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 | Conference PDF | Archive PDF | Plain Text | 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]. |