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.