  Transfer learning aims to make the most of existing pre-trained models to
achieve better performance on a new task in limited data scenarios. However, it
is unclear which models will perform best on which task, and it is
prohibitively expensive to try all possible combinations. If transferability
estimation offers a computation-efficient approach to evaluate the
generalisation ability of models, prior works focused exclusively on
classification settings. To overcome this limitation, we extend transferability
metrics to object detection. We design a simple method to extract local
features corresponding to each object within an image using ROI-Align. We also
introduce TLogME, a transferability metric taking into account the coordinates
regression task. In our experiments, we compare TLogME to state-of-the-art
metrics in the estimation of transfer performance of the Faster-RCNN object
detector. We evaluate all metrics on source and target selection tasks, for
real and synthetic datasets, and with different backbone architectures. We show
that, over different tasks, TLogME using the local extraction method provides a
robust correlation with transfer performance and outperforms other
transferability metrics on local and global level features.
