What is DETR (Detection Transformers)?

Detection Transformers (DETR) represent a significant shift in object detection methodologies by integrating Transformer architecture, initially developed for natural language processing, into the object detection pipeline. This approach allows DETR to perform end-to-end object detection without relying on traditional region proposal networks, thus simplifying the architecture and enabling parallel processing for faster inference. DETR employs self-attention mechanisms to capture complex relationships between objects, improving accuracy, particularly in cluttered scenes. However, it demands high computational resources and specifies a fixed number of object queries, which might limit its flexibility in diverse scenarios. Despite these challenges, DETR's innovative use of Transformers has positioned it as a competitive framework in the field of computer vision, capable of achieving performance comparable to state-of-the-art models like Faster R-CNN on challenging datasets.