# COCO dataset

RT-DETR is the first real-time end-to-end object detector, achieving efficient detection through hybrid encoders and query selection mechanisms without NMS post-processing.

YOLOv10 is a real-time end-to-end object detection model proposed by Tsinghua University, with significant improvements in both speed and accuracy.

YOLOv10 is an efficient real-time object detection model without extra training costs. By optimizing architecture and training strategies, it improves detection accuracy while maintaining real-time performance.

Deformable DETR is an end-to-end object detection model that improves detection performance using Transformer architecture and deformable attention mechanisms.

DETR is an end-to-end object detection model using Transformer architecture, employing ResNet-101 as the backbone network and trained on the COCO dataset.

The Deformable DETR single-scale model, designed for object detection tasks, employs end-to-end training and demonstrates excellent performance on the COCO 2017 dataset.

Deformable DETR is an end-to-end object detection model that combines the advantages of Transformer architecture and deformable convolution, achieving efficient object detection on the COCO dataset.

DETR is an end-to-end object detection model combining convolutional neural networks and Transformer architecture, supporting panoptic segmentation tasks.

DETR is an end-to-end object detection model combining convolutional neural networks with Transformers, supporting panoptic segmentation tasks.

DETR is an end-to-end object detection model based on Transformer architecture, using ResNet-50 as the backbone network and trained on the COCO dataset.

DETR is an end-to-end object detection model using Transformer, employing ResNet-101 as the backbone network and trained on the COCO dataset.

DETR is an end-to-end object detection model based on Transformer architecture, using ResNet-50 as the backbone network and trained on the COCO dataset.