Lightweight Face Recognition On Lfw
Métriques
Accuracy
MFLOPs
MParams
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | MFLOPs | MParams | Paper Title | Repository |
|---|---|---|---|---|---|
| EdgeFace - XS (g=0.6) | 0.9973 | 154 | 1.77 | EdgeFace: Efficient Face Recognition Model for Edge Devices | |
| PocketNetS | 0.9966 | 587.24 | 0.99 | PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation | |
| EdgeFace - S (g=0.5) | 0.9978 | 306.11 | 3.65 | EdgeFace: Efficient Face Recognition Model for Edge Devices | |
| Seesaw-shuffleFaceNet(mobi) | 0.9965 | - | 2.8 | SeesawFaceNets: sparse and robust face verification model for mobile platform | |
| MixFaceNet-S | 0.996 | 451.7 | 3.07 | MixFaceNets: Extremely Efficient Face Recognition Networks | |
| MobileFaceNet | 0.9928 | - | - | MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices |
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