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

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 multi-branch features of the basic block and convolutions with different kernel sizes affect the detection performance of objects at different scales. The outcome is a new strategy that can significantly enhance multi-scale feature representations of real-time object detectors. To verify the effectiveness of our work, 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…

• NN
  National Natural Science Foundation of China

  Awards: 62276145, 62225604
• FR
  Fundamental Research Funds for the Central Universities

  Award: 070-63223049