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Found 4 papers, 1 papers with code
DEYO: DETR with YOLO for End-to-End Object Detection
Specifically, in the first stage of training, we employ a classic detector, pre-trained with a one-to-many matching strategy, to initialize the backbone and neck of the end-to-end detector.
DEYOv3: DETR with YOLO for Real-time Object Detection
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.
DEYOv2: Rank Feature with Greedy Matching for End-to-End Object Detection
This paper presents a novel object detector called DEYOv2, an improved version of the first-generation DEYO (DETR with YOLO) model.
DEYO: DETR with YOLO for Step-by-Step Object Detection
Object detection is an important topic in computer vision, with post-processing, an essential part of the typical object detection pipeline, posing a significant bottleneck affecting the performance of traditional object detection models.
