detrex: Benchmarking Detection Transformers
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.
Code (1)
Tasks
Benchmarkingobject-detectionObject DetectionPose EstimationSimilar Papers 제목 키워드 기반
Benchmarking Detection Transfer Learning with Vision Transformers
Object detection is a central downstream task used to test if pre-trained network parameters confer benefits, such as improved accuracy or training speed. The complexity of object detection methods can make this benchmar…
Benchmarkingobject-detectionObject DetectionSelf-Supervised Learning+1ferret: a Framework for Benchmarking Explainers on Transformers
As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable. While several explainable AI (XAI) techniques for interpreting the o…
BenchmarkingExplainable Artificial Intelligence (XAI)Hate Speech DetectionSentiment AnalysisDo Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017
Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017. Howeve…
Network Intrusion DetectionBenchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data
PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) to detect cancer in small regions of interest (ROI) along a needle trace region. Ho…
BenchmarkingCancer ClassificationMultiple Instance LearningSpecificityBenchmarking the Computational and Representational Efficiency of State Space Models against Transformers on Long-Context Dyadic Sessions
State Space Models (SSMs) have emerged as a promising alternative to Transformers for long-context sequence modeling, offering linear $O(N)$ computational complexity compared to the Transformer's quadratic $O(N^2)$ scali…
Computational Efficiency