pre-training settings (ImageNet, Mapillary Vistas and [KITTI Synth]{}). Different pre-trainings affect the classes cars and pedestrians in a different way, the ImageNet and Vistas pre-trained model performs better on pedestrians while the [KITTI Synth]{}pre-trained body is doing better on cars. This is most likely due to the imbalanced distribution of samples in [KITTI Synth]{}while our variant with pre-training on both, Vistas *and* [KITTI Synth]{}yields the overall best results. We obtain absolute improvements of **1.9%/7.5%** for cars/pedestrians over the previously best work in [@Voigtlaender_2019_CVPR] despite using a smaller backbone and no pre-training on MS COCO. Finally, also the recognition metrics for both, detection (box mAP) and instance segmentation (mask mAP) significantly benefit from pre-training on our [KITTI Synth]{}dataset, and in conjunction with Vistas rise by 7.2% and 6.4% over the ImageNet pre-trained variant, respectively.

  -------------------------------------- -------------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ----------
                                                                                                                                     
  Method                                  Pre-training     Car        Ped        Car        Ped        Car        Ped        Box        Mask
  TrackR-CNN [@Voigtlaender_2019_CVPR]      I, C, M        76.2       47.1       87.8       65.5       87.2       75.7        –          –
  CAMOT [@Osep18ICRA]                       I, C, M        67.4       39.5       78.6       57.6       86.5       73.1        –          –
  CIWT [@osep2017combined]                  I, C, M        68.1       42.9       79.4       61.0       86.7       75.7        –          –
  BeyondPixels [@sharma2018beyond]          I, C, M        76.9        –       **89.7**      