# Bipartite matching loss

YOLOS is an object detection model based on Vision Transformer (ViT), trained with DETR loss, and performs excellently on the COCO dataset.

YOLOS is an object detection model using Vision Transformer (ViT) architecture, trained with DETR loss and fine-tuned on COCO and Matterport Balloon datasets.

YOLOS is a vision Transformer (ViT)-based object detection model trained with DETR loss, achieving 42 AP performance on the COCO dataset.

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.

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 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.