Face Verification on AgeDB-30 (test)
98.82AccuracyResNet200-TopoFR
Evaluation Results
| Method | Links | |
|---|---|---|
| ResNet200-TopoFRYear='24, Param (M)=118.8, FLOPs (M)=23.5G, Training Dataset=Glint360K, Latency (ms)=12.242026.04 | 98.82 | |
| TransFace-LYear='25, Param (M)=271.6, FLOPs (M)=25.4G, Training Dataset=Glint360K, Latency (ms)=OOM2026.04 | 98.66 | |
| TransFace-BYear='25, Param (M)=124.5, FLOPs (M)=11.5G, Training Dataset=Glint360K, Latency (ms)=18.202026.04 | 98.62 | |
| TransFace-SYear='23, Param (M)=86.7, FLOPs (M)=5.8G, Training Dataset=Glint360K, Latency (ms)=14.312026.04 | 98.5 | |
| Mag+UNPGBackbone=ResNet-1002022.03 | 98.38 | |
| CurricularFaceBackbone=ResNet101, Training Dataset=MS1MV22021.05 | 98.32 | |
| Cos+UNPGBackbone=ResNet-1002022.03 | 98.31 | |
| MagFaceBackbone=ResNet-1002022.03 | 98.3 | |
| GroupFaceBackbone=ResNet101, Training Dataset=MS1MV22021.05 | 98.28 | |
| ResNet50-ArcFaceYear='22, Param (M)=43.6, FLOPs (M)=6.3G, Training Dataset=Glint360K, Latency (ms)=3.762026.04 | 98.28 | |
| Arc+UNPGBackbone=ResNet-1002022.03 | 98.25 | |
| DCQBackbone=ResNet101, Training Dataset=MS1MV22021.05 | 98.23 | |
| ArcFaceBackbone=ResNet-1002022.03 | 98.23 | |
| CosFace (ours)Backbone=ResNet101, Training Dataset=MS1MV22021.05 | 98.22 | |
| MagFace*Backbone=ResNet-1002022.03 | 98.17 | |
| PartialFC-r0.1Backbone=ResNet101, Training Dataset=MS1MV2, Sampling Rate=0.12021.05 | 98.13 | |
| CosFaceBackbone=ResNet-1002022.03 | 98.11 | |
| FaceLiVTv2-MParam (M)=7.02, FLOPs (M)=258, Training Dataset=Glint360K, Latency (ms)=0.65(18.8× ↓)2026.04 | 98.1 | |
| PartialFC-r1.0Backbone=ResNet101, Training Dataset=MS1MV2, Sampling Rate=1.02021.05 | 98.03 | |
| FaceLiVTv2-LParam (M)=8.52, FLOPs (M)=309, Training Dataset=Glint360K, Latency (ms)=0.71(17.2× ↓)2026.04 | 98.02 | |
| FaceLiVTv2-SParam (M)=4.62, FLOPs (M)=179, Training Dataset=Glint360K, Latency (ms)=0.54(22.7× ↓)2026.04 | 97.82 | |
| Seesaw-shuffleFaceNetBatch size=192, Training epoch=162019.08 | 96.85 | |
| FaceLiVTv2-XSParam (M)=2.9, FLOPs (M)=90, Training Dataset=Glint360K, Latency (ms)=0.43(28.5× ↓)2026.04 | 96.68 | |
| Seesaw-shuffleFaceNet(mobi)Batch size=224, Training epoch=162019.08 | 96.48 | |
| Seesaw-shareFaceNetBatch size=160, Training epoch=162019.08 | 96.42 | |
| MobileFaceNet (ours)Batch size=256, Training epoch=242019.08 | 96.32 | |
| Seesaw-shuffleFaceNetBatch size=160, Training epoch=162019.08 | 96.3 | |
| MobileFaceNet#Image=3.8M, Batch size=5122019.08 | 96.07 | |
| LMobileNetEBatch size=512, Params=26.7M2019.08 | 96.06 | |
| MobileFaceNet (ours)Batch size=192, Training epoch=162019.08 | 96.03 | |
| MobileFaceNet (ours)Batch size=256, Training epoch=162019.08 | 96.03 | |
| MobileFaceNet (ours)Batch size=160, Training epoch=162019.08 | 95.92 | |
| MobileFaceNet#Image=0.5M, Batch size=5122019.08 | 93.05 |