  Loss functions play an important role in training deep-network-based object
detectors. The most widely used evaluation metric for object detection is
Average Precision (AP), which captures the performance of localization and
classification sub-tasks simultaneously. However, due to the non-differentiable
nature of the AP metric, traditional object detectors adopt separate
differentiable losses for the two sub-tasks. Such a mis-alignment issue may
well lead to performance degradation. To address this, existing works seek to
design surrogate losses for the AP metric manually, which requires expertise
and may still be sub-optimal. In this paper, we propose Parameterized AP Loss,
where parameterized functions are introduced to substitute the
non-differentiable components in the AP calculation. Different AP
approximations are thus represented by a family of parameterized functions in a
unified formula. Automatic parameter search algorithm is then employed to
search for the optimal parameters. Extensive experiments on the COCO benchmark
with three different object detectors (i.e., RetinaNet, Faster R-CNN, and
Deformable DETR) demonstrate that the proposed Parameterized AP Loss
consistently outperforms existing handcrafted losses. Code is released at
https://github.com/fundamentalvision/Parameterized-AP-Loss.
