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Models/YOLOPv2

YOLOPv2

Reported on 10 benchmarks across 4 tasks · 1 paper · 8 SOTA

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

Computer Vision5 results

  • Drivable Area DetectiononBDD100K val
    Params (M)· 2022-08-24
    38.9
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434
  • Drivable Area DetectiononBDD100K val
    mIoU· 2022-08-24
    93.2
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434
  • Lane DetectiononBDD100K val
    Accuracy (%)· 2022-08-24
    87.8
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434
  • Lane DetectiononBDD100K val
    Params (M)· 2022-08-24
    38.9
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434
  • Lane DetectiononBDD100K val
    IoU (%)· 2022-08-24
    27.25
    best: 34.2 (TwinLiteNetPlus-Large)
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434

Robots3 results

  • Autonomous VehiclesonBDD100K val
    Accuracy (%)· 2022-08-24
    87.8
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434
  • Autonomous VehiclesonBDD100K val
    Params (M)· 2022-08-24
    38.9
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434
  • Autonomous VehiclesonBDD100K val
    IoU (%)· 2022-08-24
    27.25
    best: 34.2 (TwinLiteNetPlus-Large)
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434

Methodology2 results

  • 2D Object DetectiononBDD100K val
    Params (M)· 2022-08-24
    38.9
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434
  • 2D Object DetectiononBDD100K val
    mIoU· 2022-08-24
    93.2
    SOTA
    YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionarXiv:2208.11434