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Models/nanggg

nanggg

Reported on 16 benchmarks across 2 tasks

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

Computer Vision16 results

  • Intelligent SurveillanceonVRAI test
    CMC1
    0.65
    best: 0.81 (Baseline Model)
  • Intelligent SurveillanceonVRAI test
    CMC10
    0.91
    best: 0.94 (Baseline Model)
  • Intelligent SurveillanceonVRAI test
    CMC5
    0.84
    best: 0.9 (Baseline Model)
  • Intelligent SurveillanceonVRAI test
    MAP
    0.62
    best: 0.79 (Baseline Model)
  • Intelligent SurveillanceonVRAI test-dev
    CMC1
    0.66
    best: 0.8 (Baseline Model)
  • Intelligent SurveillanceonVRAI test-dev
    CMC10
    0.91
    best: 0.95 (Baseline Model)
  • Intelligent SurveillanceonVRAI test-dev
    CMC5
    0.84
    best: 0.89 (Baseline Model)
  • Intelligent SurveillanceonVRAI test-dev
    MAP
    0.63
    best: 0.78 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test
    CMC1
    0.65
    best: 0.81 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test
    CMC10
    0.91
    best: 0.94 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test
    CMC5
    0.84
    best: 0.9 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test
    MAP
    0.62
    best: 0.79 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test-dev
    CMC1
    0.66
    best: 0.8 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test-dev
    CMC10
    0.91
    best: 0.95 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test-dev
    CMC5
    0.84
    best: 0.89 (Baseline Model)
  • Vehicle Re-IdentificationonVRAI test-dev
    MAP
    0.63
    best: 0.78 (Baseline Model)