# DAMO-YOLO : A Report on Real-Time Object Detection Design

arXiv: https://arxiv.org/abs/2211.15444 · HTML: https://mlsanity.com/paper/2211.15444/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 47 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://mlsanity.com/paper/1904.07392/) (2019)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [TOOD: Task-aligned One-stage Object Detection](https://mlsanity.com/paper/2108.07755/) (2021)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [Distilling Object Detectors with Fine-grained Feature Imitation](https://mlsanity.com/paper/1906.03609/) (2019)
- [OTA: Optimal Transport Assignment for Object Detection](https://mlsanity.com/paper/2103.14259/) (2021)
- [Channel-wise Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/2011.13256/) (2020)
- [Learning Data Augmentation Strategies for Object Detection](https://mlsanity.com/paper/1906.11172/) (2019)
- [Simple multi-dataset detection](https://mlsanity.com/paper/2102.13086/) (2021)

## Built on by (most cited first, 30 of 38)

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Effective Whole-body Pose Estimation with Two-stages Distillation](https://mlsanity.com/paper/2307.15880/) (2023)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation](https://mlsanity.com/paper/2408.04804/) (2024)
- [YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain](https://mlsanity.com/paper/2406.10139/) (2024)
- [BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection](https://mlsanity.com/paper/2309.12585/) (2023)
- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)
- [YOLO26: An Analysis of NMS-Free End to End Framework for Real-Time Object Detection](https://mlsanity.com/paper/2601.12882/) (2026)
- [YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems](https://mlsanity.com/paper/2307.13901/) (2023)
- [Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection](https://mlsanity.com/paper/2407.04381/) (2024)
- [LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection](https://mlsanity.com/paper/2504.02280/) (2025)
- [MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection](https://mlsanity.com/paper/2502.04656/) (2025)
- [SOD-YOLOv8 -- Enhancing YOLOv8 for Small Object Detection in Traffic Scenes](https://mlsanity.com/paper/2408.04786/) (2024)
- [eIQ Neutron: Redefining Edge-AI Inference with Integrated NPU and Compiler Innovations](https://mlsanity.com/paper/2509.14388/) (2025)
- [Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning](https://mlsanity.com/paper/2408.03030/) (2024)
- [Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons](https://mlsanity.com/paper/2311.07955/) (2023)
- [YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection](https://mlsanity.com/paper/2402.12641/) (2024)
- [DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection](https://mlsanity.com/paper/2406.12285/) (2024)
- [FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modules](https://mlsanity.com/paper/2408.16313/) (2024)
- [Real-Time AIoT for AAV Antenna Interference Detection via Edge-Cloud Collaboration](https://mlsanity.com/paper/2412.03055/) (2024)
- [Differential Evolution Integrated Hybrid Deep Learning Model for Object Detection in Pre-made Dishes](https://mlsanity.com/paper/2412.20370/) (2024)
- [Le-DETR: Revisiting Real-Time Detection Transformer with Efficient Encoder Design](https://mlsanity.com/paper/2602.21010/) (2026)
- [MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environment](https://mlsanity.com/paper/2307.12548/) (2023)
- [Designing High-Performing Networks for Multi-Scale Computer Vision](https://mlsanity.com/paper/2402.12536/) (2024)
- [A method for detecting dead fish on large water surfaces based on improved YOLOv10](https://mlsanity.com/paper/2409.00388/) (2024)
- [Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges](https://mlsanity.com/paper/2409.12977/) (2024)
- [WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images](https://mlsanity.com/paper/2411.11738/) (2024)
- [Efficient Vision-based Vehicle Speed Estimation](https://mlsanity.com/paper/2505.01203/) (2025)

## Abstract (arXiv)

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.