Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection, ECCV 2022

This repo is built upon DeFRCN, where you can download the datasets and the pre-trained weights.

Requirements

Python == 3.7.10

Pytorch == 1.6.0

Torchvision == 0.7.0

Detectron2 == 0.3

CUDA == 10.1

File Structure

    ├── weight/                   
    |   ├── R-101.pkl              
    |   └── resnet101-5d3b4d8f.pth   
    └── datasets/
        ├── coco/           
        │   ├── annotations/
        │   ├── train2014/
        │   └── val2014/
        ├── cocosplit/
        ├── VOC2007/            
        │   ├── Annotations/
        │   ├── ImageSets/
        │   └── JPEGImages/
        ├── VOC2012/            
        │   ├── Annotations/
        │   ├── ImageSets/
        │   └── JPEGImages/
        └── vocsplit/

Training and Evaluation

• For VOC

sh voc_train.sh mfdc SPLIT_ID

• For COCO

sh coco_train.sh mfdc

Citation

If you find our code helpful in your research, please cite the following publication:

@inproceedings{wu2022multi,
  title={Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection},
  author={Wu, Shuang and Pei, Wenjie and Mei, Dianwen and Chen, Fanglin and Tian, Jiandong and Lu, Guangming},
  booktitle={European Conference on Computer Vision},
  pages={578--594},
  year={2022},
  organization={Springer}
}

Contact

Please feel free to contact me (Email: [email protected]) if you have any questions.