# DETR++: Taming Your Multi-Scale Detection Transformer Source: [DETR++: Taming Your Multi-Scale Detection Transformer]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:16:46.056Z Authors: Chi Zhang, Lijuan Liu, Xiaoxue Zang, Frederick Liu, Hao Zhang, Xinying Song, Jindong Chen Published: 2022-06-07T02:38:31.000Z ## Abstract Convolutional Neural Networks (CNN) have dominated the field of detection ever since the success of AlexNet in ImageNet classification \[12\]. With the sweeping reform of Transformers \[27\] in natural language processing, Carion et al. \[2\] introduce the Transformer-based detection method, i.e., DETR. However, due to the quadratic complexity in the self-attention mechanism in the Transformer, DETR is never able to incorporate multi-scale features as performed in existing CNN-based detectors, leading to inferior results in small object detection. To mitigate this issue and further improve performance of DETR, in this work, we investigate different methods to incorporate multi-scale features and find that a Bi-directional Feature Pyramid (BiFPN) works best with DETR in further raising the detection precision. With this discovery, we propose DETR++, a new architecture that improves detection results by 1.9% AP on MS COCO 2017, 11.5% AP on RICO icon detection, and 9.1% AP on RICO layout extraction over existing baselines. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()