Unverified50% confidenceBenchmarkExact time
At the full VTF900 gallery scale (N=900) under frontal conditions, the ViT-B model achieves 97.68% AUC and 51.10% TAR@1%FAR.
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10/20/2026
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UnverifiedThe VTF900 study shows that high AUC under small-scale galleries does not translate into reliable performance at deployment scale, and model rankings derived from hundred-person-scale galleries may undergo substantial reordering at the 900-person scale.69% similarUnverifiedThe model's recall for high/VP attachment (N1) is as high as 99.71%, while its recall for low/NP/embedded attachment (N2) is only 89.26%.68% similarUnverifiedOn synthetic data with 10,000 samples, a 1% positive rate, and 495 negatives scoring 0.90 while the positives score 0.80 and 0.60 respectively, computing with a 0.5 threshold gives: ROC-AUC=0.9500, Average Precision=0.1449, precision=0.1681, recall=1.0000, accuracy=0.9505, TP=100, FP=495, TN=9405, FN=0.64% similarUnverifiedMainstream GPU inference solutions based on the NVIDIA H100 can typically only achieve tens to one or two hundred tokens per second on models of equivalent scale.64% similarUnverifiedAt the mention level, the model achieved an overall precision of 74.1% and a recall of 70.5%.63% similar
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