  Recently, end-to-end object detectors have gained significant attention from
the research community due to their outstanding performance. However, DETR
typically relies on supervised pretraining of the backbone on ImageNet, which
limits the practical application of DETR and the design of the backbone,
affecting the model's potential generalization ability. In this paper, we
propose a new training method called step-by-step training. Specifically, in
the first stage, the one-to-many pre-trained YOLO detector is used to
initialize the end-to-end detector. In the second stage, the backbone and
encoder are consistent with the DETR-like model, but only the detector needs to
be trained from scratch. Due to this training method, the object detector does
not need the additional dataset (ImageNet) to train the backbone, which makes
the design of the backbone more flexible and dramatically reduces the training
cost of the detector, which is helpful for the practical application of the
object detector. At the same time, compared with the DETR-like model, the
step-by-step training method can achieve higher accuracy than the traditional
training method of the DETR-like model. With the aid of this novel training
method, we propose a brand-new end-to-end real-time object detection model
called DEYOv3. DEYOv3-N achieves 41.1% on COCO val2017 and 270 FPS on T4 GPU,
while DEYOv3-L achieves 51.3% AP and 102 FPS. Without the use of additional
training data, DEYOv3 surpasses all existing real-time object detectors in
terms of both speed and accuracy. It is worth noting that for models of N, S,
and M scales, the training on the COCO dataset can be completed using a single
24GB RTX3090 GPU. Code will be released at
https://github.com/ouyanghaodong/DEYOv3.
