The paper is accepted by International Journal of Applied Earth Observation and Geoinformation (JAG) and is accessible here.
Semi-supervised object detection (SSOD) assumes that both labeled and unlabeled data originate from the same label space, constituting in-distribution (ID) samples. Open-set semi-supervised object detection (OSSOD) accommodates the existence of substantial out-of-distribution (OOD) samples, mirroring the complexities of real-world scenarios.
Overview of the proposed open-set semi-supersvised object detection (OSSOD) framework.
conda create --name ossod python=3.8
conda activate ossod
pip install torch===1.7.1+cu110 torchvision===0.8.2+cu110 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html --no-cache
git clone https://github.com/Lans1ng/OSSOD.git
cd OSSOD
pip install -r requirements.txt
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Download the DIOR dataset (Google Drive or BaiduNetDisk)
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Organize the dataset as follow:
dataset/ ├──DIOR/ │ ├── annotations_json_split1 │ ├── annotations_json_split2 │ ├── JPEGImages │ │ ├── 00001.jpg │ │ ├── 00002.jpg │ │ ├── ... │ │ ├── 23463.jpg
JPEGImages
represents all images in DIOR dataset- Annotations for split1 and split2 are already included in the repo.
Before training,please download the pretrained backbone (ResNet-50) to pretrained_model/backbone
.
Take split1 of the DIOR dataset as examples:
- Use in-distribution (ID) labeled data to train a baseline
python train.py --config configs/baseline/baseline_ssod_split1.py --work-dir work_dirs/split1/baseline_ssod --base_stage True
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Use unlabeled data to train semi-supervised detector:
- Train detectors with ID unlabeled data
#unbiased teacher python train.py --config configs/unbiased_teacher/split1/unbiased_teacher_id.py --work-dir work_dirs/split1/unbiased_teacher_ID #our proposed ossod python train.py --config configs/unbiased_teacher/split1/unbiased_teacher_ossod_id.py --work-dir work_dirs/split1/unbiased_teacher_ossod_ID
- Train detectors with ID and mixed unlabeled data
#unbiased teacher python train.py --config configs/unbiased_teacher/split1/unbiased_teacher_id_mix.py --work-dir work_dirs/split1/unbiased_teacher_ID_MIX #our proposed ossod python train.py --config configs/unbiased_teacher/split1/unbiased_teacher_ossod_id_mix.py --work-dir work_dirs/split1/unbiased_teacher_ossod_ID_MIX
- Train detectors with ID, mixed and out-of-distribution (OOD) unlabeled data
#unbiased teacher python train.py --config configs/unbiased_teacher/split1/unbiased_teacher_id_mix_ood.py --work-dir work_dirs/split1/unbiased_teacher_ID_MIX_OOD #our proposed ossod python train.py --config configs/unbiased_teacher/split1/unbiased_teacher_ossod_id_mix_ood.py --work-dir work_dirs/split1/unbiased_teacher_ossod_ID_MIX_OOD
Take split1 of the DIOR dataset as examples:
python eval.py --config configs/unbiased_teacher/split1/unbiased_teacher_ossod_id_mix_ood.py --checkpoint work_dirs/split1/unbiased_teacher_ossod_ID_MIX_OODlatest.pth --eval mAP --show-dir results
If you find this project useful in your research, please consider cite:
@article{liu2024semi,
title={Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images},
author={Liu, Nanqing and Xu, Xun and Gao, Yingjie and Zhao, Yitao and Li, Heng-Chao},
journal={International Journal of Applied Earth Observation and Geoinformation},
year={2024}
}