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Keypoint Detection 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
OpenPifPaf	70.9	76.8	67.1	OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association	-
G-RMI	-	70.0	62.3	Towards Accurate Multi-person Pose Estimation in the Wild	-
Simple Base	-	80.0	70.3	Simple Baselines for Human Pose Estimation and Tracking	-
CPN	-	77.2	68.7	Cascaded Pyramid Network for Multi-Person Pose Estimation	-
HRNet	-	81.5	71.9	Deep High-Resolution Representation Learning for Human Pose Estimation	-
Mask R-CNN	-	71.4	57.8	Mask R-CNN	-
AlphaPose	-	81.5	-	RMPE: Regional Multi-person Pose Estimation	-
Simple Pose	68.1	70.5	66.8	Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation	-
DirectPose (ResNet-101)	64.8	71.5	60.4	DirectPose: Direct End-to-End Multi-Person Pose Estimation	-
Simple Base+*	-	82.7	73.0	Simple Baselines for Human Pose Estimation and Tracking	-
CPN+	-	78.1	69.5	Cascaded Pyramid Network for Multi-Person Pose Estimation	-
MSPN	76.1	81.5	72.3	Rethinking on Multi-Stage Networks for Human Pose Estimation	-
CMU Pose	-	68.2	57.1	Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields	-
HRNet*	-	83.1	73.4	Deep High-Resolution Representation Learning for Human Pose Estimation	-
AE	-	72.6	60.6	Associative Embedding: End-to-End Learning for Joint Detection and Grouping	-
PifPaf (single-scale)	66.4	72.1	62.6	PifPaf: Composite Fields for Human Pose Estimation	-

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Keypoint Detection On Coco Test Dev | SOTA | HyperAI