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
this https URL

提供高效且表现良好的目标检测器 YOLO-MS, 基于对不同核大小的卷积如何影响不同尺度物体的检测性能进行一系列研究来实现。新的策略能够大大增强实时目标检测器的多尺度特征表示。在 MS COCO 数据集上训练的 YOLO-MS, 不依赖于其他大规模数据集或预训练权重，在相同参数和 FLOPs 数量下，优于最近的实时目标检测器，包括 YOLO-v7 和 RTMDet。