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detrex: Benchmarking Detection Transformers

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arxiv 2306.07265 v2 pith:YELUQDHC submitted 2023-06-12 cs.CV

Tianhe Ren , Shilong Liu , Feng Li , Hao Zhang , Ailing Zeng , Jie Yang , Xingyu Liao , Ding Jia
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Hongyang Li He Cao Jianan Wang Zhaoyang Zeng Xianbiao Qi Yuhui Yuan Jianwei Yang Lei Zhang
This is my paper · ORCID
classification cs.CV
keywords detrexdetectiondetr-basedmodelsunifiedalgorithmsbenchmarkcodebase
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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  1. DS-Det: Single-Query Paradigm and Attention Disentangled Learning for Flexible Object Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A flexible-query transformer detector that separates cross-attention localization from self-attention deduplication reports consistent accuracy gains over DINO across five backbones.

  2. Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Cross-DINO improves small-object detection in DETR-like detectors by mixing MLP backbone features, a cross-coding fusion module, and a category-size soft-label loss, achieving 36.4% APs on COCO.

  3. What You Perceive Is What You Conceive: A Cognition-Inspired Framework for Open Vocabulary Image Segmentation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A framework that generates image-level object concepts with a vision-language model before region segmentation improves open-vocabulary segmentation on multiple benchmarks.

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