Submitted by
Xingyi Zhou
Authors
Xingyi Zhou
Venue
UniDet_RVC
Abstract
Learning a unified label space from data
Runtime
300 ms
External data used
COCO, Cityscapes, OpenImages, Mapillary, ScanNet
Links
  • There is no link to the paper.
  • There is no link to the code.
  • There is no link to the project website.
Number of submissions
2

Detailed results
Class AP (day) AP (sunset) AP (rain) AP (snow) AP (night) AP (mean)
Trafficlight40.535.535.147.43.132.3
Firehydrant28.825.339.132.77.526.7
Chair2.90.81.42.60.11.6
Trashcan21.913.216.416.51.513.9
Person19.011.97.618.40.511.5
Motorcycle20.815.214.012.65.113.5
Car35.234.639.540.56.631.3
Van27.33.120.87.418.615.4
Bus23.721.426.115.90.517.5
Truck30.921.229.628.216.725.3
Mean25.118.223.022.26.018.9
Class AP50 (day) AP50 (sunset) AP50 (rain) AP50 (snow) AP50 (night) AP50 (mean)
Trafficlight69.164.963.473.65.955.4
Firehydrant59.650.278.657.416.352.4
Chair9.22.46.27.20.35.0
Trashcan40.524.833.629.92.926.3
Person47.233.521.548.81.730.5
Motorcycle52.256.935.046.014.040.8
Car63.162.565.964.911.353.5
Van38.54.831.910.324.422.0
Bus41.637.834.524.90.927.9
Truck52.040.048.042.925.641.7
Mean47.337.841.940.610.335.6