RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark

Xin Zhang  Xue Yang  Yuxuan Li  Jian Yang  Ming-Ming Cheng  Xiang Li^* 

CVPR 2025

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⭐️Highlights

📋Introduction

Abstract

⚡RSAR Dataset

This dataset is build on SARDet-100K and it contains 95,842 images, including 78,837 in the training set, 8,467 in the validation set, and 8,538 in the test set. Instance statistics for each category are shown in the table below

| Category | Train ins. | Val ins. | Test ins. | | :-----------: | :--------: | :------: | :-------: | | Ship (SH) | 92,950 | 10,492 | 10,700 | | Aircraft (AI) | 6,880 | 449 | 494 | | Car (CA) | 9,567 | 1,219 | 1,231 | | Tank (TA) | 9,479 | 1,000 | 886 | | Bridge (BR) | 27,512 | 3,300 | 3,272 | | Harbor (HA) | 3,304 | 400 | 399 | | Total | 149,692 | 16,860 | 16,982 |

The following figure provides a statistical analysis of the annotated instances for each category in the RSAR dataset

🛠️ Usage

1. Installation

Please refer to mmrotate dev-1.x for more detailed instructions.

sh conda create --name mmrotate python=3.8 -y conda activate mmrotate conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia pip install -U openmim mim install mmcv-full mim install mmdet git clone https://github.com/zhasion/RSAR cd RSAR pip install -v -e .

Note: Install PyTorch please following official instructions.

2. Dataset Prepare

Download the RSAR dataset from the or and extract the files to $DATAROOT. The directory structure should look like:

$DATAROOT |-- train | |-- annfiles # contains annotations :*.txt | `-- images # contains sar images :*.jpg *.bmp *.png |-- val | |-- annfiles | `-- images `-- test |-- annfiles `-- images

Please refer to data_preparation.md to prepare the other datasets (e.g. DOTAv-1.0, HRSC). Please note that the dataset needs to be sliced accordingly after downloading, and all experiments in this repository are based on a single-size setting.

3. Training

If you are using a single GPU, you can refer to the following commands:

sh python tools/train.py [your_config]

If you are using multiple GPUs, you can refer to the following commands:

sh sh tools/dist_train.sh [your_config] [available_GPU_num]

4. Evaluation

sh python tools/test.py [your_config]

Please note that if you need to output results for multiple iou thresholds and the precision of the results, you need to modify the configuration accordingly.

5. Convert model

If you find the trained model very large, please refer to publish_model.py

sh python tools/model_converters/publish_model.py [your_model.pth] [your_new_model.pth]

🚀Models and Results

1. Weakly supervised Model

The weakly supervised model is based on H2RBox-v2 with our UCR. All Models utilize ResNet50 as the backbone and trained on one GPU.

| Dataset | DM^* | mAP | Angle | lr schd | BS | Config | Download | | :-------: | :----: | :--: | :----: | :-----: | :--: | :----: | :-----------------: | | DOTA-v1.0 | 2 | 42.65 | le90 | 1x | 2 | config | ckpt | log | | DOTA-v1.0 | 3 | 43.10 | le90 | 1x | 2 | config | ckpt | log | | HRSC | 2 | 60.00 | le90 | 6x | 2 | config | ckpt | log | | HRSC | 3 | 61.74 | le90 | 6x | 2 | config | ckpt | log | | RSAR | 2 | 32.25 | le90 | 1x | 2 | config | ckpt | log | | RSAR | 3 | 32.64 | le90 | 1x | 2 | config | ckpt | log |

* denotes the dimension of mapping in UCR.

Note: It is normal for the reproduction accuracy of the model to deviate slightly from the reported accuracy, but it should all be substantially improved from the baseline model.

2. Fully supervised Model (base on RSAR)

Various detectors trained on RSAR. All models are trained on 4 GPUs.

| Backbone | Model | mAP | Angle | lr schd | BS | Config | Download | | :----------------: | :-----------------: | :---: | :----: | :-------: | :--: | :----: | :---------------: | | ResNet50 (800,800) | Rotated-RetinaNet | 27.65 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | R3Det | 30.50 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | S2ANet | 33.11 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | Rotated-FCOS | 34.22 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | Rotated-Faster RCNN | 30.46 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | O-RCNN | 33.62 | le90 | 1x | 2 | config | ckpt | log | | ReResNet50 (800,800) | ReDet | 34.30 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | RoI-Transformer | 35.02 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | Deformable DETR | 19.63 | le90 | 1x | 2 | config | ckpt | log | | ResNet50 (800,800) | ARS-DETR | 31.56 | le90 | 1x | 2 | config | ckpt | log |

* denotes a special setting in the official repo (e.g. trained 36 epochs in practice), details see STAR-MMRotate. If you need to reproduce this experiment, please refer to README.md.

📘Citation

If you find our paper or benchmark helpful for your research, please consider citing our paper and giving this repo a star ⭐. Thank you very much!

```bibtex @article{zhang2025rsar, title={RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark}, author={Zhang, Xin and Yang, Xue and Li, Yuxuan and Yang, Jian and Cheng, Ming-Ming and Li, Xiang}, journal={arXiv preprint arXiv:2501.04440}, year={2025} }

@inproceedings{li2024sardet, title={Sardet-100k: Towards open-source benchmark and toolkit for large-scale sar object detection}, author={Li, Yuxuan and Li, Xiang and Li, Weijie and Hou, Qibin and Liu, Li and Cheng, Ming-Ming and Yang, Jian}, booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS)}, year={2024}, } ```

Contact

If you have any question, please feel free to contact us via zhasion@mail.nankai.edu.cn or Github issues.

License

Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first.