Face Identification On Megaface

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
Cos+UNPG	99.27%	Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition
PartialFC + Glint360K + R100	99.10%	Partial FC: Training 10 Million Identities on a Single Machine
Arc+UNPG	98.82%	Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition
Prodpoly	98.78%	Deep Polynomial Neural Networks
GhostFaceNetV2-1	98.64%	GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
ArcFace + MS1MV2 + R100 + R	98.35%	ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Mag+UNPG	98.03%	Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition
SV-AM-Softmax	97.2%	Support Vector Guided Softmax Loss for Face Recognition
CosFace	82.72%	CosFace: Large Margin Cosine Loss for Deep Face Recognition
SphereFace (3-patch ensemble)	75.766%	SphereFace: Deep Hypersphere Embedding for Face Recognition
Light CNN-29	73.749%	A Light CNN for Deep Face Representation with Noisy Labels
SphereFace (single model)	72.729%	SphereFace: Deep Hypersphere Embedding for Face Recognition
FaceNet	70.49%	FaceNet: A Unified Embedding for Face Recognition and Clustering

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Face Identification On Megaface | SOTA | HyperAI