GrokSAR

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official repository for DenoDet

"DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images" at: https://arxiv.org/pdf/2406.02833

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GrokSAR is an open-source toolbox for SAR target detection and recognition.

• GrokSAR
  • official repository for DenoDet
  • Installation
  • Step 1: Create a conda environment
  • Step 2: Install PyTorch
  • Step 3: Install OpenMMLab 2.x Codebases
  • Step 4: Install groksar
  • Getting Started
  • Training
    • Single GPU Training
    • Multi GPU Training
  • Inference
    • Single GPU Inference
    • Multi GPU Inference
  • Model Zoo and Benchmark
  • Leaderboard
  • Model Zoo
    • DenoDet
    • DenoDet V2
  • Citation
  • License

Installation

Step 1: Create a conda environment

shell conda create --name groksar python=3.8 source activate groksar

Step 2: Install PyTorch

shell conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia

Step 3: Install OpenMMLab 2.x Codebases

```shell

openmmlab codebases

pip install -U openmim dadaptation cmake lit --no-input mim install mmengine "mmcv>=2.0.0rc4, <2.1.0" "mmdet>=3.0.0rc5, < 3.1.0" "mmsegmentation>=1.0.0" "mmrotate>=1.0.0rc1" mmyolo mmpretrain

heatmap generation dependencies

pip install grad-cam==1.4.0

other dependencies

pip install ninja --no-input pip install scikit-learn pip install psutil pip install scikit-image ```

Step 4: Install groksar

shell python setup.py develop

Note: make sure you have cd to the root directory of groksar

shell $ git clone git@github.com:GrokCV/groksar.git $ cd groksar

Getting Started

Training

Single GPU Training

For SARDet-100K dataset:

shell python tools/train_det.py configs/DenoDet/DenoDet_1x_SARDet_100k.py

For SAR-AIRcraft-1.0 dataset:

shell python tools/train_det.py configs/DenoDet/DenoDet_1x_SAR-AIRcraft-1.0.py

For MSAR dataset:

shell python tools/train_det.py configs/DenoDet/DenoDet_3x_MSAR.py

For AIR-SARShip-1.0 dataset:

shell python tools/train_det.py configs/DenoDet/DenoDet_6x_AIR-SARShip-1.0.py

Multi GPU Training

Take a 4-GPU machine as example.

For SARDet-100K dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_train.sh configs/DenoDet/DenoDet_1x_SARDet_100k.py 4

For SAR-AIRcraft-1.0 dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_train.sh configs/DenoDet/DenoDet_1x_SAR-AIRcraft-1.0.py 4

For MSAR dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_train.sh configs/DenoDet/DenoDet_3x_MSAR.py 4

For AIR-SARShip-1.0 dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_train.sh configs/DenoDet/DenoDet_6x_AIR-SARShip-1.0.py 4 Here, 4 is the number of GPUs in your machine.

Inference

Single GPU Inference

For SARDet-100K dataset:

shell python tools/test_det.py configs/DenoDet/DenoDet_1x_SARDet_100k.py {checkpoint_path}

For SAR-AIRcraft-1.0 dataset:

shell python tools/test_det.py configs/DenoDet/DenoDet_1x_SAR-AIRcraft-1.0.py {checkpoint_path}

For MSAR dataset:

shell python tools/test_det.py configs/DenoDet/DenoDet_3x_MSAR.py {checkpoint_path}

For AIR-SARShip-1.0 dataset:

shell python tools/test_det.py configs/DenoDet/DenoDet_6x_AIR-SARShip-1.0.py {checkpoint_path}

Here, {checkpoint_path} represents the path to the weights you downloaded or trained. The {curly braces} are for reference only and should not be included when using the scripts.

Multi GPU Inference

Take a 4-GPU machine as example.

For SARDet-100K dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_test.sh configs/DenoDet/DenoDet_1x_SARDet_100k.py {checkpoint_path} 4

For SAR-AIRcraft-1.0 dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_test.sh configs/DenoDet/DenoDet_1x_SAR-AIRcraft-1.0.py {checkpoint_path} 4

For MSAR dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_test.sh configs/DenoDet/DenoDet_3x_MSAR.py {checkpoint_path} 4

For AIR-SARShip-1.0 dataset:

shell CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 tools/dist_test.sh configs/DenoDet/DenoDet_6x_AIR-SARShip-1.0.py {checkpoint_path} 4

Here, {checkpoint_path} represents the path to the weights you downloaded or trained. The {curly braces} are for reference only and should not be included when using the scripts, and {4} is the number of GPUs in your machine.

Model Zoo and Benchmark

Note: Both passwords for BaiduYun and OneDrive is grok.

Leaderboard

Model Zoo

DenoDet

SARDet-100K

Model	mAP(COCO)	FLOPs	Config	Training Log	Checkpoint
DenoDet	55.88	52.69G	DenoDet1xSARDet_100k.py	百度网盘 | OneDirve

MSAR

Model	mAP(07)	mAP(12)	FLOPs	Config	Training Log	Checkpoint
DenoDet	69.90	71.21	12.89G	DenoDet3xMSAR.py	百度网盘 | OneDirve

SAR-AIRcraft-1.0

Model	mAP(07)	mAP(12)	FLOPs	Config	Training Log	Checkpoint
DenoDet	68.60	69.56	48.53G	DenoDet1xSAR-AIRcraft-1.0.py	百度网盘 | OneDirve

AIR-SARShip-1.0

Model	mAP(07)	mAP(12)	FLOPs	Config	Training Log	Checkpoint
DenoDet	72.42	73.36	48.52G	DenoDet6xAIR-SARShip-1.0.py	百度网盘 | OneDirve

DenoDet V2

SARDet-100K(val set)

Model	mAP(COCO)	FLOPs	Config	Training Log	Checkpoint
DenoDet V2	56.71	52.47G	DenoDetV21xSARDet100k.py	百度网盘 | <a href="https://1drv.ms/f/c/869d5f1d401ac2ec/ElTBf5nRCkpBtrLSrx-444UBacdKTmKmJYREyd7zjV6bw?e=tU73cd"> OneDirve

SARDet-100K(test set)

Model	mAP(COCO)	FLOPs	Config	Training Log	Checkpoint
DenoDet V2	56.39	0.21T	DenoDetV21xSARDet100ktest.py	百度网盘 | OneDirve

SAR-AIRcraft-1.0

Model	mAP(07)	mAP(12)	FLOPs	Config	Training Log	Checkpoint
DenoDet V2	69.93	70.73	48.61G	DenoDetV21xSAR-AIRcraft-1.0.py	百度网盘 | OneDirve

AIR-SARShip-1.0

Model	mAP(07)	mAP(12)	FLOPs	Config	Training Log	Checkpoint
DenoDet V2	73.98	74.86	48.61G	DenoDetV26xAIR-SARShip-1.0.py	百度网盘 | OneDirve

Citation

If you use this toolbox or benchmark in your research, please cite this project.

```bibtex @article{dai2024denodet, title={DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images}, author={Dai, Yimian and Zou, Minrui and Li, Yuxuan and Li, Xiang and Ni, Kang and Yang, Jian}, journal={IEEE Transactions on Aerospace and Electronic Systems (TAES)}, year={2024} }

@inproceedings{li2024sardet100k, title={SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection}, author={Yuxuan Li and Xiang Li and Weijie Li and Qibin Hou and Li Liu and Ming-Ming Cheng and Jian Yang}, year={2024}, booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS)}, } ```

License

This project is released under the Attribution-NonCommercial 4.0 International.