stg	256x192	0.765	0.826	mspn_coco.md
HRNet-w48+Dark	256x192	0.764	0.814	hrnet_dark_coco.md
HRNet-w48	256x192	0.756	0.809	hrnet_coco.md
HRFormer-B	256x192	0.754	0.807	hrformer_coco.md
RSN-50-3x	256x192	0.750	0.814	rsn_coco.md
CSPNeXt-l	256x192	0.750	0.800	cspnext_udp_coco.md
HRNet-w32	256x192	0.749	0.804	hrnet_coco.md
Swin-L	256x192	0.743	0.798	swin_coco.md
ViTPose-s	256x192	0.739	0.792	vitpose_coco.md
HRFormer-S	256x192	0.738	0.793	hrformer_coco.md
Swin-B	256x192	0.737	0.794	swin_coco.md
SEResNet-101	256x192	0.734	0.790	seresnet_coco.md
SCNet-101	256x192	0.733	0.789	scnet_coco.md
ResNet-101+Dark	256x192	0.733	0.786	resnet_dark_coco.md
CSPNeXt-m	256x192	0.732	0.785	cspnext_udp_coco.md
ResNetV1d-101	256x192	0.732	0.785	resnetv1d_coco.md
SEResNet-50	256x192	0.729	0.784	seresnet_coco.md
SCNet-50	256x192	0.728	0.784	scnet_coco.md
ResNet-101	256x192	0.726	0.783	resnet_coco.md
ResNeXt-101	256x192	0.726	0.781	resnext_coco.md
HourglassNet	256x256	0.726	0.780	hourglass_coco.md
ResNeSt-101	256x192	0.725	0.781	resnest_coco.md
RSN-50	256x192	0.724	0.790	rsn_coco.md
Swin-T	256x192	0.724	0.782	swin_coco.md
MSPN 1-stg	256x192	0.723	0.788	mspn_coco.md
ResNetV1d-50	256x192	0.722	0.777	resnetv1d_coco.md
ResNeSt-50	256x192	0.720	0.775	resnest_coco.md
ResNet-50	256x192	0.718	0.774	resnet_coco.md
ResNeXt-50	256x192	0.715	0.771	resnext_coco.md
PVT-S	256x192	0.714	0.773	pvt_coco.md
CSPNeXt-s	256x192	0.697	0.753	cspnext_udp_coco.md
LiteHRNet-30	256x192	0.676	0.736	litehrnet_coco.md
CSPNeXt-tiny	256x192	0.665	0.723	cspnext_udp_coco.md
MobileNet-v2	256x192	0.648	0.709	mobilenetv2_coco.md
LiteHRNet-18	256x192	0.642	0.705	litehrnet_coco.md
CPM	256x192	0.627	0.689	cpm_coco.md
ShuffleNet-v2	256x192	0.602	0.668	shufflenetv2_coco.md
ShuffleNet-v1	256x192	0.587	0.654	shufflenetv1_coco.md
AlexNet	256x192	0.448	0.521	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
HRNet-w48	256x256	0.902	0.303	hrnet_mpii.md
HRNet-w48	256x256	0.901	0.337	hrnet_mpii.md
HRNet-w32	256x256	0.900	0.334	hrnet_mpii.md
HourglassNet	256x256	0.889	0.317	hourglass_mpii.md
ResNet-152	256x256	0.889	0.303	resnet_mpii.md
ResNetV1d-152	256x256	0.888	0.300	resnetv1d_mpii.md
SCNet-50	256x256	0.888	0.290	scnet_mpii.md
ResNeXt-152	256x256	0.887	0.294	resnext_mpii.md
SEResNet-50	256x256	0.884	0.292	seresnet_mpii.md
ResNet-50	256x256	0.882	0.286	resnet_mpii.md
ResNetV1d-50	256x256	0.881	0.290	resnetv1d_mpii.md
CPM	368x368*	0.876	0.285	cpm_mpii.md
LiteHRNet-30	256x256	0.869	0.271	litehrnet_mpii.md
LiteHRNet-18	256x256	0.859	0.260	litehrnet_mpii.md
MobileNet-v2	256x256	0.854	0.234	mobilenetv2_mpii.md
ShuffleNet-v2	256x256	0.828	0.205	shufflenetv2_mpii.md
ShuffleNet-v1	256x256	0.824	0.195	shufflenetv1_mpii.md

CrowdPose Dataset

Results on CrowdPose test with YOLOv3 human detector

Model	Input Size	AP	AR	Details and Download
HRNet-w32	256x192	0.675	0.816	hrnet_crowdpose.md
CSPNeXt-m	256x192	0.662	0.755	hrnet_crowdpose.md
ResNet-101	256x192	0.647	0.800	resnet_crowdpose.md
HRNet-w32	256x192	0.637	0.785	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
ResNet-101	256x192	0.294	0.337	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
CPM	368x368	89.8	65.7	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
HRNet-w32	256x192	83.4	hrnet_posetrack18.md
ResNet-50	256x192	81.2	resnet_posetrack18.md