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SOTA
人脸识别
Face Identification On Megaface
Face Identification On Megaface
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
Repository
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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