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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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