  We aim at providing the object detection community with an efficient and
performant object detector, termed YOLO-MS. The core design is based on a
series of investigations on how convolutions with different kernel sizes affect
the detection performance of objects at different scales. The outcome is a new
strategy that can strongly enhance multi-scale feature representations of
real-time object detectors. To verify the effectiveness of our strategy, we
build a network architecture, termed YOLO-MS. We train our YOLO-MS on the MS
COCO dataset from scratch without relying on any other large-scale datasets,
like ImageNet, or pre-trained weights. Without bells and whistles, our YOLO-MS
outperforms the recent state-of-the-art real-time object detectors, including
YOLO-v7 and RTMDet, when using a comparable number of parameters and FLOPs.
Taking the XS version of YOLO-MS as an example, with only 4.5M learnable
parameters and 8.7G FLOPs, it can achieve an AP score of 43%+ on MS COCO, which
is about 2%+ higher than RTMDet with the same model size. Moreover, our work
can also be used as a plug-and-play module for other YOLO models. Typically,
our method significantly improves the AP of YOLOv8 from 37%+ to 40%+ with even
fewer parameters and FLOPs. Code is available at
https://github.com/FishAndWasabi/YOLO-MS.
