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Models/LMOT

LMOT

Reported on 14 benchmarks across 2 tasks

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

Computer Vision14 results

  • Multi-Object TrackingonMOT20
    IDF1· uses extra data
    61.1
    best: 82 (BoostTrack++)
  • Multi-Object TrackingonMOT20
    MOTA· uses extra data
    59.1
    best: 78.2 (SMILEtrack)
  • Multi-Object TrackingonMOT17
    IDF1
    70.3
    best: 83.1 (TrackTrack)
  • Multi-Object TrackingonMOT17
    MOTA
    72
    best: 81.8 (TrackTrack)
  • Multi-Object TrackingonMOT16
    IDF1· uses extra data
    72.3
    best: 76.8 (STGT)
  • Multi-Object TrackingonMOT16
    IDs· uses extra data
    669
    best: 1324 (OUTrack_fm)
  • Multi-Object TrackingonMOT16
    MOTA· uses extra data
    73.2
    best: 77.7 (PPTracking)
  • Object TrackingonMOT20
    IDF1· uses extra data
    61.1
    best: 82 (BoostTrack++)
  • Object TrackingonMOT20
    MOTA· uses extra data
    59.1
    best: 78.2 (SMILEtrack)
  • Object TrackingonMOT17
    IDF1
    70.3
    best: 83.1 (TrackTrack)
  • Object TrackingonMOT17
    MOTA
    72
    best: 81.8 (TrackTrack)
  • Object TrackingonMOT16
    IDF1· uses extra data
    72.3
    best: 76.8 (STGT)
  • Object TrackingonMOT16
    IDs· uses extra data
    669
    best: 1324 (OUTrack_fm)
  • Object TrackingonMOT16
    MOTA· uses extra data
    73.2
    best: 77.7 (PPTracking)