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Face Verification On Megaface

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
ElasticFace-Arc	98.81%	ElasticFace: Elastic Margin Loss for Deep Face Recognition
PFEfuse + match	92.51%	Probabilistic Face Embeddings
Dynamic AdaCos	97.41%	AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
SV-AM-Softmax	97.38%	Support Vector Guided Softmax Loss for Face Recognition
SphereFace (3-patch ensemble)	89.142%	SphereFace: Deep Hypersphere Embedding for Face Recognition
Prodpoly	98.95%	Deep Polynomial Neural Networks
CosFace	96.65%	CosFace: Large Margin Cosine Loss for Deep Face Recognition
DiscFace	97.44%	DiscFace: Minimum Discrepancy Learning for Deep Face Recognition	-
GhostFaceNetV2-1	98.72%	GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
ArcFace + MS1MV2 + R100 + R	98.48%	ArcFace: Additive Angular Margin Loss for Deep Face Recognition
SphereFace (single model)	85.561%	SphereFace: Deep Hypersphere Embedding for Face Recognition
Light CNN-29	85.133%	A Light CNN for Deep Face Representation with Noisy Labels

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