{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/damo-yolo-a-report-on-real-time-object","title":"DAMO-YOLO : A Report on Real-Time Object Detection Design","arxiv_id":"2211.15444","date":"2022-11-23","proceeding":null,"authors":["Xianzhe Xu","Yiqi Jiang","Weihua Chen","Yilun Huang","Yuan Zhang","Xiuyu Sun"],"abstract":"In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. In particular, we use MAE-NAS, a method guided by the principle of maximum entropy, to search our detection backbone under the constraints of low latency and high performance, producing ResNet/CSP-like structures with spatial pyramid pooling and focus modules. In the design of necks and heads, we follow the rule of ``large neck, small head''.We import Generalized-FPN with accelerated queen-fusion to build the detector neck and upgrade its CSPNet with efficient layer aggregation networks (ELAN) and reparameterization. Then we investigate how detector head size affects detection performance and find that a heavy neck with only one task projection layer would yield better results.In addition, AlignedOTA is proposed to solve the misalignment problem in label assignment. And a distillation schema is introduced to improve performance to a higher level. Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios. For general industry requirements, we propose DAMO-YOLO-T/S/M/L. They can achieve 43.6/47.7/50.2/51.9 mAPs on COCO with the latency of 2.78/3.83/5.62/7.95 ms on T4 GPUs respectively. Additionally, for edge devices with limited computing power, we have also proposed DAMO-YOLO-Ns/Nm/Nl lightweight models. They can achieve 32.3/38.2/40.5 mAPs on COCO with the latency of 4.08/5.05/6.69 ms on X86-CPU. Our proposed general and lightweight models have outperformed other YOLO series models in their respective application scenarios.","url_abs":"https://arxiv.org/abs/2211.15444v4","url_pdf":"https://arxiv.org/pdf/2211.15444v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"damo-yolo-a-report-on-real-time-object","repo_url":"https://github.com/alibaba/lightweight-neural-architecture-search","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"damo-yolo-a-report-on-real-time-object","repo_url":"https://github.com/tinyvision/damo-yolo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"damo-yolo-a-report-on-real-time-object","repo_url":"https://github.com/modelscope/modelscope","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"damo-yolo","method_name":"DAMO-YOLO"},{"method_slug":"spatial-pyramid-pooling","method_name":"Spatial Pyramid Pooling"}],"datasets_introduced":[],"methods_introduced":[{"slug":"damo-yolo","name":"DAMO-YOLO","full_name":"DAMO-YOLO"}],"results":[{"leaderboard":"/sota/real-time-object-detection-on-coco","task":"Real-Time Object Detection","dataset":"COCO (Common Objects in Context)","model":"DAMO-YOLO-L","rank_in_archive_order":53,"of":82,"metrics":{"FPS (V100, b=1)":"126","box AP":"50.8"},"uses_additional_data":false},{"leaderboard":"/sota/real-time-object-detection-on-coco","task":"Real-Time Object Detection","dataset":"COCO (Common Objects in Context)","model":"DAMO-YOLO-M","rank_in_archive_order":59,"of":82,"metrics":{"FPS (V100, b=1)":"233","box AP":"49.2"},"uses_additional_data":false},{"leaderboard":"/sota/real-time-object-detection-on-coco","task":"Real-Time Object Detection","dataset":"COCO (Common Objects in Context)","model":"DAMO-YOLO-S","rank_in_archive_order":68,"of":82,"metrics":{"FPS (V100, b=1)":"325","box AP":"46"},"uses_additional_data":false},{"leaderboard":"/sota/real-time-object-detection-on-coco","task":"Real-Time Object Detection","dataset":"COCO (Common Objects in Context)","model":"DAMO-YOLO-T","rank_in_archive_order":73,"of":82,"metrics":{"FPS (V100, b=1)":"397","box AP":"42"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.15444","atlas_url":"https://app.syntology.ai/?focus=2211.15444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.15444"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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