May 27, 2020 · DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner.

Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture.

We presented DETR, a new design for object detection systems based on transformers and bipartite matching loss for direct set prediction. The approach achieves comparable results to an optimized Faster R-CNN baseline on the challenging COCO dataset.

DETR uses so-called object queries to detect objects in an image. The number of queries determines the maximum number of objects that can be detected in a single image, and is set to 100 by default (see parameter num_queries of DetrConfig ).

May 27, 2020 · We are releasing Detection Transformers (DETR), an important new approach to object detection and panoptic segmentation. It’s the first object detection framework to successfully integrate Transformers as a central building block in the detection pipeline.

Apr 18, 2025 · This study conducts a detailed comparison of RF-DETR object detection base model and YOLOv12 object detection model configurations for detecting greenfruits in a complex orchard environment marked by label ambiguity, occlusions, and background blending. A custom dataset was developed featuring both single-class (greenfruit) and multi-class (occluded and …

Feb 6, 2025 · DETR (DEtection TRansformer) revolutionizes this approach by treating object detection as a direct set prediction problem, eliminating the need for these manual steps. DETR leverages the global...

For object detection an early Transformer-based object detection approach, called DETR, has been introduced in [CMS+20]. In DETR the task of object detection is considered as a set prediction problem.

Our DEtection TRansformer (DETR, see Figure1) predicts all objects at once, and is trained end-to-end with a set loss function which performs bi-partite matching between predicted and ground-truth objects. DETR simpli es the detection pipeline by dropping multiple hand-designed components that en-

Detr, or Detection Transformer, is a set-based object detector using a Transformer on top of a convolutional backbone. It uses a conventional CNN backbone to learn a 2D representation of an input image.