DEtection TRansformers (DETR)

In 2020, Facebook AI research unveiled DEtection TRansformers (DETRCarion, Nicolas, et al. "End-to-end object detection with transformers." European conference on computer vision. Cham: Springer International Publishing, 2020.), introducing a novel approach to object detection. DETR stands out by incorporating Transformers as a central component in the object detection pipeline, marking a departure from previous system architectures.

DETR demonstrated comparable performance to state-of-the-art methods, including the well-established Faster R-CNN baseline, when applied to the challenging COCO (Common Objects in Context) datasetCOCO is a diverse image dataset for object detection, segmentation, and captioning tasks. in 2020. Notably, it achieves this while simplifying and streamlining the architecture, representing a significant evolution in the field of computer vision.

Exploring DETR

DETR, a model designed for detecting objects, utilizes the transformer architecture, initially created for natural language processing. This innovative approach effectively tackles the object detection challenge by incorporating the transformer's self-attention mechanism.

The crucial element of DETR's structure is the transformer, a neural network architecture famous for its self-attention feature. This mechanism allows the model to grasp intricate connections and interdependencies among elements within a sequence or dataset. In the case of DETR, the self-attention mechanism of the transformer significantly contributes to comprehending the content and spatial associations of objects in an image.

How DETR detects objects

DETR treats the object detection problem differently from traditional object detection systems like Faster R-CNNEfficient region-based convolutional neural network for object detection tasks or YOLOYou Only Look Once, real-time object detection algorithm efficiency.. Below, we outline how DETR approaches object detection.

• Direct set prediction: Instead of using the conventional two-stage process involving region proposal networks and subsequent object classification, DETR frames object detection as a direct set prediction problem. It considers all objects in the image as a set and aims to predict their classes and bounding boxes in one pass.

• Object queries: DETR introduces the concept of "object queries." These queries represent the objects that the model needs to predict. The number of object queries is typically fixed, regardless of the number of objects in the image.

• Transformer self-attention: The transformer's self-attention ...