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Introduction

This repo contains the official PyTorch implementation of our ICCV paper DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection.

Updates!!

  • 【2021/10/10】 We release the official PyTorch implementation of DeFRCN.
  • 【2021/08/20】 We have uploaded our paper (long version with supplementary material) on arxiv, review it for more details.

Quick Start

1. Check Requirements

  • Linux with Python >= 3.6
  • PyTorch >= 1.6 & torchvision that matches the PyTorch version.
  • CUDA 10.1, 10.2
  • GCC >= 4.9

2. Build DeFRCN

  • Clone Code 
    git clone https://github.com/er-muyue/DeFRCN.git
cd DeFRCN
  • Create a virtual environment (optional) 
    virtualenv defrcn
cd /path/to/venv/defrcn
source ./bin/activate
  • Install PyTorch 1.6.0 with CUDA 10.1 
    pip3 install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
  • Install Detectron2 
    python3 -m pip install detectron2==0.3 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html
    • If you use other version of PyTorch/CUDA, check the latest version of Detectron2 in this page: Detectron2.
    • Sorry for that I don’t have enough time to test on more versions, if you run into problems with other versions, please let me know.
  • Install other requirements. 
    python3 -m pip install -r requirements.txt

3. Prepare Data and Weights

  • Data Preparation

    • We evaluate our models on two datasets for both FSOD and G-FSOD settings:

      Dataset Size GoogleDrive BaiduYun Note
      VOC2007 0.8G download download -
      VOC2012 3.5G download download -
      vocsplit <1M download download refer from TFA
      COCO ~19G - - download from offical
      cocosplit 174M download download refer from TFA

    • Unzip the downloaded data-source to datasets and put it into your project directory:

        ...
  datasets
    | -- coco (trainval2014/*.jpg, val2014/*.jpg, annotations/*.json)
    | -- cocosplit
    | -- VOC2007
    | -- VOC2012
    | -- vocsplit
  defrcn
  tools
  ...
  • Weights Preparation
    • We use the imagenet pretrain weights to initialize our model. Download the same models from here: GoogleDrive BaiduYun
    • The extract code for all BaiduYun link is 0000

4. Training and Evaluation

For ease of training and evaluation over multiple runs, we integrate the whole pipeline of few-shot object detection into one script run_*.sh, including base pre-training and novel-finetuning (both FSOD and G-FSOD).

  • To reproduce the results on VOC, EXP_NAME can be any string (e.g defrcn, or something) and SPLIT_ID must be 1 or 2 or 3 (we consider 3 random splits like other papers). 
    bash run_voc.sh EXP_NAME SPLIT_ID (1, 2 or 3)
  • To reproduce the results on COCO, EXP_NAME can be any string (e.g defrcn, or something) 
    bash run_coco.sh EXP_NAME
  • Please read the details of few-shot object detection pipeline in run_*.sh, you need change IMAGENET_PRETRAIN* to your path.

Results on COCO Benchmark

  • Few-shot Object Detection

    Method mAPnovel
    Shot 1 2 3 5 10 30
    FRCN-ft 1.0* 1.8* 2.8* 4.0* 6.5 11.1
    FSRW - - - - 5.6 9.1
    MetaDet - - - - 7.1 11.3
    MetaR-CNN - - - - 8.7 12.4
    TFA 4.4* 5.4* 6.0* 7.7* 10.0 13.7
    MPSR 5.1* 6.7* 7.4* 8.7* 9.8 14.1
    FSDetView 4.5 6.6 7.2 10.7 12.5 14.7
    DeFRCN (Our Paper) 9.3 12.9 14.8 16.1 18.5 22.6
    DeFRCN (This Repo) 9.7 13.1 14.5 15.6 18.4 22.6

  • Generalized Few-shot Object Detection

    Method mAPnovel
    Shot 1 2 3 5 10 30
    FRCN-ft 1.7 3.1 3.7 4.6 5.5 7.4
    TFA 1.9 3.9 5.1 7 9.1 12.1
    FSDetView 3.2 4.9 6.7 8.1 10.7 15.9
    DeFRCN (Our Paper) 4.8 8.5 10.7 13.6 16.8 21.2
    DeFRCN (This Repo) 4.8 8.5 10.7 13.5 16.7 21.0
  • * indicates that the results are reproduced by us with their source code.
  • It's normal to observe -0.3~+0.3AP noise between your results and this repo.
  • The results of mAPbase and mAPall for G-FSOD are list here GoogleDrive, BaiduYun.
  • If you have any problem of above results in this repo, you can download configs and train logs from GoogleDrive, BaiduYun.

