  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.
