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Models/MobileNetV2+KD-Loss

MobileNetV2+KD-Loss

Reported on 15 benchmarks across 5 tasks · 1 paper

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

Computer Vision6 results

  • Face Reconstructionon300W
    NME_inter-ocular (%, Challenge)· 2021-11-13
    6.13
    best: 8.2 (ASMNet)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • Face Reconstructionon300W
    NME_inter-ocular (%, Common)· 2021-11-13
    3.56
    best: 5.09 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • Face Reconstructionon300W
    NME_inter-ocular (%, Full)· 2021-11-13
    4.06
    best: 5.63 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • 3D Face Reconstructionon300W
    NME_inter-ocular (%, Challenge)· 2021-11-13
    6.13
    best: 8.2 (ASMNet)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • 3D Face Reconstructionon300W
    NME_inter-ocular (%, Common)· 2021-11-13
    3.56
    best: 5.09 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • 3D Face Reconstructionon300W
    NME_inter-ocular (%, Full)· 2021-11-13
    4.06
    best: 5.63 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047

Music3 results

  • Facial Recognition and Modellingon300W
    NME_inter-ocular (%, Challenge)· 2021-11-13
    6.13
    best: 8.2 (ASMNet)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • Facial Recognition and Modellingon300W
    NME_inter-ocular (%, Common)· 2021-11-13
    3.56
    best: 5.09 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • Facial Recognition and Modellingon300W
    NME_inter-ocular (%, Full)· 2021-11-13
    4.06
    best: 5.63 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047

Methodology3 results

  • 3Don300W
    NME_inter-ocular (%, Challenge)· 2021-11-13
    6.13
    best: 8.2 (ASMNet)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • 3Don300W
    NME_inter-ocular (%, Common)· 2021-11-13
    3.56
    best: 5.09 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • 3Don300W
    NME_inter-ocular (%, Full)· 2021-11-13
    4.06
    best: 5.63 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047

Medical3 results

  • 3D Face Modellingon300W
    NME_inter-ocular (%, Challenge)· 2021-11-13
    6.13
    best: 8.2 (ASMNet)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • 3D Face Modellingon300W
    NME_inter-ocular (%, Common)· 2021-11-13
    3.56
    best: 5.09 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047
  • 3D Face Modellingon300W
    NME_inter-ocular (%, Full)· 2021-11-13
    4.06
    best: 5.63 (3DDFA)
    Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksarXiv:2111.07047