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 | Conference PDF | Archive PDF | Plain Text | 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 |