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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Spatial Transform Decoupling for Oriented Object Detection
Authors: Hongtian Yu, Yunjie Tian, Qixiang Ye, Yunfan Liu
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 18,967 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,631 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | STD achieves state-of-the-art performance on the benchmark datasets including DOTA-v1.0 (82.24% m AP) and HRSC2016 (98.55% m AP), which demonstrates the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | University of Chinese Academy of Sciences EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at https://github.com/yuhongtian17/Spatial-Transform Decoupling. |
| Open Datasets | Yes | Experiments are conducted on two commonly-used datasets for oriented object detection, namely DOTA-v1.0 (Xia et al. 2018) and HRSC2016 (Liu et al. 2017). |
| Dataset Splits | Yes | The dataset consists of a total of 188,282 individual instances distributed across 15 different classes, and it is partitioned into training, validation, and test sets containing 1,411, 458, and 937 images, respectively. The commonly used training, validation, and test sets consist of 436, 181, and 444 images, respectively. |
| Hardware Specification | Yes | All experiments are conducted on 8 A100 GPUs with a batch size of 8. |
| Software Dependencies | No | The experimental results are obtained on the MMRotate platform (Zhou et al. 2022). |
| Experiment Setup | Yes | The model is trained for 12 epochs on DOTA-v1.0 and 36 epochs on HRSC2016. We adopt the Adam W optimizer (Kingma and Ba 2014) with an initial learning rate of 1e 4/2.5e 4 for DOTA-v1.0/HRSC2016, a weight decay of 0.05, and a layer decay of 0.75/0.90 for Vi T/Hi Vi T. |