The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and other perception tasks. However, the current field lacks a unified and comprehensive benchmark specifically tailored for DETR-based models. To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation. We conduct extensive experiments under detrex and perform a comprehensive benchmark for DETR-based models. Moreover, we enhance the performance of detection transformers through the refinement of training hyper-parameters, providing strong baselines for supported algorithms.We hope that detrex could offer research communities a standardized and unified platform to evaluate and compare different DETR-based models while fostering a deeper understanding and driving advancements in DETR-based instance recognition. Our code is available at https://github.com/IDEA-Research/detrex. The project is currently being actively developed. We encourage the community to use detrex codebase for further development and contributions.

翻译：DEtection TRansformer（DETR）算法已在研究界获得广泛关注，并逐渐成为目标检测及其他感知任务的主流方法。然而，当前领域缺乏一种专门针对基于DETR模型的统一且全面的基准测试平台。为解决这一问题，我们开发了一个统一、高度模块化且轻量级的代码库detrex，其支持大多数主流的基于DETR的实例识别算法，涵盖包括目标检测、分割和姿态估计在内的多种基础任务。我们在detrex框架下进行了大量实验，并对基于DETR的模型执行了全面的基准测试。此外，我们通过优化训练超参数提升了检测变压器的性能，为所支持的算法提供了强有力的基准。我们希望detrex能够为研究社区提供一个标准化且统一的平台，用以评估和比较不同的基于DETR的模型，同时促进对基于DETR的实例识别的深入理解并推动相关进展。我们的代码可在https://github.com/IDEA-Research/detrex获取。该项目目前正在积极开发中。我们鼓励社区使用detrex代码库进行进一步开发与贡献。