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Models/LMTCNN-2-1 (single crop, tensorflow)

LMTCNN-2-1 (single crop, tensorflow)

Reported on 12 benchmarks across 6 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision4 results

  • Face ReconstructiononAdience Gender
    Accuracy (5-fold)· 2018-06-06
    85.16
    best: 97.39 (MiVOLO-V2)
    SOTA
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023
  • 3D Face ReconstructiononAdience Gender
    Accuracy (5-fold)· 2018-06-06
    85.16
    best: 97.39 (MiVOLO-V2)
    SOTA
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023
  • Face ReconstructiononAdience Age
    Accuracy (5-fold)· 2018-06-06
    44.26
    best: 84.91 (ViT-hSeq)
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023
  • 3D Face ReconstructiononAdience Age
    Accuracy (5-fold)· 2018-06-06
    44.26
    best: 84.91 (ViT-hSeq)
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023

Music2 results

  • Facial Recognition and ModellingonAdience Gender
    Accuracy (5-fold)· 2018-06-06
    85.16
    best: 97.39 (MiVOLO-V2)
    SOTA
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023
  • Facial Recognition and ModellingonAdience Age
    Accuracy (5-fold)· 2018-06-06
    44.26
    best: 84.91 (ViT-hSeq)
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023

Methodology2 results

  • 3DonAdience Gender
    Accuracy (5-fold)· 2018-06-06
    85.16
    best: 97.39 (MiVOLO-V2)
    SOTA
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023
  • 3DonAdience Age
    Accuracy (5-fold)· 2018-06-06
    44.26
    best: 84.91 (ViT-hSeq)
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023

Medical2 results

  • 3D Face ModellingonAdience Gender
    Accuracy (5-fold)· 2018-06-06
    85.16
    best: 97.39 (MiVOLO-V2)
    SOTA
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023
  • 3D Face ModellingonAdience Age
    Accuracy (5-fold)· 2018-06-06
    44.26
    best: 84.91 (ViT-hSeq)
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023

Natural Language Processing2 results

  • Age And Gender ClassificationonAdience Gender
    Accuracy (5-fold)· 2018-06-06
    85.16
    best: 97.39 (MiVOLO-V2)
    SOTA
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023
  • Age And Gender ClassificationonAdience Age
    Accuracy (5-fold)· 2018-06-06
    44.26
    best: 84.91 (ViT-hSeq)
    Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsarXiv:1806.02023