  To detect and segment objects in images based on their content is one of the
most active topics in the field of computer vision. Nowadays, this problem can
be addressed using Deep Learning architectures such as Faster R-CNN or YOLO,
among others. In this paper, we study the behaviour of different configurations
of RetinaNet, Faster R-CNN and Mask R-CNN presented in Detectron2. First, we
evaluate qualitatively and quantitatively (AP) the performance of the
pre-trained models on KITTI-MOTS and MOTSChallenge datasets. We observe a
significant improvement in performance after fine-tuning these models on the
datasets of interest and optimizing hyperparameters. Finally, we run inference
in unusual situations using out of context datasets, and present interesting
results that help us understanding better the networks.

Computer Vision and Pattern Recognition