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..
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection
Authors: Yuxuan Li, Xiang Li, Weijie Li, Qibin Hou, Li Liu, Ming-Ming Cheng, Jian Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With this high-quality dataset, we conducted comprehensive experiments and uncovered a crucial challenge in SAR object detection: the substantial disparities between the pretraining on RGB datasets and finetuning on SAR datasets in terms of both data domain and model structure. 5 Experiments and Analysis |
| Researcher Affiliation | Academia | 1 PCA Lab, VCIP, CS, Nankai University 2NKIARI, Futian, Shenzhen 3Academy of Advanced Technology Research of Hunan |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The dataset and code is available at https://github.com/zcablii/SARDet_100K. Establishment of a new benchmark in SAR object detection by releasing the datasets and code associated with our research. |
| Open Datasets | Yes | Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a large-scale and diverse dataset for research purposes. The dataset and code is available at https://github.com/zcablii/SARDet_100K. |
| Dataset Splits | Yes | If the source dataset already provides predefined train, validation, and test splits, we adopt their split settings. Otherwise, we perform the splitting by ensuring a ratio of 8:1:1 for the train, validation, and test sets respectively. |
| Hardware Specification | Yes | We primarily conduct our experiments using the MMPretrain [11] and the MMDetection [8] frameworks, on 8 RTX-3090 GPUs (24G). |
| Software Dependencies | No | We primarily conduct our experiments using the MMPretrain [11] and the MMDetection [8] frameworks - no version numbers are provided for these frameworks. |
| Experiment Setup | Yes | For detailed information on the hyperparameters and training settings, please refer to Table S12. Table S12: Hyper-parameter of pretrain and finetune settings. Cls.: Classification, Det.: Detection, B.S.: Batch Size, L.R.: Learning Rate. Task / Model Dataset Optim. B.S. L.R Epochs |