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Attention-Guided Pyramid Context Networks

Paper Link
Authors: Tianfang Zhang, Lei Li, Siying Cao, Tian Pu and Zhenming Peng

Datasets

Usage

Train

python train.py --net-name agpcnet_1 --batch-size 8 --save-iter-step 20 --dataset mdfa
python train.py --net-name agpcnet_1 --batch-size 8 --save-iter-step 40 --dataset sirstaug
python train.py --net-name agpcnet_1 --batch-size 8 --save-iter-step 100 --dataset merged

Inference

python inference.py --pkl-path {checkpoint path} --image-path {image path}

Evaluation

python evaluation.py --dataset {dataset name} 
                     --sirstaug-dir {base dir of sirstaug}
                     --mdfa-dir {base dir of MDFA}
                     --pkl-path {checkpoint path}

Results

MethodsDataPrecisionRecallmIoUFmeasureAUCDownload
AGPCNet+ResNet18MDFA0.59390.72410.48430.65250.8682model
AGPCNet+ResNet18SIRST Aug0.83230.85420.72880.84310.9344model
AGPCNet+ResNet18Merged0.74530.83840.65170.78910.9194model
<center class="half"> <img src="./roc.jpg" width=100%/> </center> <center style="font-size:16px;color:#C0C0C0;text-decoration:underline">Figure. Illustration of ROC compared with state-of-the-art methods.</center>

Evaluation of model-driven algorithms based on traditional metrics refers [ISTD-python].

Citation

@article{zhang2023attention,
  title={Attention-guided pyramid context networks for detecting infrared small target under complex background},
  author={Zhang, Tianfang and Li, Lei and Cao, Siying and Pu, Tian and Peng, Zhenming},
  journal={IEEE Transactions on Aerospace and Electronic Systems},
  year={2023},
  doi={10.1109/TAES.2023.3238703},
  publisher={IEEE}
}