Few-Shot Object Detection in Remote Sensing Images via Label-Consistent Classifier and Gradual Regression

Code for reproducing the results in our TGRS-2024 paper Few-Shot Object Detection in Remote Sensing Images via Label-Consistent Classifier and Gradual Regression. Our code is based on the open-source project mmfewshot.

Requirements

python packages
- Python 3.8
- torch 2.0.0
- mmcv-full 1.6.0
- mmdet 2.27.0
- mmcls 0.25.0
CUDA 11.8

Installation

Run following shell commands to prepare the running enviroments.

conda create -n SAE-FSDet python=3.8
conda activate SAE-FSDet
# install pytorch
pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118
# install mmcv-full mmdet mmcls
pip install openmim
mim install mmcv-full==1.6.0
mim install mmdet==2.27.0
mim install mmcls==0.25.0
# clone SAE-FSDet repo
git clone https://github.com/YanxingLiu/SAE-FSDet.git
cd SAE-FSDet
pip install -r requirements.txt
pip install -v -e .

Dataset Preparation

All the data are organized in data folder. The data folder layout should look like this.data
data
├── DIOR
├── NWPU VHR-10 dataset
└── few_shot_ann

DIOR dataset

For DIOR dataset, you can download DIOR dataset from its official website and prepare the data folder like this.data
DIOR
├── Annotations
│ ├──00001.xml
│ ├──00002.xml
│ ├──*.xml
├── ImageSets
│ ├──Main
│   │   ├── train.txt
│   │   ├── val.txt
│   │   ├── trainval.txt
│   │   ├── test.txt
├── JPEGImages
└── *.jpg

NWPU VHR-10 dataset

For NWPU VHR-10 dataset, you can download it from here.The original dataset was not divided into training and validation sets. As a result, we uploaded our train/val splits in data/NWPU VHR-10 dataset/Main. The final folder layout should look like this. data
NWPU VHR-10 dataset
├── ground truth
├── Main
├── negative image set
├── positive image set
└── readme.txt

Few-shot annotations

The few_shot_ann is our few-shot data splits. You can unzip the few_shot_ann.zip in data folder and the final layout will be look like this. few_shot_ann
├── dior
│ ├── benchmark_10shot
│ ├── benchmark_1shot
│ ├── benchmark_20shot
│ ├── benchmark_2shot
│ ├── benchmark_3shot
│ └── benchmark_5shot
└── vhr10
├── benchmark_10shot
├── benchmark_20shot
├── benchmark_3shot
└── benchmark_5shot

Getting Started

Train a model

Base trianing:

# Single gpu
python tools/detection/train.py configs/detection/SAE-FSDet/dior/split1/SAE-FSDet_r101_fpn_dior-split1_base-training.py
# multi gpu
bash tools/detection/dist_train.sh configs/detection/SAE-FSDet/dior/split1/SAE-FSDet_r101_fpn_dior-split1_base-training.py ${GPU_NUM}

Fine-tuning:

# Single gpu
python tools/detection/train.py configs/detection/SAE-FSDet/dior/split1/SAE-FSDet_r101_fpn_dior-split1_20shot_finetuning.py 
# multi gpu
bash tools/detection/dist_train.sh configs/detection/SAE-FSDet/dior/split1/SAE-FSDet_r101_fpn_dior-split1_20shot_finetuning.py ${GPU_NUM}

Note that if you use multi gpus, you should modify the learning rate and training iters based on Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. All of our experiments are running in a single gpu.

Test a model

You can test a trained model by using the following commands:

# Single gpu
python tools/detection/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --eval mAP
# Multi gpu
bash tools/detection/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} --eval mAP

For example, test the performance of 20-shot model.

python tools/detection/test.py configs/detection/SAE-FSDet/dior/split1/SAE-FSDet_r101_fpn_dior-split1_20shot_finetuning.py \
        models/SAE-FSDet_r101_fpn_dior-split1_20shot_finetuning/latest.pth \
        --eval mAP

Acknowledgement

This repo is based on the open-source mmfewshot project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features of mmfewshot.

Citation

@ARTICLE{10445268,
  author={Liu, Yanxing and Pan, Zongxu and Yang, Jianwei and Zhang, Bingchen and Zhou, Guangyao and Hu, Yuxin and Ye, Qixiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Few-Shot Object Detection in Remote Sensing Images via Label-Consistent Classifier and Gradual Regression}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  keywords={Detectors;Remote sensing;Feature extraction;Proposals;Training;Object detection;Shape;Few-shot learning;object detection;remote sensing images;transfer-learning},
  doi={10.1109/TGRS.2024.3369666}}

leenamx/SAE-FSDet