May 26, 2020 · The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture.

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.

Jun 7, 2023 · DEtection TRansformer (DETR) introduces transformers to object detection tasks by reframing detection as a set prediction problem. Consequently, eliminating the need for proposal generation and post-processing steps.

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.

Aug 20, 2023 · DEtection TRansformer (DETR) and You Only Look Once (YOLO) are the two prominent approaches for object detection. YOLO has earned its reputation as the go-to model for real-time object...

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

DEtection TRansformer, DETR for short, simplifies the detector by using an encoder-decoder transformer after the feature extraction backbone to directly predict bounding boxes in parallel, requiring minimal post-processing.

The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture.

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.

DETR is a new object detection model that avoids using a lot of hand-crafted variables such as anchor box sizes and IoU thresholds used in non-max suppression. Rather it just asks the maximum number of objects it should expect in a single image.