wmyname/vfa

Official code of the paper "Few-Shot Object Detection via Variational Feature Aggregation" (AAAI 2023)

VFA

Few-Shot Object Detection via Variational Feature Aggregation (AAAI2023)
Jiaming Han, Yuqiang Ren, Jian Ding, Ke Yan, Gui-Song Xia.
arXiv preprint.

Our code is based on mmfewshot.

Setup

• Installation

Here is a from-scratch setup script.

conda create -n vfa python=3.8 -y
conda activate vfa

conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=11.0 -c pytorch

pip install openmim
mim install mmcv-full==1.3.12

# install mmclassification mmdetection
mim install mmcls==0.15.0
mim install mmdet==2.16.0

# install mmfewshot
mim install mmfewshot==0.1.0

# install VFA
python setup.py develop

• Prepare Datasets

Please refer to mmfewshot's detection data preparation.

Model Zoo

All pretrained models can be found at github release.

Results on PASCAL VOC dataset

• Base Training

Split	Base AP50	config	ckpt
1	78.6	config	ckpt
2	79.5	config	ckpt
3	79.8	config	ckpt

• Few Shot Fine-tuning

Split	Shot	nAP50	config	ckpt
1	1	57.5	config	ckpt
1	2	65.0	config	ckpt
1	3	64.3	config	ckpt
1	5	67.1	config	ckpt
1	10	67.4	config	ckpt
2	1	40.8	config	ckpt
2	2	45.9	config	ckpt
2	3	51.1	config	ckpt
2	5	51.8	config	ckpt
2	10	51.8	config	ckpt
3	1	49.0	config	ckpt
3	2	54.9	config	ckpt
3	3	56.6	config	ckpt
3	5	59.0	config	ckpt
3	10	58.5	config	ckpt

Results on COCO dataset

• Base Training

Base mAP	config	ckpt
36.0	config	ckpt

• Few Shot Finetuning

Shot	nAP	config	ckpt
10	16.8	config	ckpt
30	19.5	config	ckpt

Train and Test

• Testing

# single-gpu test
python test.py ${CONFIG} ${CHECKPOINT} --eval mAP|bbox

# multi-gpus test
bash dist_test.sh ${CONFIG} ${CHECKPOINT} ${NUM_GPU} --eval mAP|bbox

For example:

• test VFA on VOC split1 1-shot with sinel-gpu, we should run:

python test.py configs/vfa/voc/vfa_split1/vfa_r101_c4_8xb4_voc-split1_1shot-fine-tuning.py \
work_dirs/vfa_r101_c4_8xb4_voc-split1_1shot-fine-tuning/iter_400.pth \
--eval mAP

• test VFA on COCO 10-shot with 8 gpus, we should run:

bash dist_test.sh configs/vfa/coco/vfa_r101_c4_8xb4_coco_10shot-fine-tuning.py \
work_dirs/vfa_r101_c4_8xb4_coco_10shot-fine-tuning/iter_10000.pth \
8 --eval bbox

• Training

# single-gpu training
python train.py ${CONFIG}

# multi-gpus training
bash dist_train.sh ${CONFIG} ${NUM_GPU}

For example: train VFA on VOC.

# Stage I: base training.
bash dist_train.sh configs/vfa/voc/vfa_split1/vfa_r101_c4_8xb4_voc-split{1,2,3}_base-training.py 8

# Stage II: few-shot fine-tuning on all splits and shots.
voc_config_dir=configs/vfa/voc/
for split in 1 2 3; do
    for shot in 1 2 3 5 10; do
        config_path=${voc_config_dir}/vfa_split${split}/vfa_r101_c4_8xb4_voc-split${split}_${shot}shot-fine-tuning.py
        echo $config_path
        bash dist_train.sh $config_path 8
    done
done

Note: All our configs and models are trained with 8 gpus. You need to change the learning rate or batch size if you use fewer/more gpus.

Citation

If you find our work useful for your research, please consider citing:

@InProceedings{han2023vfa,
    title     = {Few-Shot Object Detection via Variational Feature Aggregation},
    author    = {Han, Jiaming and Ren, Yuqiang and Ding, Jian and Yan, Ke and Xia, Gui-Song},
    booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23)},
    year      = {2023}
}