YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913699903569920 |
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| author | Chen, Yuming Yuan, Xinbin Wang, Jiabao Wu, Ruiqi Li, Xiang Hou, Qibin Cheng, Ming-Ming |
| author_facet | Chen, Yuming Yuan, Xinbin Wang, Jiabao Wu, Ruiqi Li, Xiang Hou, Qibin Cheng, Ming-Ming |
| contents | 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 state-of-the-art real-time object detectors, including YOLO-v7, RTMDet, and YOLO-v8. Taking the XS version of YOLO-MS as an example, it can achieve an AP score of 42+% on MS COCO, which is about 2% higher than RTMDet with the same model size. Furthermore, our work can also serve as a plug-and-play module for other YOLO models. Typically, our method significantly advances the APs, APl, and AP of YOLOv8-N from 18%+, 52%+, and 37%+ to 20%+, 55%+, and 40%+, respectively, with even fewer parameters and MACs. Code and trained models are publicly available at https://github.com/FishAndWasabi/YOLO-MS. We also provide the Jittor version at https://github.com/NK-JittorCV/nk-yolo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05480 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection Chen, Yuming Yuan, Xinbin Wang, Jiabao Wu, Ruiqi Li, Xiang Hou, Qibin Cheng, Ming-Ming Computer Vision and Pattern Recognition 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 state-of-the-art real-time object detectors, including YOLO-v7, RTMDet, and YOLO-v8. Taking the XS version of YOLO-MS as an example, it can achieve an AP score of 42+% on MS COCO, which is about 2% higher than RTMDet with the same model size. Furthermore, our work can also serve as a plug-and-play module for other YOLO models. Typically, our method significantly advances the APs, APl, and AP of YOLOv8-N from 18%+, 52%+, and 37%+ to 20%+, 55%+, and 40%+, respectively, with even fewer parameters and MACs. Code and trained models are publicly available at https://github.com/FishAndWasabi/YOLO-MS. We also provide the Jittor version at https://github.com/NK-JittorCV/nk-yolo. |
| title | YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.05480 |