Face Verification On Megaface
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| Prodpoly | 98.95% | Deep Polynomial Neural Networks |
| ElasticFace-Arc | 98.81% | ElasticFace: Elastic Margin Loss 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 |
| DiscFace | 97.44% | DiscFace: Minimum Discrepancy Learning for Deep Face Recognition |
| 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 |
| CosFace | 96.65% | CosFace: Large Margin Cosine Loss for Deep Face Recognition |
| PFEfuse + match | 92.51% | Probabilistic Face Embeddings |
| SphereFace (3-patch ensemble) | 89.142% | SphereFace: Deep Hypersphere Embedding for 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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