Awesome
SAR-JEPA: A Joint-Embedding Predictive Architecture for SAR ATR
These are codes and weights of the paper：
百度网盘: 链接：https://pan.baidu.com/s/14sRPSCygTKMelSy4ZkqRzw?pwd=jeq8 提取码：jeq8
Dataset
| Dataset | Size | #Target | #Scene | Res(m) | Band | Polarization | Description |
|---|---|---|---|---|---|---|---|
| MSAR | 28,499 | >4 | >6 | 1 | C | Quad | Ground and sea target detection dataset |
| SAR-Ship | 39,729 | >1 | >4 | 3~25 | C | Quad | Ship detection dataset in complex scenes |
| SARSim | 21,168 | 7 | 3 | 0.3 | X | Single | Vehicle simulation dataset |
| SAMPLE | 5,380 | 10 | 1 | 0.3 | X | Single | Vehicle simulation and measured~dataset |
| MSTAR | 5,216 | 10 | 1 | 0.3 | X | Single | Fine-grained vehicle classification dataset |
| FUSAR-Ship | 9,830 | 10 | >5 | 1.1~1.7 | C | Double | Fine-grained ship classification dataset |
| SAR-ACD | 2,537 | 6 | 3 | 1 | C | Single | Fine-grained aircraft classification dataset |
Pre-training
Our code is based on LoMaR with MAE and MaskFeat, and its enviroment is follow LoMaR.
-
This repo is based on
timm==0.3.2, for which a fix is needed to work with PyTorch 1.8.1+. -
The relative position encoding is modeled by following iRPE. To enable the iRPE with CUDA supported. Of curese, irpe can run without build.
cd rpe_ops/
python setup.py install --user
For pre-training with default setting
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=25642 main_pretrain.py --data_path ${IMAGENET_DIR}
Our main changes are in the model_lomar.py
self.sarfeature1 = GF(nbins=self.nbins,pool=self.cell_sz,kensize=5,
img_size=self.img_size,patch_size=self.patch_size)
self.sarfeature2 = GF(nbins=self.nbins,pool=self.cell_sz,kensize=9,
img_size=self.img_size,patch_size=self.patch_size)
self.sarfeature3 = GF(nbins=self.nbins,pool=self.cell_sz,kensize=13,
img_size=self.img_size,patch_size=self.patch_size)
self.sarfeature4 = GF(nbins=self.nbins,pool=self.cell_sz,kensize=17,
img_size=self.img_size,patch_size=self.patch_size)
Fine-tuning with pre-trained checkpoints
Our few-shot learning is based on Dassl. You may need to installate this and use our modified tools.py and transforms.py for SAR images. You can run MIM_finetune.sh and MIM_linear.sh.
Contact us
If you have any questions, please contact us at lwj2150508321@sina.com
@article{li2023predicting,
title={Predicting Gradient is Better: Exploring Self-Supervised Learning for {SAR} {ATR} with a Joint-Embedding Predictive Architecture },
author={Li, Weijie and Wei, Yang and Liu, Tianpeng and Hou, Yuenan and Liu, Yongxiang and Liu, Li},
journal={arXiv preprint},
url={https://arxiv.org/abs/2311.15153},
year={2024}
}