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We aim at providing the object detection community with an efficient andperformant object detector, termed YOLO-MS. The core design is based on aseries of investigations on how convolutions with different kernel sizes affectthe detection performance of objects at different scales. The outcome is a newstrategy that can strongly enhance multi-scale feature representations ofreal-time object detectors. To verify the effectiveness of our strategy, webuild a network architecture, termed YOLO-MS. We train our YOLO-MS on the MSCOCO dataset from scratch without relying on any other large-scale datasets,like ImageNet, or pre-trained weights. Without bells and whistles, our YOLO-MSoutperforms the recent state-of-the-art real-time object detectors, includingYOLO-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 learnableparameters and 8.7G FLOPs, it can achieve an AP score of 43%+ on MS COCO, whichis about 2%+ higher than RTMDet with the same model size. Moreover, our workcan 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 evenfewer parameters and FLOPs. Code is available athttps://github.com/FishAndWasabi/YOLO-MS.

YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection