# End-to-end object detection

DETR is a Transformer-based object detection model that handles detection tasks in an end-to-end manner, eliminating the need for complex post-processing steps.

DETR-Resnet50 is a Transformer-based semantic segmentation model, providing efficient mobile deployment capabilities through the Core ML format.

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.

Deformable DETR is a Transformer-based object detection model that enables end-to-end training through bounding box refinement and two-stage detection, suitable for 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.

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.

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