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Face Identification On Megaface

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

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

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