  In the past years, YOLO-series models have emerged as the leading approaches
in the area of real-time object detection. Many studies pushed up the baseline
to a higher level by modifying the architecture, augmenting data and designing
new losses. However, we find previous models still suffer from information
fusion problem, although Feature Pyramid Network (FPN) and Path Aggregation
Network (PANet) have alleviated this. Therefore, this study provides an
advanced Gatherand-Distribute mechanism (GD) mechanism, which is realized with
convolution and self-attention operations. This new designed model named as
Gold-YOLO, which boosts the multi-scale feature fusion capabilities and
achieves an ideal balance between latency and accuracy across all model scales.
Additionally, we implement MAE-style pretraining in the YOLO-series for the
first time, allowing YOLOseries models could be to benefit from unsupervised
pretraining. Gold-YOLO-N attains an outstanding 39.9% AP on the COCO val2017
datasets and 1030 FPS on a T4 GPU, which outperforms the previous SOTA model
YOLOv6-3.0-N with similar FPS by +2.4%. The PyTorch code is available at
https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO,
and the MindSpore code is available at
https://gitee.com/mindspore/models/tree/master/research/cv/Gold_YOLO.
