Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Rank-DETR for High Quality Object Detection
Authors: Yifan Pu, Weicong Liang, Yiduo Hao, YUHUI YUAN, Yukang Yang, Chao Zhang, Han Hu, Gao Huang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments, showcasing consistent performance improvements across recent strong DETR-based methods such as H-DETR and DINO-DETR. |
| Researcher Affiliation | Collaboration | 1Tsinghua University 2Peking University 3University of Cambridge 4Microsoft Research Asia |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes the methods using text and mathematical formulations. |
| Open Source Code | Yes | Code is available at https://github.com/Leap Lab THU/Rank-DETR. |
| Open Datasets | Yes | We perform object detection experiments using the COCO object detection benchmark [42] |
| Dataset Splits | Yes | Our model is trained on the train set and evaluated on the val set. |
| Hardware Specification | Yes | These assessments were conducted on RTX 3090 GPUs |
| Software Dependencies | No | The paper mentions using the "detrex [54] toolbox" but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | We adhere to the same experimental setup as the original papers for H-DETR [30] and DINO-DETR [75]. |