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Pose Estimation On Crowdpose

Métriques

AP

AP50

AP75

APM

Test

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AP	AP50	AP75	APM	Test	Paper Title	Repository
KAPAO-S	63.8	87.7	69.4	64.8	72.1	Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation
BUCTD-W48	72.9	-	-	-	-	Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
KAPAO-M	67.1	88.8	73.4	68.1	75.2	Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation
OpenPifPaf	70.5	89.1	76.1	-	-	OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
SwinV2-L 1K-MIM	75.5	-	-	-	-	Revealing the Dark Secrets of Masked Image Modeling
MIPNet (HRNet-W48)	70.0	-	-	71.1	-	Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
KAPAO-L	68.9	89.4	75.6	69.9	76.6	Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation
BUCTD-W48 (w/cond. input from PETR)	76.7	-	-	-	-	Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
SwinV2-B 1K-MIM	74.9	-	-	-	-	Revealing the Dark Secrets of Masked Image Modeling
BUCTD-W48 (w/cond. input from PETR, and generative sampling)	78.5	-	-	-	-	Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
Hourglass-104	65.2	85.9	69.5	66.2	-	Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation
ViTPose-G	78.3	85.3	81.4	86.6	-	ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation

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Pose Estimation On Crowdpose | SOTA | HyperAI