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.

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.

present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, e ectively removing the need for many hand-designed compo-nents like a non-maximum suppression proc.

May 27, 2020 · The DETR framework consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel.

Apr 12, 2024 · In this article, we will delve into the concepts of DETR for end to end object detection using Transformers architecture which mainly utilises the bipartite matching objective. It is a simple...

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...

Apr 23, 2022 · DEtection TRansformer or DETR is designed, which proposes a set-based global loss that forces unique predictions via bipartite matching, using a Transformer encoder-decoder architecture....

DETR consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use things like region proposals, non-maximum suppression procedure and anchor generation.

Aug 8, 2023 · DETR have three main components: CNN Backbone, Transformer Encoder, Transformer Decoder. This is a CNN backbone like ResNet-50, ResNet-101. This is used to generate low resolution feature maps. If an initial image is of [3, H0, W0] dimensions encoder reduces it to [C, H, W].