  One-stage object detection is commonly implemented by optimizing two
sub-tasks: object classification and localization, using heads with two
parallel branches, which might lead to a certain level of spatial misalignment
in predictions between the two tasks. In this work, we propose a Task-aligned
One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a
learning-based manner. First, we design a novel Task-aligned Head (T-Head)
which offers a better balance between learning task-interactive and
task-specific features, as well as a greater flexibility to learn the alignment
via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL)
to explicitly pull closer (or even unify) the optimal anchors for the two tasks
during training via a designed sample assignment scheme and a task-aligned
loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a
51.1 AP at single-model single-scale testing. This surpasses the recent
one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP),
and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also
demonstrate the effectiveness of TOOD for better aligning the tasks of object
classification and localization. Code is available at
https://github.com/fcjian/TOOD.
