Appendix \[sec:params\] for a full specification of the training parameters. The network weights are initialized from an ImageNet-pretrained model (“I” in the tables), or from a Panoptic Segmentation model trained on the Mapillary Vistas dataset (“M” in the tables), depending on the experiment. Differently from [@Voigtlaender_2019_CVPR], we do not pre-train on MS COCO [@LinMSCOCO2014] (“C” in the tables).

The hyper-parameters of the inference algorithm are set as follows: threshold $\tau=1$; window size $N_w$ equal to the per-GPU batch size used during training; minimum tracklet length $N_t=5$. Note that, in contrast with [@Voigtlaender_2019_CVPR], we do not fine-tune these parameters, instead keeping them fixed in all experiments, and independent of class. All experiments are run on four V100 GPUs with 32GB of memory.

[KITTI MOTS]{} {#sec:experiments-ks}
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The [KITTI MOTS]{}dataset was introduced in [@Voigtlaender_2019_CVPR], adding instance segmentation masks for cars and pedestrians to a subset of 21 sequences from KITTI raw [@Geiger2012CVPR]. It has a total of 8.008 images (5.027 for training and 2.981 for validation), containing approximately 18.8k/8.1k annotated masks for 431/99 tracklets of cars/pedestrians in training, and roughly 8.1k/3.3k masks for 151/68 tracklets on the validation split. Approximately $12.3\%$ of all masks were manually annotated and the rest are human-verified 