Results on VOC Benchmark

  • Few-shot Object Detection

    Method Split-1 Split-2 Split-3
    Shot 1 2 3 5 10 1 2 3 5 10 1 2 3 5 10
    YOLO-ft 6.6 10.7 12.5 24.8 38.6 12.5 4.2 11.6 16.1 33.9 13.0 15.9 15.0 32.2 38.4
    FRCN-ft 13.8 19.6 32.8 41.5 45.6 7.9 15.3 26.2 31.6 39.1 9.8 11.3 19.1 35.0 45.1
    FSRW 14.8 15.5 26.7 33.9 47.2 15.7 15.2 22.7 30.1 40.5 21.3 25.6 28.4 42.8 45.9
    MetaDet 18.9 20.6 30.2 36.8 49.6 21.8 23.1 27.8 31.7 43.0 20.6 23.9 29.4 43.9 44.1
    MetaR-CNN 19.9 25.5 35.0 45.7 51.5 10.4 19.4 29.6 34.8 45.4 14.3 18.2 27.5 41.2 48.1
    TFA 39.8 36.1 44.7 55.7 56.0 23.5 26.9 34.1 35.1 39.1 30.8 34.8 42.8 49.5 49.8
    MPSR 41.7 - 51.4 55.2 61.8 24.4 - 39.2 39.9 47.8 35.6 - 42.3 48.0 49.7
    DeFRCN (Our Paper) 53.6 57.5 61.5 64.1 60.8 30.1 38.1 47.0 53.3 47.9 48.4 50.9 52.3 54.9 57.4
    DeFRCN (This Repo) 55.1 57.4 61.1 64.6 61.5 32.1 40.5 47.9 52.9 47.5 48.9 51.9 52.3 55.7 59.0

  • Generalized Few-shot Object Detection

    Method Split-1 Split-2 Split-3
    Shot 1 2 3 5 10 1 2 3 5 10 1 2 3 5 10
    FRCN-ft 9.9 15.6 21.6 28.0 52.0 9.4 13.8 17.4 21.9 39.7 8.1 13.9 19 23.9 44.6
    FSRW 14.2 23.6 29.8 36.5 35.6 12.3 19.6 25.1 31.4 29.8 12.5 21.3 26.8 33.8 31.0
    TFA 25.3 36.4 42.1 47.9 52.8 18.3 27.5 30.9 34.1 39.5 17.9 27.2 34.3 40.8 45.6
    FSDetView 24.2 35.3 42.2 49.1 57.4 21.6 24.6 31.9 37.0 45.7 21.2 30.0 37.2 43.8 49.6
    DeFRCN (Our Paper) 40.2 53.6 58.2 63.6 66.5 29.5 39.7 43.4 48.1 52.8 35.0 38.3 52.9 57.7 60.8
    DeFRCN (This Repo) 43.8 57.5 61.4 65.3 67.0 31.5 40.9 45.6 50.1 52.9 38.2 50.9 54.1 59.2 61.9

  • Note that we change the λGDL-RCNN for VOC to 0.001 (0.01 in paper) and get better performance, check the configs for more details.

  • The results of mAPbase and mAPall for G-FSOD are list here GoogleDrive, BaiduYun.

  • If you have any problem of above results in this repo, you can download configs and logs from GoogleDrive, BaiduYun.

Acknowledgement

This repo is developed based on TFA and Detectron2. Please check them for more details and features.

Citing

If you use this work in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:

@inproceedings{qiao2021defrcn,
  title={DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection},
  author={Qiao, Limeng and Zhao, Yuxuan and Li, Zhiyuan and Qiu, Xi and Wu, Jianan and Zhang, Chi},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={8681--8690},
  year={2021}
}