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Multi Person Pose Estimation On Coco Test Dev

Métriques

AP

APL

APM

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AP	APL	APM	Paper Title	Repository
Hourglass-104	65.6	68.8	63.3	Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation
SPM	66.9	73.1	62.6	Single-Stage Multi-Person Pose Machines
HigherHRNet (ScaleNet_P4)	71.6	77.2	67.5	ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition	-
Identity Mapping Hourglass	68.1	70.5	66.8	Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
ResNet50	73.7	-	-	AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation
CMU-Pose	61.8	68.2	57.1	Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
HRNet-W32	76.2	-	-	AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation
PersonLab	68.7	75.5	64.1	PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
HigherHRNet (HR-Net-48)	70.5	75.8	66.6	HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
G-RMI	64.9	70.0	62.3	Towards Accurate Multi-person Pose Estimation in the Wild	-
RMPE	61.8	67.6	58.6	RMPE: Regional Multi-person Pose Estimation
SMPR (HR-Net-32)	70.2	77.2	65.9	SMPR: Single-Stage Multi-Person Pose Regression
SCIO (HRNet-48)	79.2	84.2	74.1	Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation	-
HRNet-W48plus	78.7	-	-	AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation
Supervising Self-Attention	66.5	-	-	Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association

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