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Face Identification On Megaface

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
GhostFaceNetV2-198.64%GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
CosFace82.72%CosFace: Large Margin Cosine Loss for Deep Face Recognition-
Cos+UNPG99.27%Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition-
PartialFC + Glint360K + R10099.10%Partial FC: Training 10 Million Identities on a Single Machine-
SphereFace (3-patch ensemble)75.766%SphereFace: Deep Hypersphere Embedding for Face Recognition-
Mag+UNPG98.03%Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition-
ArcFace + MS1MV2 + R100 + R98.35%ArcFace: Additive Angular Margin Loss for Deep Face Recognition-
Prodpoly98.78%Deep Polynomial Neural Networks-
FaceNet70.49%FaceNet: A Unified Embedding for Face Recognition and Clustering-
Light CNN-2973.749%A Light CNN for Deep Face Representation with Noisy Labels-
SV-AM-Softmax97.2%Support Vector Guided Softmax Loss for Face Recognition-
SphereFace (single model)72.729%SphereFace: Deep Hypersphere Embedding for Face Recognition-
Arc+UNPG98.82%Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition-
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