  Coreset selection is a method for selecting a small, representative subset of
an entire dataset. It has been primarily researched in image classification,
assuming there is only one object per image. However, coreset selection for
object detection is more challenging as an image can contain multiple objects.
As a result, much research has yet to be done on this topic. Therefore, we
introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD
generates imagewise and classwise representative feature vectors for multiple
objects of the same class within each image. Subsequently, we adopt submodular
optimization for considering both representativeness and diversity and utilize
the representative vectors in the submodular optimization process to select a
subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed
random selection by +6.4%p in AP$_{50}$ when selecting 200 images.
