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Models/YOLO Para

YOLO Para

Reported on 10 benchmarks across 5 tasks

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

Methodology20 results

  • 3DonM5-Malaria Dataset
    AP
    71
  • 3DonMP-IDB
    AP
    86.5
    best: 95.1
  • 3DonMP-IDB
    AP
    88.3
    best: 95.1
  • 3DonMP-IDB
    AP
    94.9
    best: 95.1
  • 3DonMP-IDB
    AP
    95.1
  • 2D ClassificationonM5-Malaria Dataset
    AP
    71
  • 2D ClassificationonMP-IDB
    AP
    86.5
    best: 95.1
  • 2D ClassificationonMP-IDB
    AP
    88.3
    best: 95.1
  • 2D ClassificationonMP-IDB
    AP
    94.9
    best: 95.1
  • 2D ClassificationonMP-IDB
    AP
    95.1
  • 2D Object DetectiononM5-Malaria Dataset
    AP
    71
  • 2D Object DetectiononMP-IDB
    AP
    86.5
    best: 95.1
  • 2D Object DetectiononMP-IDB
    AP
    88.3
    best: 95.1
  • 2D Object DetectiononMP-IDB
    AP
    94.9
    best: 95.1
  • 2D Object DetectiononMP-IDB
    AP
    95.1
  • 16konM5-Malaria Dataset
    AP
    71
  • 16konMP-IDB
    AP
    86.5
    best: 95.1
  • 16konMP-IDB
    AP
    88.3
    best: 95.1
  • 16konMP-IDB
    AP
    94.9
    best: 95.1
  • 16konMP-IDB
    AP
    95.1

Computer Vision5 results

  • Object DetectiononM5-Malaria Dataset
    AP
    71
  • Object DetectiononMP-IDB
    AP
    86.5
    best: 95.1
  • Object DetectiononMP-IDB
    AP
    88.3
    best: 95.1
  • Object DetectiononMP-IDB
    AP
    94.9
    best: 95.1
  • Object DetectiononMP-IDB
    AP
    95.1