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..
GSDet: Gaussian Splatting for Oriented Object Detection
Authors: Zeyu Ding, Jiaqi Zhao, Yong Zhou, Wen-liang Du, Hancheng Zhu, Rui Yao
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 3 datasets indicate that GSDet achieves AP50 gains of 0.7% on DIOR-R, 0.3% on DOTA-v1.0, and 0.55% on DOTA-v1.5 when evaluated with adaptive control and outperforms mainstream detectors. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, China University of Mining and Techology 2Mine Digitization Engineering Research Center of the Ministry of Education EMAIL |
| Pseudocode | Yes | Algorithm 1 GSDet Training |
| Open Source Code | Yes | Code link https://github.com/wokaikaixinxin/GSDet. |
| Open Datasets | Yes | We conduct extensive experiments on three datasets DOTA-v1.0 [Xia et al., 2018], DOTA-v1.5 [Xia et al., 2018] and DIOR-R [Cheng et al., 2022a]. |
| Dataset Splits | Yes | DOTA-v1.0 [Xia et al., 2018] comprises 1,869 images in the trainval set and 937 images in the test set, annotated with 188,282 instances across 15 categories. DOTA-v1.5 [Xia et al., 2018] dataset extends the DOTA-v1.0 dataset by adding a new category named Container Crane while keeping the same images. The number of instances is increased to 403,318 in total. DIOR-R [Cheng et al., 2022a] dataset consists of 11,725 training images in the trainval set, 11,738 test images in the test set and 192,512 instances belonging to 20 categories. |
| Hardware Specification | Yes | All models are trained with the batchsize 4 on two Nvidia 2080ti (2 images per GPU). |
| Software Dependencies | No | Our code is built on MMrotate with pytorch. No specific version numbers for MMrotate or Pytorch are provided, which prevents full reproducibility. |
| Experiment Setup | Yes | The optimizer Adam W [Loshchilov and Hutter, 2018] is used with the learning rate as 2.5 10 5 and the weight decay as 10 4. All models are trained with the batchsize 4 on two Nvidia 2080ti (2 images per GPU). The training schedule is 24 epochs, with the learning rate divided by 16 and 22 epochs. Data augmentation strategies contain only random flips. |